<p>This paper addresses the problem of selecting suitable time difference of arrival (TDOA) measurements for passive coherent location with multiple-transmitters/receivers. Firstly, we propose a unified mathematical framework for the following target location algorithms: spherical interpolation (SI), spherical intersection (SX), and nonlinearly constrained least squares (NLCLS). This paper generalizes the currently available models for scenarios with multiple-transmitters/one-receiver and one-transmitter/multiple-receivers. The SI/SX algorithms use closed-form expressions to tackle the location problem without considering all the nonlinear relationships among the optimization variables. As for the NLCLS algorithm, such nonlinearities are taken into account with the help of nonlinear constraints. Effects like multipath propagation and shadow fading increase TDOA measurement values resulting in outliers. To remove the outliers, we propose three TDOA selection processes. The first one uses a simple comparison rule. The second approach iteratively detects outliers based on cost function comparisons. The last approach divides the search region into cuboids. The cuboid-based approach separates consistent TDOAs from outliers, and its centroid represents a new location estimate. The numerical experiments show that the proposed cuboid-based method has greater robustness when increasing the probability of outliers.</p>
<p>This paper addresses the problem of selecting suitable time difference of arrival (TDOA) measurements for passive coherent location with multiple-transmitters/receivers. Firstly, we propose a unified mathematical framework for the following target location algorithms: spherical interpolation (SI), spherical intersection (SX), and nonlinearly constrained least squares (NLCLS). This paper generalizes the currently available models for scenarios with multiple-transmitters/one-receiver and one-transmitter/multiple-receivers. The SI/SX algorithms use closed-form expressions to tackle the location problem without considering all the nonlinear relationships among the optimization variables. As for the NLCLS algorithm, such nonlinearities are taken into account with the help of nonlinear constraints. Effects like multipath propagation and shadow fading increase TDOA measurement values resulting in outliers. To remove the outliers, we propose three TDOA selection processes. The first one uses a simple comparison rule. The second approach iteratively detects outliers based on cost function comparisons. The last approach divides the search region into cuboids. The cuboid-based approach separates consistent TDOAs from outliers, and its centroid represents a new location estimate. The numerical experiments show that the proposed cuboid-based method has greater robustness when increasing the probability of outliers.</p>
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