(3D) dilution of precision TDOP Time dilution of precision GDOP Geometric dilution of precision These values follow mathematically from the positions of the usable satellites. Signal receivers allow the display of these positions (skyplot) as well as the DOP values. The term can also be applied to other location systems that employ several geographical spaced sites. It can occur in electronic-counter-counter-measures (electronic warfare) when computing the location of enemy emitters (radar jammers and radio communications devices). Using such an interferometry technique can provide certain geometric layout where there are degrees of freedom that cannot be accounted for due to inadequate configurations. The effect of geometry of the satellites on position error is called geometric dilution of precision (GDOP) and it is roughly interpreted as ratio of position error to the range error. Imagine that a square pyramid is formed by lines joining four satellites with the receiver at the tip of the pyramid. The larger the volume of the pyramid, the better (lower) the value of GDOP; the smaller its volume, the worse (higher) the value of GDOP will be. Similarly, the greater the number of satellites, the better the value of GDOP. Interpretation The DOP factors are functions of the diagonal elements of the covariance matrix of the parameters, expressed either in a global or a local geodetic frame. Computation As a first step in computing DOP, consider the unit vectors from the receiver to satellite i: where denote the position of the receiver and denote the position of satellite i. Formulate the matrix, A, which (for 4 pseudorange measurement residual equations) is: The first three elements of each row of A are the components of a unit vector from the receiver to the indicated satellite. The last element of each row refers to the partial derivative of pseudorange w.r.t. receiver's clock bias. Formulate the matrix, Q, as the covariance matrix resulting from the least-squares normal matrix: In general: where is the Jacobian of the sensor measurement residual equations , with respect to the unknowns, ; is the Jacobian of the sensor measurement residual equations with respect to the measured quantities , and is the correlation matrix for noise in the measured quantities. For the preceding case of 4 range measurement residual equations: , , , , , , , and the measurement noises for the different have been assumed to be independent which makes . This formula for