The Enhanced Locating Performance of an Integrated Cross-Correlation and Genetic Algorithm for Radio Monitoring Systems
2014

Yao-Tang Chang
Department of Information Technology
Kao Yuan University
82151KaohsiungTaiwan

Chi-Lin Wu
Department of Electro-Optical Engineering
National Formosa University
632YunlinTaiwan

Hsu-Chih Cheng firstname.lastname@example.com
Department of Electro-Optical Engineering
National Formosa University
632YunlinTaiwan

The Enhanced Locating Performance of an Integrated Cross-Correlation and Genetic Algorithm for Radio Monitoring Systems

Sensors
14201410.3390/s140407541Received: 14 March 2014; in revised form: 20 April 2014 / Accepted: 21 April 2014 /sensors * Author to whom correspondence should be addressed; E-Mail:
The rapid development of wireless broadband communication technology has affected the location accuracy of worldwide radio monitoring stations that employ time-difference-of-arrival (TDOA) location technology. In this study, TDOA-based location technology was implemented in Taiwan for the first time according to International Telecommunications Union Radiocommunication (ITU-R) recommendations regarding monitoring and location applications. To improve location accuracy, various scenarios, such as a three-dimensional environment (considering an unequal locating antenna configuration), were investigated. Subsequently, the proposed integrated cross-correlation and genetic algorithm was evaluated in the metropolitan area of Tainan. The results indicated that the location accuracy at a circular error probability of 50% was less than 60 m when a multipath effect was present in the area. Moreover, compared with hyperbolic algorithms that have been applied in conventional TDOA-based location systems, the proposed algorithm yielded 17-fold and 19-fold improvements in the mean difference when the location position of the interference station was favorable and unfavorable, respectively. Hence, the various forms of radio interference, such as low transmission power, burst and weak signals, and metropolitan interference, was proved to be easily identified, located, and removed.

Introduction

Radio frequency monitoring systems are applied worldwide to protect and maintain a favorable radio frequency environment that enables easy communication at any location and at any time using any device [1,2]. Since AOA monitoring/location stations cannot compensate for geographical changes that affect the radio frequency environment, the accurate performance of angle-of-arrival (AOA) location technology has degraded severely.

Wireless broadband communication technology has developed rapidly worldwide. Hence, the amount of the various forms of interference that can occur when using advanced communication technologies, such as low transmission power, burst and weak signals, and metropolitan interference, has increased rapidly too [3,4].

New monitoring and location technologies have been developed to prevent radio interference. Several previous studies have proposed integrating a time-difference-of-arrival (TDOA)-based location system with existing AOA-based location systems [5][6][7][8][9]. Hence, the complementary application of integrated TDOA/AOA location systems can improve the location accuracy and extend the location coverage area.

TDOA-based location technology involves two main processes, namely cross-correlation and solving hyperbolic algorithms [10]. General cross-correlation technology is beyond the scope of the current study, and solving hyperbolic algorithms is difficult because searching for the best solution resulting from a set of nonlinear equations is a crucial problem in TDOA location technology.

Fang and Chan have presented numerous methods for solving nonlinear hyperbolic algorithms [11,12] and have provided an exact closed-form solution that is not available when applying the Taylor-series method, which might converge to a local minimum when the initial input is unsatisfactory. In addition, a closed form solution for localization of distant transmitters was developed by using the triangulation of hyperbolic asymptotes rather than hyperbolae themselves [13]. To improve the location accuracy of previously proposed TDOA-based monitoring and location systems [10], a general cross-correlation and genetic algorithm (GA) [14][15][16][17] was proposed and evaluated in a metropolitan area, Tainan (Taiwan). The proposed integrated cross-correlation and GA (hereafter abbreviated as CCGA) was applied to identify the global optimal solution in nonlinear hyperbolic equations.

The remainder of this paper is organized as follows: Section 2 delineates the configuration of the TDOA-based monitoring and location system. Section 3 describes the experimental procedure of the proposed integrated CCGA. Subsequently, the simulation results obtained when applying the GA in extending the number of locations and in a three-dimensional (3D) environment are provided in Section 4. Furthermore, the experiments performed in Taiwan and the GA improvement are analyzed compared to the matrix calculation by computer in Section 5. Finally, Section 6 presents the conclusions.

Design of the Time-Difference-of-Arrival-Based Monitoring and Location System

In general, the following radio-spectrum monitoring technology is applied: (1) time of arrival (TOA), and (2) the AOA recommended in the 1995 and 2010 International Telecommunications Union Radiocommunication (ITU-R) spectrum monitoring handbooks, respectively [1,2]. In these publications, the monitoring site installation procedure and measuring equipment to execute the monitoring and location of radio interference stations is described. Furthermore, the advantages and disadvantages of TDOA-based technologies are presented in the ITU-R Report SM.2211 [18]. The transmitter and receiver must be precisely synchronized when using TOA location technology. The AOA of location technology is limited by non-line-of-sight (NLOS) and multipath effects. Hence, low transmission power, burst and weak signals, and metropolitan interference are difficult to identify and analyze (locate) using AOA technology. The proposed TDOA technology can be used to overcome the aforementioned shortfalls and is presented as follows.

Concept of the Time-Difference-of-Arrival-Based Location System

When applying the conventional TDOA scheme, two pairs of TDOA estimates are obtained using a cross-correlation algorithm [19][20][21][22][23][24][25]. The two pairs of estimates are then converted into two pairs of range (distance) difference measurements that in TDOA are multiplied by the propagation speed of electromagnetic waves (i.e., 3 × 10 8 m/s). The position (X T ,Y T ) is achieved by identifying the solutions to both Equations (1) and (2) because two hyperbolic curves are created to determine a crossing point (one TDOA estimate can be used to create one hyperbolic curve):

(1) (2) where (X A ,Y A ), (X B ,Y B ), and (X C ,Y C ) denote the known positions (i.e., longitude and latitude) of three TDOA monitoring stations, and d ij denotes the difference in range (distance) between monitoring stations i and j. The coordinates (X A ,Y A ) are generally designed as the position of the central control monitoring station configured with the central control server, and (X T ,Y T ) denotes an unknown position of the measured transmitter (i.e., interference signal).

TDOA-based location technology involves two main processes, namely cross-correlation and solving hyperbolic algorithms based on Equations (1) and (2) [10]. The cross-correlation algorithm calculates the time difference of arrival (TDOA) between a pair of TDOA-based stations. Subsequently, the TDOA is converted into the difference in range between the pair of TDOA-based stations, as shown in Equations (1) and (2).

In the first process, the general cross-correlation method is used to estimate the time delay coming from the interfering station and the emission signal of the interfering station is simultaneously detected between pairs of location stations. In the second process solving the hyperbolic algorithm presented in Equations (1) and (2) is difficult because searching for the best solution resulting from a set of nonlinear equations is a crucial problem in TDOA location technology.

In theory, the Equations (1) and (2) can only intersect at one point. In practice, since the effect of non-line of sight, multi-path and the presence of noise (e.g., the residual errors of GPS synchronization) results in the variance of a pair of hyperbolae (i.e., the error of time difference of arrival), the true location of the emitter (i.e., refer to as interference station) should be inside the region enclosed by the overlap of a pair of hyperbolae spreading results. In addition, ghost targets (i.e., ambiguous problem) occur because a pair of symmetry hyperbolae is characterized by a leading and lagging time to intersect at two locations for a positive range distance.

The proposed radio spectrum monitoring system, which follows the ITU-R spectrum monitoring requirement [1,2] and installed in the current study, can provide additional information, including the radio frequency and power spectrum of the interference signal to identify various targets. Here, the higher power spectrum density denotes a shorter distance from the emitter (i.e., interference station) to the TDOA monitoring station and then can distinguish either lag or lead time differences of arrival on hyperbolae.

Furthermore, in order to improve the location accuracy in current TDOA monitoring systems, selecting the arbitrary TDOA station #A or #B is installed with AOA configuration to provide additional bearing line information on TDOA complementary application [5,10,26]. Hence, the integrated TDOA/AOA monitoring system was applied to enhance the location accuracy by the current authors in previous work [10]. In the current experimental design, the AOA monitoring center can be installed in TDOA #1 (Luzhu, Kaohsiung Science Park presented in below section 3). That is, the TDOA #1 is characterized by TDOA and AOA measurements to enhance the location accuracy. Hence, the combination of TDOA #1 and an additional AOA technique at the control center station can process both hyperbolae (TDOA) and bearing line (AOA) calculations to significantly resolve the effect of ghost targets, non-line of sight and multi-path. Importantly, since the localization error of the spreading region enclosed by the spreading region of a pair of hyperbolae, the proposed genetic algorithm must be properly modified with selection, crossover, mutation procedures and the definition of fitness function to make an accurate convergence requirement in error tolerance.

Configuration of the Proposed Time-Delay-of-Arrival-Based Monitoring and Location System

As shown in Figure 1, the configuration process consists of:

(1) a site survey and specification design phase;

(2) a TDOA trial installation and measurement phase;

(3) a phase in which the location is calculated using a cross-correlation approach; (4) a phase in which a hyperbolic equation is solved; and (5) a phase in which the position of the interference transmitter (X T ,Y T ) is added to a geographical map.

The design configuration the TDOA monitoring and location system [11,12] consists of hardware, a cross-correlation and solving hyperbolic algorithm, and a user interface. Importantly, an evaluation criterion is applied to verify the performance of the location accuracy. When TDOA is applied, multiple location stations are used to measure the time required for a pending signal to arrive at each location station receiver.

The hardware and software of the TDOA technology were designed based on the performance of the receiver, time synchronization, and network connectivity observed in the experiments to ensure that accurate solutions were obtained. The architecture of the monitoring and location system and the location requirements are shown in Figure 2.

Signal synchronization is crucial in TDOA-based monitoring. TDOA-based monitoring and location systems typically use a GPS receiver to establish a common time reference. For the various TDOA monitoring station located in different geographical area, the residual errors of GPS synchronization results from various satellite clocks and the transmission environment, such as of ionosphere delay and tropospheric delay, etc. In order to connect together each monitoring station for time synchronization, a digital circuit switch network (e.g., PDH, SDH network and fixed line) is better than a packet switch network (i.e., IP network) for precise synchronicity accuracy. Here, the leased line (fixed fiber-to-the-building, FTTB) for the active master line and broadband integrated service digital network (B-ISDN) for the back-up line are employed, respectively, in the current experiment.

Furthermore, many synchronization polices are designed to compensate different link delays, including NTP, RBS, TPSN, FTSP, GTSP with time synchronization and the Whistle method without time synchronization, respectively [26][27][28][29][30][31]. For the commercial application of the radio spectrum monitoring system installed in the current study, the acquired time stamp signal from a GPS antenna is transported by the master monitoring station and used in slave monitoring (TDOA #2 and #3 presented Applying general cross-correlation approaching 1. Achieving time difference of arrival of a pair of locating station 2. converting into range (distance) difference

Applying various approaching for solving nonlinear equation problem?

The locating calculation phase for TDOA system Indentifying and Analyzing(location) of TDOA locating 1. Depending on the power lever of measured signal to determine the monitoring station is close to interference transmission signal.

Proper bandwidth and sampling rate is selected for un-known interference station

Coverage area estimation and Site survey 1. 20km~30km Coverage area for site survey 2. Design of hardware, software and application layer 3. Requiring download and upload data rate of 2 Mbyte and 300 Kbyte, respectively in 3G network

Site survey and specification design phase

Approaching derivation seen in appendix

Current Genetic algorithm(GA)

The additional scenarios simulated with MATLAB GAtool

Previous hyperbolic algorithm

Practical experiment with genetic algorithm embedded in TDOA locating station

Locating calculation to integrate into map Transmitter of 88.3MHz,91.5MHz, 89.1MHz and 91.9MHz is measured and calculate the location of longitude and latitude. (longitude, latitude) in below section 3) to steer the local clocks. Here, the master monitoring station (i.e., TDOA #1) is installed in Luzhu, Kaohsiung Science Park (i.e., called central control station server) and two slave monitoring stations are installed in TDOA # 2 and #3, respectively, as seen in below section 3. Like previous configurations [10], the experimental monitoring station used in this study was configured in three steps, namely hardware selection, software design (cross-correlation and solving hyperbolic equations algorithm), and estimation of location errors.

TDOA trial installation and measurement phase

To ensure that the location performance is favorable, TDOA technology depends on the precise identification and estimation of frequency, bandwidth, and modulation mode coming from unknown emission stations to determine the optimum sample rate of received signals. In this experiment, frequency modulation (FM) characterized by a 200 KHz bandwidth is assumed as the interference station. The FM transmission signal was detected for a 1-min time interval to execute the TDOA location processing.

The sample was optimized to improve the cross-correlation peak by selecting the proper bandwidth of the filter according to the difference in the arrival time. Unlike AOA technology, no demodulation is conducted in the sampling process when using TDOA technology. The transmission signal in the time domain is combined with the GPS time stamp to execute the cross-correlation algorithm at the central station.

According to the ITU-R spectrum monitoring handbook 1995, the TDOA monitoring and location system must feature the monitoring function, thus enabling the operator to easily identify the variation in specific interference signals for a modulation type and, subsequently, select a precise sampling rate at each TDOA-based monitoring station. Hence, the user interface was designed as shown in Figure 3. Three TDOA-based monitoring stations were installed in this study. Therefore, three pairs of TDOA estimates and three pairs of hyperbolic curves were obtained. To enhance the location accuracy of the proposed TDOA monitoring and location system, the user interface must be designed such that the hyperbolic curve exhibiting a favorable location performance can be observed in the operating screen. Hence, the operator is easy to select better location line from calculated hyperbolic curve, as shown in the lower right corner of Figure 4.

Experimental Design Conducted to Test the Time-Difference-of-Arrival-Based Monitoring and Location System

Based on the process shown in Figure 1, the experimental design was developed further, as shown in Figure 5. TDOA-based location technology involves two major processes, namely cross-correlation and solving hyperbolic algorithms. The cross-correlation approach entails calculating the TDOA based on a pair of TDOA-based location stations. General cross-correlation technology is beyond the scope of this study.

Solving hyperbolic algorithms is difficult because solving a set of nonlinear equations presented in Equations (1) and (2) is the biggest challenge to find the best solution in TDOA location accuracy [11,12]. Except for the location solution obtained by the approach of solving nonlinear hyperbolic equations, a novel closed-form location estimator was developed for TDOA emitter localization based on triangulation of the TDOA hyperbolic asymptotes rather than the TDOA hyperbolae themselves applied in current study. By employing the clustering the bearing angles of the linear asymptotes with a kernel density estimate, the solution of associating the linear asymptotes with the emitter is presented to resolve significant location errors and ghost targets arising from the presence of moderate noise without the consideration of multipath distortion, multiple and moving interference emission [13].

By using the general cross-correlation algorithm shown in Figure 6, an example of the time difference at 88.3 MHz was obtained. Subsequently, the range (distance) difference was obtained that the time difference of arrival is multiplied by the speed of radio waves (3 × 10 8 m/s).   In this study, two types of algorithm for solving hyperbolic equations were evaluated, as shown in Figure 5. One approach involved solving the hyperbolic equations by using the derivation process described in the Appendix. The second approach involved applying the GA in a practical experiment.

First, to improve the location accuracy, various scenarios, such as an extended TDOA-based location station (similar to sensor networks) and a 3D environment (considering an unequal location antenna configuration) were investigated.

Second, the proposed CCGA was evaluated in the metropolitan area of Tainan. Finally, according to the ITU-R Report SM.2211 [18], the location performance of TDOA-based technology depends strongly on the position of the measured transmitter station. Figure 7 illustrates favorable and unfavorable performance at the central point of the triangle and away from the TDOA-based coverage area, respectively. The most unfavorable scenario in the experiment occurred when the measured station broadcasted at 91.9 MHz. In the trial experiment, three monitoring stations, including the Jinkang and Gaote stations in the Tainan metropolitan area (i.e., southern Taiwan), were selected to create an appropriate triangular arrangement. The central station was established at Luzhu (Kaohsiung Science Park). A distance of approximately 9.94 km was calculated between Jinkang station and Luzhu. As shown in Figure 7, the proposed experimental configuration is major implemented in the metropolitan area. Hence, it is easy to prove the TDOA location reliability and accuracy even the decay of the location performance resulting from the multi-path effect [3,4,1825].

The central station, AOA/TDOA #1, was installed in Luzhu. TDOA #2 and TDOA #3 (Tainan metropolitan area) acted as remote monitoring stations. These monitoring stations were selected by considering the effects of the barriers created by buildings, ground reflection, and reflected waves. In addition, an upload transmission rate suitable for a 3G mobile network was required. The positions (longitude and latitude) of the TDOA-based monitoring and transmission stations are listed in Table 1.

Each specific central frequency (i.e., 88.3 MHz, 91.5 MHz, 89.1 MHz, and 91.9 MHz) was recorded for 200 s in a 34-Mbyte storage file, and the monitoring transmission signal of the receiver was then played back to calculate the location error. The three monitoring signals were simultaneously transferred to the central control station via the remote stations, and the time difference of the three pairs of measured signals was then obtained using the integrated CCGA.

Enhancing the Accuracy of the Time-Difference-of-Arrival-Based Location System Using the Genetic Algorithm in Simulations

TDOA estimation is divided into two stages. The first stage involves estimating the time delay to determine the TDOA between pairs of location station. In the second stage, the TDOA value is converted into a value of pairs of range (distance) difference, and a set of nonlinear hyperbolic equations is obtained. Available algorithms are then used to determine the exact solution of the nonlinear hyperbolic equations in the second stage. Because solving multiple equations involving highly nonlinear problems is difficult, several iterative methods, including the maximum likelihood estimation and constrained optimization methods, have been proposed.

Location algorithms are generally categorized as deterministic or stochastic computing algorithms. Deterministic algorithms, such as the aforementioned algorithms proposed by Fang and Chan, as well as Taylor series expansion [11,12], are applied when using direct or iterative methods.

The approach of Fang is characterized by favorable location performance in LOS scenarios, but not in the locating station scenario. However, when the location error at a locating station is substantial, the performance of the algorithm proposed by Fang is severely affected.

The algorithm proposed by Chan features low computational complexity and favorable location performance when a propagation channel model with additive white Gaussian noise (AWGN) is used. However, the location performance rapidly declines in non-LOS scenarios.

When using the Taylor series expansion algorithm, the initial position for iterative processing must be estimated. By obtaining the local minimum square solution, the estimated position is improved in each iterative process and the computational complexity increases considerably. The Taylor series expansion algorithm can be adapted to various types of propagation in open-air environments. However, determining the initial position of an unknown station influenced by interference is difficult, thus reducing the location accuracy.

Stochastic computing algorithms are employed to identify the optimal solution within a matching condition pool. The termination condition is defined and assigned to prevent divergence or premature convergence toward local suboptimal results. Hence, the results generated by stochastic computing algorithms are characterized by slight differences in each iterative process, and these algorithms are suitable for solving searching space problems involving numerous data.

Features of the Genetic Algorithm Applied in Time-Difference-of-Arrival-Based Location Systems

The GA, which is classified as a stochastic computing algorithm, is an effective search method that has been used widely in identifying the optimal solutions of various problems. The GA mimics the mechanisms of natural evolution, in which stronger individuals are more likely to survive in a competitive environment. A GA treats each potential solution of a problem as an individual, represented by a string of parameter values in a binary or real form. These parameter values represent the genes of a chromosome. In GA evolution, the suitability of each chromosome as a solution to the optimization problem is indicated by its fitness value based on a specific objective fitness function. The GA commences by establishing a population pool of randomly selected chromosomes.

When inconsistencies exist among individuals (i.e., the genes of a chromosome), a new generation of genes is created by employing the selection, crossover, and mutation processes. The fitness value of the new generation is evaluated, and evolution continues until the optimal solution or the termination condition is reached. The first generation is called the parent generation. The subsequent generation consists of the child chromosomes, which are created through crossover and mutation processes. The population consists of all generations.

Compared with iterative deterministic methods for identifying solutions such as the GA [1417], direct solving methods, such as those of Fang and Chan and the Taylor series expansion algorithm [11,12], enable the global solution to be identified easily, and the solutions are not divergent or converge toward a local suboptimal result. However, when the transmission channel passes through an unfavorable open-air environment and the target is a mobile transmitter, using direct solving methods reduces the location accuracy and increases the complexity of calculation.

In this study, the proposed GA featured parallel search ability and exhibited a high probability to prevent local suboptimal results, thus enabling the nonlinear hyperbolic equations in Equations (1) and (2) and the Appendix to be solved. In addition, the capability of the proposed GA to achieve the optimal global solution depends on the crossover and mutation processes as well as flexible terminal condition selection. The procedure used to apply the proposed GA in the TDOA-based location system is shown in Figure 8. The basic operations executed in the proposed GA approach are described briefly as follows:

(a) The GA commences by establishing an initial population pool of randomly selected chromosomes. As shown in Figure 8, the real genetic algorithm is applied to create the solutions of the parameters as a string of real values. Thus, a GA treats each potential solution of a problem as an individual, represented by a string of parameter values in a real form. (b) In the recursive processing of the GA, the fitness value indicates the suitability of each chromosome. In other words, the fitness value is determined based on an objective fitness function and serves as a solution to the optimization problem. The fitness function for the location accuracy of three TDOA-based location systems is expressed as:

where i denotes one of the TDOA-based monitoring stations; (X i ,Y i ) denotes the position (i.e., coordinate values of longitude and latitude) of the i-th TDOA-based monitoring station; and d i is the distance between the i-th TDOA-based monitoring station and the interference transmitter (X T ,X T ) used to calculate the location error. The goal of the proposed GA scheme is to identify the optimal (X,Y) values, which are the values at which the fitness function f (X,Y) is minimal. Hence, (X T ,X T ) is the optimal solution for the coordinate values of the interference transmitter.

(c) In each recursive generation of the GA, the selection operator determines the individual strings that are copied into the mating pool and used as the basis for creating the subsequent generation. In general, a particular string characterized by a high probability that the value of the fitness function is minimal is copied into the mating pool. In this study, the most common method, roulette-wheel selection, was employed to select the candidate strings to be reproduced in a statistical fashion based only on their relative fitness function values.

(d) Crossover is an approach to moving through the space of possible solutions based on the information associated with existing solutions. In the crossover process, the GA selects two strings randomly from the mating pool and then determines whether or not crossover should occur by applying a parameter known as the crossover probability, p. In this study, the uniform crossover probability of p = 50% was employed.

(e) Mutation prevents a population of chromosomes from becoming too similar to each other, thereby slowing or stopping evolution, or causing the GA to converge toward a local suboptimal solution. In this study, uniform mutation was employed.

Improving the Performance of the Time-Difference-of-Arrival-Based Location System by Using the Genetic Algorithm

First, to simplify the development procedure, the GA tool of the MATLAB software application was employed to replace the hyperbolic algorithm shown in the Appendix. Second, the programming language created using the MATLAB software application was embedded into the digital signal-processing module of the TDOA-based location system configured on the central control station.

ITU-R Report SM.2211 [18] indicated that the various location performance is relative to the position of the emission station (i.e., interference station). In this publication for qualitative analysis, the location accuracy of the central point of the triangle is better than that of away from the geographical coverage area in TDOA-based location station. In addition, the TDOA location performance relies on the topology and coverage area of location station and the interference type resulting from the low transmission power, burst/weak signals, and metropolitan emission.

In the simulation, which was conducted by using the GA tool of the MATLAB software application, the signal of the transmitter station could be received from any direction, and the effects of
2 3 1 2 2 ) ) ( ) ( ( ) , (        i i i T i T d Y Y X X Y X f
power loss and diffraction in the transmission channel were neglected. Figure 9ac show that the simulation configuration was varied to investigate the location accuracy in various scenarios (a) inside the coverage area of the TDOA-based location system and (b) outside the coverage area of TDOA-based location system. Furthermore, the location accuracy of spreading (extending) location networks (i.e., sensor networks) was analyzed while the number of TDOA-based location systems was increased. The antenna height of the three TDOA-based location stations was configured based on the same sea-level surface. The positions of the location stations were assigned to simulated coordinates of (0,0), (10,0), (0,10), and (10,10). The kilometer was the unit of distance.

In the simulation of the GA, the radiowave transmission speed was 3 × 10 8 m/s (i.e., electro-magnetic wave speed), the termination condition was 500 iterations, and obtaining the 10 groups (X T ,X T ) of location positions in the simulated processing. The results of simulating the proposed GA are shown in Figure 10. The GA exhibited more favorable performance than did the computing approaches presented in the Appendix. Figure 10. The locating accuracy by using proposed GA in the various scenarios presented in Figure 9.

In a practical scenario, the antennas of three TDOA-based location stations were installed on different buildings. The heights were varied to determine their effects on the location accuracy. To improve the location performance, the 3D location error was investigated. Figure 11 shows that the monitoring site was deployed in 3D free space. Five monitoring positions, Stations A, B, C, D, and E, were positioned at (0 km, 0 km, 0 m), (10 km,10 km,100 m), (10 km,10 km, 100 m), (0 km,10 km,100 m), and (10 km,0 km,0 m), respectively. The distance along the X and Y axes was measured in kilometers. The distance along the Z axis (the antenna height above sea-level surface) was measured in meters.  As shown in Figure 12, the location error was analyzed while the number of monitoring sites was gradually increased. Figure 12. The locating accuracy by using the proposed genetic algorithm in a 3D free space environment.
A (X A ,Y A ) C(X C ,Y C ) Z Y B (X B ,Y B ) E(X E ,Y E ) d D d C d A d B d E S(X T ,Y T ) (10km,

Implementation of the Proposed Integrated CCGA Algorithm in a Time-Difference-of-Arrival-Based Location System

The location accuracy of the calculation approach presented in the Appendix and the GA were compared, as shown in Figure 5, and the 10 results measured at the 88.3-MHz transmitter (assumed interference station) when the GA was not used are shown in Table 2. To enhance the accuracy of a previous experiment described in [10], the proposed integrated CCGA was implemented in the current study. The location performance of the proposed algorithm measured at an 88.3-MHz broadcasting station is shown in Table 3. The experimental results regarding the performance of the hyperbolic algorithm and proposed GA in TDOA monitoring are summarized in Table 4. The location errors varied according to the position of the broadcasting station transmitting at a frequency of 88.3, 91.5, 89.1, and 91.9 MHz, respectively. Table 4 indicates that favorable and unfavorable performance occurred at the central point of the triangle and outside the TDOA-based coverage area, respectively. The least favorable scenario occurred when the measured station broadcasted at 91.9 MHz.

The results indicated the worst location accuracy of 91.9 MHz at a circular error probability (CEP) of 50% was less than 60 m when the multipath effect was present. Compared with the results obtained when applying the hyperbolic algorithm in the conventional TDOA-based location system, the mean difference improved 17-fold (i.e., 230.31/13.05) and 19-fold (i.e., 929.26/48.87) at 88.3 MHz and 91.9 MHz, respectively. Moreover, the standard deviation improved 3-fold (i.e., 22.24/6.49) and 11-fold (i.e., 275.76/24.09) at 88.3 MHz and 91.9 MHz, respectively. Hence, proposed integrated CCGA was proved to exhibited high location accuracy, despite metropolitan interference.

Conclusions

The rapid development of wireless broadband communication technology has affected the location accuracy of worldwide radio monitoring stations that employ TDOA location technology, and the stations in Taiwan are no exception. In this study, TDOA-based location technology was implemented in Taiwan for the first time according to International Telecommunications Union Recommendation requirements regarding monitoring and location applications. To improve location accuracy, various scenarios, such as a three-dimensional environment (considering an unequal locating antenna configuration), were investigated.

Moreover, the proposed GA was integrated with a cross-correlation algorithm (CCGA) in a practical experiments for the first time. The results indicated that the location accuracy at a CEP of 50% was less than 60 m when a multipath effect was present in a metropolitan area.

Compared with results obtained when applying the hyperbolic algorithm in the conventional TDOA-based location system, the mean difference improved 17-fold and 19-fold at 88.3 MHz and 91.9 MHz, respectively. Moreover, the standard deviation improved 3-fold and 11-fold at 88.3 MHz and 91.9 MHz, respectively. Hence, the proposed CCGA scheme was proved to identify, locate, and remove radio interference resulting from low transmission power, burst and weak signals, or metropolitan.

In future work, the proposed integrated cross-correlation and genetic algorithm (CCGA) will be used to further investigate the location error resulting from more mobile detected target, and various environmental issues in the effect of non-line of sight (NLOS) and multi-path. The interference station (longitude, latitude) of is thus solved.

Figure 1 .
1The major installation processing consist of below step in current proposed TDOA-based monitoring station.

Figure 2 .
2The proposed TDOA monitoring/locating system (a) system architecture (b) physical configuration in current experiments.

Figure 3 .
3The proposed interface of the TDOA-based monitoring/locating system by selecting the sampling time, filter, bandwidth, etc.

Figure 4 .
4The proposed user interface on integrating the monitoring power spectrum density and locating position by indicating hyperbolic curve.

Integrated monitoring and locating (hyperbolic curve) funtion

Figure 5 .
5The experimental processing of proposed and conventional TDOA-based monitoring/location system. analyzing the various approaching for mean difference, standard deviation and circular error probability(CEP)50%, respectively.Applying various approaching for solving nonlinear equation problem?Approaching derivation seen in appendix range (distance) difference of arrival d ij Simulated Genetic algorithm(GA) with MATLAB GAtool

Figure 6 .
6The 88.3 MHz experimental evaluation of (a) time difference and (b) distance difference using cross-correlation algorithm in TDOA-based monitoring/locating system.

Figure 7 .
7The proposed experiment configuration for investigating the TDOA limitation of location performance in multi-path effect of metropolitan area.

Figure 8 .
8The procedure of proposed genetic algorithm applied in TDOA-based locating system.

Figure 9 .
9The simulated configuration of various scenarios on (a) inside coverage area (b) outside coverage area (c) spreading (extending) locating networks of TDOA-based locating system.

Figure 11 .
11The 3D locating error analysis of monitoring/locating station (sensor station).

Figure A1 .
A1The demonstrated diagram of solving the location by using the previous computing algorithm.Subtract the square of Equation (A2) from the square of Equation (A1) as follows: ij denotes the distance between the i-th and j-th stations. The symbol c denotes the (A7) described as below into A and B matrix of Equations (A18) and (A8), respectively:

Table 1 .
1The proposed monitoring and transmission position of the proposed experiment.Proposed monitoring and
measured broadcasting station
Longitude
Latitude

TDOA #1 monitoring (Luzhu)
Central control station
E 120∘15' 42.41"
N 22∘50' 21.70"

TDOA #2 monitoring station (Gaote)
E 120∘17' 9.00"
N 22∘55' 29.00"
TDOA #3 monitoring station (Jinkang)
E 120∘10' 14.60"
N 22∘59' 49.70"
88.3 MHz broadcasting station
E 120∘16' 26.36"
N 22∘54' 15.15"
91.5 MHz broadcasting station
E 120∘15' 29.00"
N 22∘56' 55.00"
89.1 MHz broadcasting station
E 120∘13' 45.00"
N 22∘59' 57.00"
91.9 MHz broadcasting station
E 120∘12' 33.00"
N 23∘0' 45.00"

Table 2 .
2The location performance using without genetic algorithm for the measured 88.3 MHz broadcasting station. measured broadcasting station of 88.3 MHz @Actual location (E 120.2656,N 22.9064)The Location
result
index

Longitude
Range
Error
(m)

Maximum
Range
Error (m)

Minimum
Range
Error (m)

Mean Difference
(m)

Standard
Deviation
(m)
Latitude

1
E 120.2673
222.62

274.53
200.82
230.31
22.24

N 22.9076

2
E 120.2673
236.58
N 22.9078

3
E 120.2671
200.82
N 22.9075

4
E 120.2671
206.87
N 22.9076

5
E 120.2673
251.54
N 22.9080

6
E 120.2672
274.53
N 22.9076

7
E 120.2672
214.47
N 22.9075

8
E 120.2672
244.51
N 22.9080

9
E 120.2672
221.55
N 22.9077

10
E 120.2673
229.64
N 22.9077

Table 3 .
3The location performance using the proposed genetic algorithm for the measured 88.3 MHz broadcasting station. measured broadcasting station of 88.3 MHz @Actual location(E 120.2656,N 22.9064)Table 4. The summarized results of the experiment for TDOA-based measured locations.The Location
result
index

Longitude
Range
Error
(m)

Maximum
Range
Error (m)

Minimum
Range
Error (m)

Mean Difference
(m)

Standard
Deviation
(m)
Latitude

1
E 120.2656
10.94

24.72
2.11
13.05
6.49

N 22.9065

2
E 120.2654
23.01
N 22.9064

3
E 120.2656
10.37
N 22.9065

4
E 120.2655
13.48
N 22.9065

5
E 120.2656
15.89
N 22.9065

6
E 120.2657
24.72
N 22.9062

7
E 120.2656
6.95
N 22.9064

8
E 120.2657
13.08
N 22.9064

9
E 120.2656
9.95
N 22.9065

10
E 120.2656
2.11
N 22.9064

Location
Technology

Frequency
(MHz)

Distributed
Range (m)

Location
accuracy @ CEP
50% (m)

Mean
difference
(m)

Standard
deviation
(m)

Conventional
Hyperbolic
algorithm for
TDOA location

88.3
20
235
230.31
22.24

91.5
110
230
260.50
76.97

89.1
350
550
515.22
137.25

91.9
950
950
929.26
275.76

Proposed
Genetic
algorithm for
TDOA location

88.3
40
12
13.05
6.49

91.5
78
22
23.45
14.89

89.1
140
56
51.57
16.87

91.9
158
60
48.87
24.09

© 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
AcknowledgmentsThis study was supported under grant No. NSC 102-2221-E-244-001 by the National Science Council and by the special-interference project of the National Communications Commission in Taiwan. Also, we thank National Instruments (NI) and the Telecom Technology Center (TTC) in Taiwan for providing essential measurement equipment and technical consultations, respectively.Author ContributionsAppendixBased onFigure A1published in[32], the derivation process used in locating the transmitter position (X T ,Y T ) is formulated as follows:
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New Neighbours: Modelling the Growing Population of γ-ray Millisecond Pulsars
8 Jul 2010

C Venter
A K Harding
T J Johnson
New Neighbours: Modelling the Growing Population of γ-ray Millisecond Pulsars
8 Jul 20106DD909E424551C6F9D1D4BF1EABABF5DarXiv:1007.1381v1[astro-ph.HE]
The Fermi Large Area Telescope, in collaboration with several groups from the radio community, have had marvellous success at uncovering new γ-ray millisecond pulsars (MSPs).In fact, MSPs now make up a sizable fraction of the total number of known γ-ray pulsars.The MSP population is characterized by a variety of pulse profile shapes, peak separations, and radio-to-γ phase lags, with some members exhibiting nearly phase-aligned radio and γ-ray light curves (LCs).The MSPs' short spin periods underline the importance of including special relativistic effects in LC calculations, even for emission originating from near the stellar surface.We present results on modelling and classification of MSP LCs using standard pulsar model geometries.

Introduction

To date, Fermi Large Area Telescope (LAT) has uncovered more than a dozen new γ-ray millisecond pulsars (MSPs) [17].These MSPs were discovered by folding the γ-ray data with the known radio period, using timing solutions provided by various radio telescopes.Discovery of the first eight Fermi-LAT MSPs [1] indicated that these MSP pulse profiles and spectral properties mimic those of the younger pulsars, and that there are non-zero lags between the radio and γ-ray peaks.However, the discovery of PSR J0034−0534 [3] exhibiting phase-aligned γ-ray and radio peaks provided evidence for co-location of the radio and γ-ray emission regions.This phenomena has previously only been observed for the Crab pulsar.Recent detection of γ-ray pulsations aligned with the radio peaks [23,4]  PSR J1939+2134 [6], as well as the first 'black-widow' MSP PSR J1959+2048 [15], now provide two more examples of this exceptional behaviour.Publication of a number of new γ-ray MSP detections are awaited [22] (some resulting from followup radio observations of unpulsed Fermi-LAT sources which appear in the First Fermi-LAT source catalogue), and the growing MSP population now constitute a significant fraction of all Fermi-LAT pulsar detections to date [2].

The γ-ray MSPs exhibit a variety of light curve (LC) shapes, and have been modelled using standard two-pole caustic (TPC), outer gap (OG), and pair-starved polar cap (PSPC) models [26], an annular gap model [11], lately also altitude-limited TPC / OG models [25], and lastly using a force-free magnetic field [7].We elaborate on some of these models in Section 2. (See [17] for a review of γ-ray pulsar population modelling.)

Pulsar Models

Below is a brief description of some modelling aspects (see [26] for details).

B-field

As before, we assume the retarded vacuum dipole field [10,13] to be the basic geometric structure of the MSP magnetosphere.The magnetic moment is inclined by an angle α with respect to the spin axis, while ζ is the observer angle.

Caustics Due to the rapid increase of the co-rotation velocity with altitude, approaching c as one reaches the light cylinder, it is important to include relativistic effects when calculating LCs.The effects of (i) B-field line curvature, (ii) time-offlight delays, and (iii) aberration combine to form intense caustic emission on the trailing field lines, while the emission is spread out on the leading field lines [20,14].

Polar Cap (PC) Model

The PC model [9] assumes extraction of primary charges from the stellar surface, acceleration of these charges to relativistic velocities along B-field lines, and subsequent γ-ray curvature radiation and pair creation at a very low altitude.A pair-formation front (PFF) is formed which screens the accelerating E-field (E || ) parallel to the B-field.

Faded PC Model This is a geometric approximation [25], where we use a rising and falling exponential function to modulate the assumed emissivity close to the stellar surface, as motivated by pair cascade modelling.

PSPC Model When E || is not large enough to accelerate primary electrons to sufficiently high energies so pair cascades will occur, it will be unscreened, and one expects acceleration and radiation from the full open field line region above the PC [18,19].SG / TPC Model When enforcing the boundary condition that E || vanishes on the last open B-field lines (LOFLs), the PFF altitude becomes dependent on magnetic co-latitude, and narrow gaps form just inside of the LOFLs [5].The SG model [21] may provide the framework for the geometric TPC model [12].OG Model The Goldreich-Julian charge density [16] changes sign at the null charge surface (NCS), and OGs are assumed to form close to the LOFLs, above the NCS [8,24].

Altitude-limited TPC / OG Models We lastly investigated geometric TPC / OG models with arbitrary lower, R min , and upper, R max , radial gap extensions [3].

Results

First 8 MSPs We found [26] that 6 of the 8 Fermi-LAT MSPs may be modelled using standard TPC / OG models, while 2 are exclusively fit by PSPC models.

PSR J0034−0534

Discussion and Conclusions

Our results indicate that the high-energy (HE) radiation comes from narrow gaps in the outer magnetosphere, requiring the presence of robust pair formation to screen much of the open field line region.However, MSPs do not produce enough pairs in the standard vacuum dipole models to screen E || .Standard geometrical models still provide suitable LC fits though, and the resulting α and ζ are in reasonable agreement with fits from radio polarization measurements.In the case of phase-aligned γ-ray and radio pulses, we infer the existence of co-located emission regions, as well as high-altitude radio emission.Future work includes investigations of HE spectra using full radiation codes, collective emission from MSPs in globular clusters, and pulsar population studies.

Fig. 1
1

from the famous 'first' MSP
C. VenterUnit for Space Physics, North-West University, Potchefstroom Campus, Private Bag X6001,Potchefstroom 2520, South AfricaA.K. Harding and T.J. JohnsonAstrophysics Science Division, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Acknowledgements CV is supported by the South African National Research Foundation.AKH acknowledges support from the NASA Astrophysics Theory Program.
. A A Abdo, Science. 3258482009

. A A Ab