text:
Tax cuts are changes that reduce the amount paid under the law to government revenue. The taxes cut are on income, profits, sales, or assets. Tax cuts occur in many different forms. They are a one-time rebate, a reduction in the overall rate, or a tax credit. Most comprehensive tax reform plans include them. For examples, see Fair Tax and Flat Tax.

Tax cuts also refer to tax deductions, loopholes, or credits. The recipient benefits from a change that reduces taxes. Tax cuts are always popular while tax increases are not.

Types

The types of tax cuts correspond to the different types of taxes.

Income tax cuts reduce the amount paid by individuals and families on wages earned. This increases consumer spending, which drives nearly 70% of the economy. To understand why, see Components of GDP.

Capital gains tax cuts reduce taxes on sales of assets. That gives more money to investors. They put more money into companies, through stock purchases, helping them grow. It also drives up the prices of housing and other real estate, oil, gold, and other assets.

Inheritance or estate tax cuts reduce the amount paid by heirs on their parents' assets.

Business tax cuts reduce taxes on profit. These give more money to companies to invest and hire workers.

Small business tax cuts help entrepreneurs employing 50 or fewer workers. That's a great way to add jobs since small businesses create 65% of all new jobs.

Corporate tax cuts lower corporate income taxes. That gives them more money to invest back into their businesses. That boosts spending on durable goods orders, including capital goods. It also creates jobs.

Payroll tax cuts lower the payments made to Social Security, Medicare, and Unemployment taxes. Businesses and employees share this cost, so a payroll tax cut helps both.

Tax Cuts by President

Barack Obama proposed the $787 billion American Recovery and Reinvestment Act (ARRA), passed by Congress in March 2009. It had $288 billion in tax cuts. It reduced that year's income taxes for individuals by $400 each and $800 for families. Employers lowered the amount withheld so workers could spend the money right away. Because it wasn't publicized very well, many people didn't even notice the increased amount of their paychecks.

ARRA also reduced income taxes by the amount equal to the sales tax on new car purchases. It provided $17 billion in tax cuts for households who invested in renewable energy sources. It included $54 billion in small business tax cuts Here are the ARRA details.

In 2010, Congress approved the $858 billion Obama tax cut plan. It cut payroll taxes by 2%, adding $120 billion to consumer spending. It extended the college tuition tax credit. It continued the unemployment benefit extension through 2011. It reinstated the 35% inheritance tax on estates worth $5 million ($10 million families) to pay for the cuts. It cut $55 billion in taxes for specific industries.

In 2013, Congress extended the Obama tax cuts were kept on incomes below $400,000 (or $450,000 for married couples). There was no expiration date. For more, see Fiscal Cliff 2013.

George Bush used tax cuts to fight the 2001 recession. There were three Bush tax cuts. The first, known as EGTRRA, was passed in 2001. It reduced tax rates by three percentage points and created a new 10% rate at the lowest incomes. It cut the marriage, estate, and gift tax. It expanded the Child Tax Credit and Earned Income Tax Credit.

In 2003, Congress passed JGTRRA to speed up the EGTRRA tax cuts and help investors. It reduced the maximum tax rate on long-term capital gains and dividends to 15%.

In 2008, the Bush tax rebate was mailed to taxpayers. Although costly to execute, it worked because people knew they were getting a break. They felt they could spend it. However, the economy had already slid into a recession, so it didn't stimulate the economy enough to reverse course.

Bill Clinton raised the income tax rate early in his first term. But in 1997, Clinton reduced the capital gains tax rate to 20% from 28%. He raised the exemption on the inheritance tax to $1 million from $600,000. He created Roth IRAs that allowed capitals gains to grow and be withdrawn tax-free. He raised the limits for deductible IRAs. (Source: The Dangerous Myths About the Bill Clinton Tax Increase, Forbes, July 16, 2012)

Ronald Reagan cut the income tax rate from 70% to 28% for the top levels, and similarly for all other levels of income. He cut the corporate tax rate from 48% to 34%. He was such an advocate of supply-side economics that it's sometimes known as Reaganomics. It worked to stop the 1981 recession because taxes were in the "Prohibitive Range," according to the Laffer Curve. This theory also states that tax revenue from a stronger economy replaces any funds initially lost.

Richard Nixon did not cut taxes. Instead, he added 10% import tax.

Lyndon Johnson pushed through JFK's tax cuts. The Senate approved on February 7, 1964. It lowered the top income tax rate to 70% from 91% over two years. It lowered the bottom rate to 14% from 20%. It lowered the corporate rate to 48% from 52%.

John F. Kennedy advocated a cut in income taxes. He wanted to lower the top rate from 91% to 65%. (Sources: JFK Presidential Library and Museum, Address to the Economic Club of New York, December 14, 1962. U.S. News, The Myth of JFK as a Supply-Side Tax Cutter, January 26, 2011)

How Tax Cuts Stimulate the Economy

How tax cuts affect the economy depends on the type of tax being cut. Tax cuts boost the economy by putting more money into circulation. They also increase the deficit if they aren't offset by spending cuts. As a result, tax cuts improve the economy in the short-term but depress the economy in the long-term if they lead to increased federal debt.

Once tax cuts are put in place, they are difficult to revoke. Why? A tax cut reversal feels like, and has the same impact, as a tax increase. That's what happened when the Bush tax cuts were due to expire in 2010.


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|Publication number
|Publication type
|Application number
|Publication date
|Jan 22, 2008
|Filing date
|Jul 5, 2005
|Priority date
|Jul 5, 2005
|Fee status
|Also published as
|Publication number
|Aleks Göllü
|Original Assignee
|Pinc Solutions
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (65), Referenced by (36), Classifications (14), Legal Events (4)
|External Links: USPTO, USPTO Assignment, Espacenet
The present application is related to U.S. patent application Ser. No. 11/054,905, entitled "Position-Tracking Device for Position Tracking System," and U.S. patent application Ser. No. 11/054,881, entitled "Position-Tracking System," both of which were filed on Feb. 10, 2005.
This application discloses an invention that is related, generally and in various embodiments, to systems and methods for determining a location of an object. The disclosed invention may be utilized to track the respective locations of any number of objects.
Position-tracking systems seek to identify the location of mobile objects in real-time and are used in a wide variety of applications, including transportation, logistics management, healthcare, security, etc. Position-tracking systems that can provide continuous location information are desirable for applications that require non-interrupted visibility of the mobile object through a journey. For objects that are moved with a vehicle (such as inter-modal shipping containers or trailers), being able to locate the position of the objects in real-time can help improve the efficiency of supply-chain and inventory management while reducing the loss in productivity of assets. As businesses increasingly depend on a global supply chain and distribution networks, more goods and assets are being moved around for longer distances and with more parties involved in the process. In order to facilitate an automated, error-free data exchange for logistics management, the availability of continuous information about these assets in real-time is more valuable than "discrete" information that is only available for certain regions, albeit with perhaps higher position resolution.
Existing position-tracking systems typically employ satellite, radio wave or other wireless technologies. However, such systems typically suffer from limitations caused by line-of-sight requirements for location signals and/or interference caused by the environment in which they operate (e.g., multi-path fading or radio waves in an indoor setting). In addition, although much effort has gone into extending the operating range for Global Positioning System (GPS) and GPS-like technologies with more and more sophisticated software implementations, GPS systems alone are still generally ineffective and unreliable for location tracking indoors or in other environments where obstructions to the satellite signals present challenges. Further, other non-GPS based position technologies that have been proposed to date are largely based on a "local GPS" system using an assembly of locally deployed wireless antennas to mimic the function of the GPS satellites. Due to the high cost of designing and implementing such systems, however, they remain a costly proposition
In one general respect, this application discloses a system for determining a location of an object. According to various embodiments, the system includes an object location tracker and a computer system. The object location tracker is configured for attachment to a mobile vehicle and includes an object identification reading device and a position-tracking device. The object identification reading device senses object identification indicia on the object, such as RF ID tags, bar codes, etc., as the mobile vehicle moves around an environment in which the object is situated. The position-tracking device computes the location of the object location tracker as the mobile vehicle moves throughout environment. The computer system associates the sensed object identification indicia of the object, as determined by the reading device, with a location in the environment based on the position of the object location tracker in the environment, as determined by the position-tracking device, when the reading device senses the object identification indicia. The mobile vehicle may include its own mobility system, such as a forklift or an autonomous robotic device, or the mobile vehicle may be, for example, a pushcart that is pushed around the environment. Also, the mobile vehicle may be a person walking around.
The position-tracking device may provide variable-resolution position information for the mobile vehicle based on the environment in which the vehicle is moving. For example, while in a "wide resolution" area where very high position resolution is not required, the system may compute a general position for the vehicle based on a wireless telephone network Cell-ID/map correlation architecture. That is, the system may utilize Cell-ID information obtained from a wireless telephone network in conjunction with a database of area maps to determine a general position of the vehicle (e.g., within the geographic area covered by a certain Cell-ID). In this case, the position tracking system is said to operate in the "wide area" mode.
When the vehicle enters an area (such as a facility, warehouse, campus, etc.) where greater position resolution is required (a so-called "high resolution area"), the position tracking system may transition to a corresponding high-resolution mode. The mode transition may be activated, for example, by a wireless activation signal provided at the ingress of the high-resolution area. The greater position resolution may be realized using other wireless signals, from a wireless aiding system within the high-resolution environment, combined with the inputs from the inertial sensors. The wireless aiding system can consist of a number of objects with known fixed positions with identification indicia that are read. The position-tracking device may compute the amount of displacement in the longitudinal, latitudinal and attitudinal directions based on the measurements from the inertial sensors. When aided by the wireless reference position signals from the wireless aiding system, the combined implementation can provide enhanced accuracy (e.g., on the order of meters) without ever losing coverage of the vehicle, regardless of the layout of the high-resolution environment. When the mobile vehicle exits the high-resolution area, the wireless activation signal at the point of egress may deactivate the high-resolution mode and the system may automatically revert back to the wide area mode.
For vehicle tracking in the high-resolution mode, the system may exploit distinct patterns of motion that can be identified as motion "signatures" that are characteristic of certain types of motion. The position tracking system may estimate the state of the vehicle (e.g., the heading, position, velocity of the vehicle) based on kinematic models for the current mode of the vehicle. Adaptive and cascaded Kalman filtering or similar techniques, for example, may be employed in the analysis to more accurately estimate the position and velocity of the vehicle based on the motion pattern identified.
The state history of the vehicle may be stored and used in the state estimate computation to make continuous adjustments to the state estimates in the past. For instance, the inertial sensor measurements and a priori estimations for a configurable time period at a configurable frequency may be stored in a data storage device. As new information becomes available, the a priori estimations may be adjusted to provide more accurate final estimations (with a time delay). For example, when the tracking system determines that the vehicle has become "stationary" based on an identified match to a motion signature, any residual estimated velocity may be corrected (or, smoothed) backwards in time to provide an improved velocity and, hence, position estimations from the original calculation. Alternatively, one can store state history between the sighting of two objects with fixed known positions and then calculate a trajectory that passes through both of them.
The computer system may maintain a centralized, real-time record of the locations of the objects identified by the reading device. The record can be stored in a database and/or it may be a direct input to a logistic or other IT management system so that the location information can be further processed or utilized by other applications
These and other aspects of the present invention will be apparent from the description to follow.
Various embodiments of the disclosed invention will be described herein by way of example in conjunction with the following figures, wherein:
The tracking system 10 tracks the position of the objects as they travel through areas, such as area 12, where very high position location resolution is not needed, and areas, such as area 14, where high position resolution is needed. "Wide resolution" areas may include areas where the objects are in transit between locations such as, for example, intermodal transportation hubs, shipyards, rail yard, etc. In such areas, position resolution on the order of hundreds or thousands of meters may be acceptable. On the other hand, "high resolution" areas, whether indoor or outdoor environments, are areas that require more precise location information for logistical purposes, such as on the order of a few meters or less. Examples of high-resolution areas include manufacturing facilities, campuses, warehousing facilities, etc.
The position-tracking system 10 may include, according to various embodiments, one or more tracking devices 16 and a host computer system 18 that communicates with the tracking devices 16. The tracking devices 16 may be attached or otherwise connected to the objects to be tracked, for example. In
The tracking devices 16 preferably include processing capabilities that allow them to estimate their real-time position based on, for example, inertial sensor inputs and wireless signals. The tracking devices 16 may operate in one of two modes—a wide area mode and a high-resolution mode—and compute their estimated position differently depending on its mode of operation. For example, when a tracking device 16 is in the high-resolution area 14 (such as connected to goods on the forklift 23 1 or connected to the forklift 23 2 itself), the tracking device 16 may correspondingly be in its high-resolution mode and compute its estimated position with an accuracy on the order of a few meters or less based on the input from inertial sensors and wireless signals from a wireless aiding system. The wireless aiding system may include, as shown in
The tracking device 16 may transmit its estimated position to the host computer system 18. This may be done continuously, periodically, randomly, pseudo-randomly, and/or upon a request from the host computer system 18. The host computer system 18 may maintain a centralized, real-time record of the locations of the tracked objects. The record can be stored in a database 24 and/or it may be a direct input to a logistic or other IT management system so that the location information can be further processed or utilized by other applications.
The host computer 18 may be implemented as one or a number of networked computers, such as servers, PCs, workstations, etc. In various embodiments, as described above, the tracking device 16 may compute its estimated position and transmit the position to the host 18, although according to other embodiments, the position determination processing may be distributed between the processing capabilities of the tracking device 16 and the host 18. Also, although only three tracking devices 16 is shown in
The wireless aiding system used in the high-resolution area 14 may include a number of reference point transmitters 28 positioned throughout the high-resolution area 14. Each reference point transmitter 28 may wirelessly transmit a high accurately reference location position to the tracking devices 16, such as with RF, acoustic, optical, IR or other suitable signals, such that the tracking devices 16 can compute their position based on the reference as well as with input from inertial sensors, as described in more detail below. The high-resolution areas 14 may also include a number of radio access points 30. The radio access points 30 may provide a wireless gateway (e.g., via the IEEE 802.11 or IEEE 802.15.4 protocols) for communications between the position tracking devices 16 and the host computer system 18. The radio access points 30 may be in communication with the host 18 via a data transfer network 20 such as, for example, a LAN, a corporate intranet, a WAN, a MAN, a TCP/IP network, a broadband computer network, a wireless communication network, or a combination of one or more of these data transfer networks.
The reference point transmitters 28 and the radio access points 30 may be strategically placed throughout the high-resolution area 14 so as to avoid interference caused by obstructions in the environment and/or co-channel interference, yet reduce the number of each that is needed to provide adequate position resolution and communications with the host 18. For example, in various applications it may be advantageous to position the reference point transmitters 28 points along or near common travel paths for the objects in the environment.
In addition to the processor 42 and the inertial sensor assembly 40, the tracking device 16 may include a reference position receiver 44, a number of RF transceivers 46A-C, and a wireless telephone network transceiver 48. The reference position receiver 44 may receive signals from the reference point transmitters 28 and forward the received reference position information to the processor 42 to aid in the position determination process. The RF transceiver 46A may communicate with the radio access points 30 and/or with other tracking devices 16. As such, the RF transceiver 46A may report object location information (as computed by the processor 42) back to the host 18 via the radio access points 30, as well as receive control and monitoring signals from the host 18 and send responses thereto.
The wireless telephone network transceiver 48 may communicate with a wireless telephone network that includes base stations 50 (see
The tracking device 16 may also include a memory device 52 and a power source, such as battery 54. The memory device 52 may be in communication with the processor 42, and store instructions and data for the processor 42. The memory device 52 may be implemented, for example, as one or more RAM units or flash memory devices, or any other suitable memory device(s). The battery 54 supplies power to the various power-consuming components of the tracking device 16.
As shown in
A wireless signal transmitted from points of egress/ingress between the wide resolution areas 12 and the high-resolution areas 14, such as from a transmitter 66 at gate 68 in
In the wide area mode, the tracking device 16 may determine its general position, according to various embodiments, based on cell-ID information received from the wireless telephone network base stations 50. That is, the tracking device 16 may be in communication with one of the base stations 50 of the wireless telephone network according to conventional call hand-off technology employed by the wireless telephone network and obtain the Cell-ID from the base station 50 in the hand-shake/registration communications with the base station 50. Each base station 50 may serve a certain geographic area (a "cell site"). The wide area resolution module 62 of the tracking device 16 may correlate the received Cell-ID information to map information stored in the memory device 52 to determine the general location of the tracking device 16 (e.g., within the geographic area covered by the base station 50). Thus, as the object travels between cell sites as it travels throughout the wide resolution area 12, the wide area resolution module 62 may update its position as it enters each new cell site based on the new Cell-ID information that it receives. The geographic area of a cell in a wireless telephone network typically ranges from hundreds to a few thousand meters. In such a scheme, the positioning resolution varies as the object moves from areas with higher base station densities to areas with lower base station densities.
Further, if there is pre-planned route information for the object and that information is available to the tracking system, that information can be used to further refine the location estimation. For example, if it is known that the object is to be transported on a truck on particular highway in a particular region, when the wide area resolution module 62 determines the object is in the region based on the received cell-ID, it may further refine its location estimation to be on or near the highway in the geographic region covered by the relevant base station 50. The pre-planned route information may be stored in the memory device 52 of the tracking device 16, or it may be stored in a memory unit associated with the host 18 (e.g., the database 24). In the later case, the host 18 may compute the refined location estimation based on the pre-planned route information.
According to yet other embodiments, the wide area resolution module 62 may reside with the host 18. In such an embodiment, the cell-site map information may be stored in the database 24 and the tracking device 16 may report back to the host 18 the Cell-ID information. The host 18 may then perform the correlation between the Cell-ID information and the cell-site map information to compute the general location of the tracking device 16.
According to other embodiments, instead of or in addition to using the Cell-ID information to compute the location of the object in the wide resolution areas 12, the tracking device 16 may include a GPS receiver (not shown) to aid in the location determination process. For an embodiment where both the Cell-ID information and the GPS information are used to track the object, the wide area resolution module 62 may use both inputs to verify the location of the object.
The high-resolution module 60 of the tracking device 16 may compute the location and orientation of the object based on the inertial sensor inputs and the inputs from the wireless aiding system. The inertial sensors may provide outputs that measure motions of the object, such as linear accelerations and rates of rotation. In the description to follow, the vector d is used to identify the measured x, y and z accelerations, and the vector • is used identify the x, y, and z rotation rates as measured by the inertial sensor assembly 40. The measurements of the sensors may be assumed to contain noises that can be represented by stochastic models such as:
(Measured Data)=(Sensor Bias)+(Scale Factor)×(Actual Data)+(Drift Rate)×(Time)
where "Sensor Bias" and "Drift Rate" are random variables with possible time-varying characteristics.
Referring to
When the current mode detector 82 determines that there is a mode transition at time t=k, it stores the time point of the transition tm i in a buffer S3 at block 94. The current mode detector 82 may also reference the buffer S3 to recall when the last mode transition occurred.
At step 86, a current mode state estimator algorithm computes the current state estimate of the object (i.e., state estimate at t=k−p) and the state estimate is written into the buffer S2 at block 84. As mentioned above, the "state" estimate of the object may include values for the parameters {circumflex over (p)}, {circumflex over (b)}, {right arrow over (I)}, {right arrow over (R)}, m, {circumflex over (ν)}, and {right arrow over (E)}, or a subset of these parameters. As described in more detail below in conjunction with
Available correction inputs 88 at time t=k, such as external correction inputs available from, for example, the reference point transmitters 28, may be stored in the buffer S4 at block 90. Notationally, the correction inputs may be represented by the correction information matrix {right arrow over (C)}, which may include several vectors, including {circumflex over (p)}, {circumflex over (ν)} and ű (where ű is a vector of the yaw, pitch and roll Euler angles of the attitude {right arrow over (R)}). Also, as mentioned about, mode transition correction information from the current state estimator algorithm 86 may be stored in the buffer S4.
At step 92, a state estimate corrector algorithm computes a corrected state estimate of the object at time t=k−p−q based on the historical state information stored in buffer S2, inputs regarding when the last mode transition occurred (time tg i) from the buffer S3, and the correction information stored in buffer S4, where q is an additional delay representing the amount of time that passes between the arrival of correction information. The correction algorithm 92 may check to see whether any corrections have arrived in the buffer S4 and, if so, apply those corrections to the state estimates in the buffer S2 to generate corrected state estimates. For example, assume that at time tg i correction information has arrived either (a) from the correction inputs 88 or (b) as a result of a mode transition detected by the current mode detector 82 and its resulting correction computed by the current mode state estimator 86 or (c) both (a) and (b). The corrector algorithm 92 may then compute and apply the corrections as it computes to the state estimates in the buffer S2 for the time period tg i-1 to tg i. The state estimation at time t=k−p−q, where q is an additional delay to account for the corrected information, may then be supplied as the output of the high resolution state estimation algorithm. In this approach, tg i−tg i-1<g, such that the corrector algorithm 92 can work on all past states. Thereafter, the current mode state estimator 86 may use the new corrected state estimate as it computes the ongoing states estimates for the object.
A vector ĝ of the correction history, e.g., a list of the last i time points where a state correction was provided (e.g., ĝ=[tg 1, tg 2 . . . , tg i]), may be stored in the buffer S3 at block 94. The buffer S3 may also store a vector ĥ of the mode history, e.g., a list of the last j time points where the mode of the object was switched (e.g., ĥ=[tm 1, tm 2 . . . , tm j]). That is, when a change in the current mode is detected at step 82, the vector ĥ stored in the buffer S3 at block 94 is updated.
In certain embodiments, the buffer S2 may store data at a slower rate than the other buffers. For example, the buffer S2 may store data at a rate of 1 Hz, whereas the other buffers may store data at a rate of 96 Hz. In that case, the buffer S2 stores every 96th value. As the buffer S2 outputs state estimation information, its time stamp may indicate the necessary fractional time stamp adjustment.
The process of the detecting the current mode of the object (step 82 of
A disadvantage of using a curve fitting algorithm is that to generate one time-increment of state estimation for each of the N motion models and the straight integration, (N+1)*p calculations must be performed, which may be too computationally expensive for some applications. To compensate, according to various embodiments, the current mode estimation can be performed at a lower frequency than the actual state estimation. In addition, if the probability of a mode transition is high, the resolution can be increased to better identify the transition time.
For bodies/objects with few motion modes or very distinct acceleration patterns, statistical analysis can also or alternatively be performed on the inertial sensor data to detect the current mode. For example, if the body is a car, which is either stationary or moving, statistical analysis of acceleration in the x direction may be sufficient to detect motion. For instance, the statistical analysis algorithm may analyze the standard deviation of a moving time window of the acceleration values. Changes in the standard deviation may be used to indicate mode transitions. The amount of time spent in the mode may then be used to ensure that the mode change detection was valid before a final decision is made that a mode transition has occurred.
Also, according to various embodiments, right after a transition it may be assumed that there are no other transitions for a time period, such as a fraction of p. During this time period, it may not be necessary to perform additional mode transition detection.
A current mode motion model 112 has a model for the allowed motion patterns for the object given the current motion mode, as determined by the current mode detector 82. For example, if the object is traveling in a car or other similar vehicle type in terms of allowed motions, the current mode motion model 112 may assume that the object cannot move in the body y direction. On the other hand, for example, if the object is being lifted by a forklift, the current mode motion model 112 may assume that the motion has to be in the z direction, and no motion may be expected in the x-y plane. Based on the allowed motion patterns for the given mode, the current mode motion model 112 computes a state estimate for the object at time t=k−p that is consistent with the motion model. A number of such motion models may be used for different motion types, with different assumptions for different modes as applicable. Because the actual state of the object is probably somewhere between the state estimates computed by the straight integration 110 and the current mode motion model 112, a Kalman filter 114, or some other suitable filtering technique, may take the two different state estimates and corresponding confidence factors (based on, e.g., known sensor noise and known model shortcomings) to compute a state estimate that is a weighted sum of the two state estimates. The Kalman filter 114 may be a standard Kalman filter or an adaptive one. According to various embodiments, a number of adaptive, cascaded Kalman filters may be used. The parameters of the filter(s) may be derived from noise characteristics of the inertial sensors, the accuracy of the current mode motion model 112 and other tuning.
The state estimate corrector algorithm 92 may correct the state estimates stored in the buffer S2 based on the corrective information stored in the buffer S4. The corrector algorithm 92 may differentiate between different types of corrective data in making corrections. For example, in various embodiments, the corrector algorithm 92 may differentiate between frequent and infrequent correction types. For infrequent corrections, such as when a position update is provided (such as from one of the reference position transmitters 28), the appropriate heading and/or velocity corrections that would shift the estimated position of the object to the updated position are computed. In making this determination, the initial heading information may be assumed to be incorrect and that the velocity error accumulated linearly. Once the heading and velocity corrections (and sometimes the bias corrections) are calculated, the results may be applied to correct the entire state trajectory in the buffer S2. In various embodiments, the corrector algorithm 92 may use a closed form solution to determine the heading and velocity corrections based on the estimated object location (without corrective inputs) and the updated object location.
The following describes one embodiment of how the state estimate corrector algorithm 92 may operate. Assume that a tracked object traverses a trajectory from an initial position {circumflex over (p)}0 to a final position {circumflex over (p)}n over a period of T seconds. Let v0 define the initial speed and let ĥ0 define the initial speed heading in a navigation frame, i.e., a vector with unit magnitude, where v0*ĥ0 would be the velocity in the navigation frame. Let the time interval T be divided into n sub increments, each of length dt (i.e., T=n*dt). Let {circumflex over (p)}i, vi and ĥi denote the position, speed, and speed heading, respectively, at time i*dt, and let {circumflex over (p)}n, vn and ĥn be the final position, speed, and speed heading. Further, assume that the final position is calculated as:
For simplicity, let {circumflex over (p)}n=0 in the following analysis.
Let {circumflex over (f)}i be a unit heading vector in the navigation frame at time i. Let {right arrow over (R)}i be the rotation matrix that maps from the inertial frame to the navigation frame. Assume that at time n, some correction information is given and its impact on the current state is to be calculated. Three types of error may be considered: (1) velocity estimation errors, (2) speed heading errors, and (3) accumulating velocity error.
For velocity estimation errors, the following types of velocity estimation errors should be accommodated while performing dead reckoning from the initial to the final position of an object:
For cars or other similar vehicles, {circumflex over (f)}i is typically equal to ĥi since the speed heading is typically in the body-x direction and there is minimal body-y or body-z speed to correct. As such each {circumflex over (f)}i is known and given at time i*dt. If one wants to accommodate error accumulation in all axes in the body frame, representing the three sensor biases, then {circumflex over (f)}i can be rewritten as {circumflex over (f)}i={right arrow over (R)}i*{circumflex over (f)}, where {circumflex over (f)} is a constant heading in the body frame, representing the error accumulation ratio in the body frame, and {right arrow over (R)}i is a rotation matrix, rotating the body frame to the navigation frame. Therefore, the position that accounts for the errors may be given as:
Assume at time n a correction is obtained. The above equation can be used if the provided correction information is the correct position {circumflex over (p)}c, in which case the correct values for dv and {circumflex over (f)} can be selected to make sure that the trajectory is shifted from {circumflex over (p)}n to {circumflex over (p)}c, and the updated velocity may be computed at time n.
Alternatively the correction information may be a new velocity (i.e. dv and {circumflex over (f)}) and the new position may be calculated. Let
The heading ĥa and the rotation matrix {right arrow over (H)}b can be updated at each time increment and remembered as part of the system state. As such when at time n, a correction arrives, the correction can be applied without having to recalculate the state trajectory for t=[0,n]. Therefore, at any time n, one can either solve for {circumflex over (f)} and dv, or apply their impact.
Where the heading ĥi values are wrong, they may be corrected by rotating them by an amount described by the rotation matrix {right arrow over (EH)}i. If {right arrow over (EH)}i={right arrow over (EH)}0, the error model may assume the initial heading was wrong. If it is desired to model some heading error accrual, it may be represented by a series of varying {right arrow over (EH)}i. The new point may be given as:
Here, the vectors {circumflex over (f)}i have been rotated as well, since the heading error typically represents the misalignment between body frame and navigation frame. But this term can be dropped depending on the needs of the model.
Because, for the general {right arrow over (EH)}i, or even for small angle {right arrow over (EH)}i, there is no immediate simplification that enables the heading error accumulation to be modeled and represented, it may be assumed that {right arrow over (EH)}i are all equal to {right arrow over (EH)}, which is equivalent to saying the initial heading was wrong. In this case the problem is simplified to:
This equation can then be used at time n to accommodate the correction information obtained.
The last equation above applies to the case for a vehicle where the initial heading assumption was wrong and velocity error is accumulating. In this case, if a position update is provided and given a new point {circumflex over (p)}c, this correction can be accommodated by selecting an {right arrow over (EH)} to rotate {circumflex over (p)}n to a point {circumflex over (p)}r such that a translation from {circumflex over (p)}r in the direction {right arrow over (EH)}*ĥa leads to {circumflex over (p)}c. The amount of translation to {circumflex over (p)}c is adjusted by proper selection of the dv value. Alternatively, one could first translate {circumflex over (p)}n to a point {circumflex over (p)}t along ĥa such that {circumflex over (p)}t can then be rotated to {circumflex over (p)}c by picking {right arrow over (EH)}. If given a velocity correction, the other unknowns can be solved for.
The host processing system 18 may have knowledge (stored in the database 24, for example) about physical constraints of the environment and make additional position refinements based on that information. For example, the host processing system 18 may know the location of certain walls or other permanently located obstacles in the environment and, based thereon, refine the reported position of an object if the reported position coincides with such a wall or obstacle. Also, for certain object types, the host processing system 18 may assume that no two objects are in the same position at the same time.
According to various embodiments, the power management module 160 may receive the inputs from the inertial sensor assembly 40 and, based thereon, determine an appropriate power level for the tracking device 16 (such as, e.g., full power, reduced duty cycle, hibernation or sleep mode) that is needed for proper operation of the tracking device 16 at that particular moment. For example, according to one embodiment, for a position tracking system 10 where continuous position sensing and computation of the tracking device 16 is only needed when the object under surveillance is in motion, the power management module 160 can, for example, cause the processor 42 to enter a sleep mode if no (or minimal) movement is detected for a certain time period. Also, for example, the power management module 160 may select a reduced duty cycle mode when the tracking device 16 is in the wide area resolution mode and select a full power mode when the tracking device 16 is in the high resolution mode (except when the processor 42 enters the sleep mode due to lack of movement, for example, regardless of operational mode). Although this description of the power management module 160 is in the context of a tracking device with an on-board processor, such a power management module that is responsive to inertial sensor inputs in selecting the power level can be used for any device with an on-board processor.
In other embodiments, the position-tracking device 16 could be mounted on, attached to, or otherwise carried by a mobile vehicle and used to determine the position of objects located throughout an environment as the mobile vehicle moves throughout the environment.
The object identification reading device 180 is for sensing object identification indicia 173 on the object 172. The object identification indicia 173 may be embodied, for example, in the form of a radio frequency identification (RF ID) tag on the object 172, a bar code on the object 172, a character such as a letter, a numeral, a symbol, etc. on the object 172, or any combination thereof on the object 172. Correspondingly, the object identification reading device 180 may include, for example, a radio frequency identification reader for sensing a RF ID tag on the object 172, a bar code scanner for reading a bar code on the object 172, a camera with optical character recognition (OCR) for recognizing characters on the object 172, etc. or any combination thereof.
Although only one reading device 180 is shown attached to the vehicle 178 in
In addition, where the environment includes a number of objects with fixed positions, the identification indicia read from those fixed-position objects by the reading device(s) 180 can be used to aid in the location determination process much like a reference position signal received from one of the reference position signal transmitters 28. In other words, if the reading device 180 reads an object with fixed known location, the position tracking device 16 can use that information to aid in the location determination process.
The position-tracking device 16 may be as described above. For example, with reference to
The position tracking device 16 may interpolate between reference points in determining its position. As described above, a time trajectory between the two reference positions may be computed by the high resolution module 60 (see
The computer system 176 is in communication with the object location tracker 174 and may be similar to the host computer 18 described hereinabove. The sensed object identification indicia and the updated location information of the object location tracker 174 are transmitted wirelessly to the computer system 176. The facility does not need to provide ubiquitous wireless communication coverage. Wireless coverage in a small area may be enough so long as the vehicle 178 passes through this coverage area at satisfactory intervals.
The computer system 176 may include a correlation module 184 for associating the sensed object identification indicia 173 of the object 172, as determined by the reading device 180, with a location in the environment based on the position of the object location tracker 174 in the environment, as determined by the position-tracking device 16, when the object identification reading device 180 senses the object identification indicia 173. In addition, as the vehicle 178 moves throughout an environment and/or as the correlation module 180 receives object location data from other vehicles 178, the correlation module 180 may update and filter the received object location information to obtain near real-time object location information for the objects in the environment. Further, since the computer system 176 can receive information from multiple tracking devices 174 each moving in the environment, the correlation module 180 can use the redundant location information from the multiple trackers 174 to refine the location of the objects. The object location information may be stored in the database 54 and/or used by other logistics systems, as described above.
The computer system 176 may also include an imaging module 186 for generating a representation of the environment and the locations of each tracked object 172 in the environment. The representation may be, for example, printed, or displayed on a monitor to show the location of each tracked object 172 in an environment.
The correlation module 184 and the imaging module 184 may be implemented as software code to be executed by the computer system 176 using any suitable computer instruction type. The software code may be stored as a series of instructions or commands on a computer readable medium.
A system 170 as described above may be used, for example, to track the location of and identify inventory in the environment. For example, the object-tracking device 170 could be attached to a book return pushcart for use in a library to track the location of books in the library that have, for example, RF ID tags or bar codes that can be sensed by the reading device 180. Similarly, the object-tracking device 170 could be attached to shopping cart for use in a retail store (such as a grocery store) to track the location of inventory in the store. Also, the object-tracking device 170 could be attached to a forklift or other type of industrial vehicle for use in a warehouse or transportation yard to track the location of inventory or other goods. In yet other application, the object-tracking device 170 could be attached to vehicle that roams around a parking lot to track the location of cars or other vehicles in the parking lot that have, for example, RF ID tags or bar codes that can be sensed by the reading device 180.
While several embodiments of the invention have been described, it should be apparent, however, that various modifications, alterations and adaptations to those embodiments may occur to persons skilled in the art with the attainment of some or all of the advantages of the invention. It is therefore intended to cover all such modifications, alterations and adaptations without departing from the scope and spirit of the present invention as defined by the appended claims.
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|U.S. Classification
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|International Classification
|Cooperative Classification
|European Classification
|Jul 5, 2005
Owner name: PINC SOLUTIONS, CALIFORNIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:GOLLU, ALEKS;REEL/FRAME:016760/0850
Effective date: 20050629
|Aug 25, 2011
|Surcharge for late payment
|Aug 25, 2011
|Fee payment
Year of fee payment: 4
|Jul 6, 2015
|Fee payment
Year of fee payment: 8

subset: commoncrawl


text:
To Count Rockets and to Daydream
Israel is demanding that Syria cut its ties with Iran, with Hezbollah, with Hamas and the rest of the groups as a precondition for negotiations. This is a dream.
Communities "bordering the Gaza Strip" is a homey expression, suggesting warmth and caring for the towns in the western Negev that continue to suffer Qassam barrages and sniper fire. It deserves to be dropped in favor of the more direct concept of "communities on the southern front" to describe their position accurately.
It is a front. There is a war taking place there, one that has been going on for far too long, and Israel is not winning.
Israel is not winning because it is trying out military-diplomatic alchemy that doesn't work. Israel still insists on viewing Hamas as a kind of terrorist cell that can be brought down by assassinations or tank shelling, and at most as a "terrorist infrastructure" with which no negotiations should be conducted. As if Israel hasn't noticed that this organization is carrying out political and diplomatic maneuvers that influence not only the communities "bordering the Gaza Strip," but also the Palestinian Authority; is causing all sorts of headaches for Egypt and caused a near crisis in the relations between Cairo and Jerusalem; and is linking Iran to the territories and is developing its own diplomatic reality.
That same Israel heaved a sigh of relief last week when it learned that the Qassam rockets landing on the communities "bordering the Gaza Strip" were not those of Hamas but of Islamic Jihad or the Popular Front for the Liberation of Palestine. We are now told that Hamas - almost as a sign of respect for this "responsible" organization - is still preserving that very cease-fire that Israel maintains it did not negotiate with the Islamist group.
At best, Israel's descriptions of the negotiations are strange. It may not be negotiating with the Palestinian groups, but it has a street gang-style dialogue with Hamas. "You will not attack me in the communities 'bordering the Gaza Strip,' and I will not attack you in the Gaza Strip." This message was made clear through Egypt to the Hamas leadership. But Hamas is not the only gang on this street; there is Islamic Jihad and the families of the Martyrs' Brigades. They are all armed, and none of them are bound by Hamas' orders. Thus, that same "there-is-no-negotiation" Israel will have to conduct talks with each component of the "terrorist infrastructure" if it wants to bring calm to the communities "bordering the Gaza Strip."
This is the situation that Israel will face if it chooses to copy in the Gaza Strip the cease-fire understandings from the 1996 Grapes of Wrath operation in Lebanon. But there is another way, a serious diplomatic way of the kind that is conducted between enemy states when they recognize that violent groups may strip them of their monopoly on policy making.
Syria is a necessary and possible partner for such negotiations. It is necessary because the leadership of the Palestinian factions shooting the rockets from Gaza are hosted in its territory. Syria has known how to convince the leaders of these factions to cooperate with the Palestinian Authority (see the Mecca Conference 2007) and hold their fire or curtail their opposition to the Annapolis Conference, when it believed this would serve Syrian interests. Syria is a potential partner because negotiations with Israel may restore the Golan Heights to it and loosen the American chokehold it feels and maybe even remove international pressure over Lebanon. Presumably if Syria were given the opportunity to choose between catering to the Palestinian organizations and control in Lebanon, it would opt for the second. Now Damascus is not even asked to choose. It rules over the groups and also navigates Lebanese politics. In short, Syria is a country conducting itself according to its interests, and this is the kind of partner Israel is looking for.
But Israel is finding it difficult to distinguish between interests and dreams. Israel is demanding that Syria cut its ties with Iran, with Hezbollah, with Hamas and the rest of the groups as a precondition for negotiations. This is a dream. The interest is allowing negotiations with Syria to bring about strategic change, instead of conditioning negotiations on change. We will see, for example, what Iran, Hezbollah and Hamas will say when negotiations between Israel and Syria start. Will Iran break ties with Syria or will it also emerge as yet another pragmatic state that knows it needs Syria no less than Syria needs it? Will Hamas or Hezbollah go to war with Syria, or do they also recognize where their supply line lies? To answer these questions does not require courage. Just the opening of personal diaries and setting a date for a first meeting. Meanwhile, it is apparently easier to hold "no negotiations" with Hamas and count Qassams than to conduct a policy.
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text:
The stock market is closed today.
It's the Fourth of July holiday and you'll probably be nursing a hangover and perhaps a sunburn over the next few days.
So why not kick back on the couch in the air conditioning and watch one of these Wall Street movies while you recover?
You'll definitely enjoy yourself, and you might even learn something.


text:
Seeking a Lightning to Micro USB adapter? Apple has you covered
Live in the US, and want a Lightning to Micros USB adapter? Apple has you covered. It has just begun offering a new $19 adapter called "Lightning to Micro USB Adapter" (who would've guessed?). Of course, this is only compatible with the current lineup of Lightning-sporting devices, including the iPhone 5, iPad mini, iPad 4th gen, 5th gen iPod touch, and 7th gen iPod nano. If you are interested in this for whatever your reasoning, head on down to the source link below or click here.
What do you think? Interested in one? Any other adapters Apple should make? Let us know in the comments.
Via: AppAdvice, The Verge, Apple Store