Detection and estimation for multiple targets with two omnidirectional sensors in the presence of false measurements

A track-before-detect methodology for target detection and estimation in the presence of false measurements is presented that uses two omnidirectional passive sensors. The estimation technique is based on maximum-likelihood estimation. The measurement model is nonlinear and includes false alarms. The algorithm is first developed for a single target and then extended to multiple targets. For multiple targets, unresolved measurements are also considered to provide a realistic analysis of targets crossing in the measurement space. The Cramer-Rao lower bound is derived for the target parameter estimation in the presence of false measurement. A detection mechanism that can validate the existence of a target corresponding to the estimated track is formulated. For a single target, it is shown that only the global maximum leads to the acceptance of the target hypothesis. The test for multiple targets is obtained by formulating a multiple-hypotheses problem. The theoretical performance predictions are validated via Monte Carlo simulations. The effect on the performance of the density of false measurements is illustrated in examples. The highest false-measurement density for which this technique works corresponds to SNR=2 dB