Target Localization and Measurement Association in PCL-PET Hybrid Heterogeneous Network

This article discusses the problem of target localization and measurement association in a hybrid heterogeneous network consisting of passive coherent location (PCL) and passive emitter tracking (PET). The distinction from existing literature lies in this article's consideration of the general scenario where PCL receivers can only receive signals from specific transmitters. Two target localization algorithms are proposed based on semidefinite relaxation and Newton–Homotopy methods, corresponding to situations where the initial target position is unknown and known, respectively. Furthermore, this article introduces a measurement association algorithm for the PCL-PET hybrid heterogeneous network. It demonstrates that the test statistic follows a chi-square distribution, providing theoretical guidance for selecting thresholds in hypothesis decisions. Monte Carlo simulations indicate that the proposed localization algorithms achieve accuracy close to the Cramér–Rao lower bound, and the measurement association algorithm effectively discerns whether measurements originate from the same target. In addition, contour plots of localization accuracy illustrate the more significant potential of the PCL-PET hybrid heterogeneous network compared to single PCL and PET networks.