Passive Tracking in Heavy Clutter With Sensor Location Uncertainty

Abstract

In order to address the problem of passive tracking from multiple asynchronous angle-only sensors with location uncertainty in heavy clutter, a new iterative maximum-likelihood probabilistic data-association algorithm is proposed in this paper. An iterative prediction-update framework is adopted in the algorithm to simultaneously estimate the target state as well as the sensor state. At the prediction stage, a deterministic sampling approach is used to adjust the measurement covariance with sensor location uncertainty. Then a two-step grid-search technique is proposed to optimize the log-likelihood ratio, combined with a gradient-based search method. At the update stage, the operational sensor states are updated with target state estimates and measurements in corresponding validation gates. The updated sensor states are used to establish a more accurate log-likelihood ratio in the next iteration, which leads to better parameter estimation. In addition, the effects of the sensor location uncertainty on the track acceptance test and the posterior Cramer-Rao lower bound are also analyzed. Simulation results show that the proposed method provides a computationally efficient way to improve track initialization performance in heavy clutter with sensor location uncertainty. The proposed work has applications in sonar tracking, geolocation, electronic support measures, and infrared search and tracking systems.