Joint Detection and Localization With an Efficient Censoring Method for Distributed Monopulse Radars Under Communication Constraints

This paper deals with the joint detection and localization (JDL) problem in distributed monopulse radars considering communication constraints. Initially, we devise a JDL algorithm with unlimited communication rates (JDL-UCR) to realize multi-target detection and localization by directly utilizing raw sampled signals at the fusion center (FC). Theoretically, joint processing of raw data yields superior detection and/or localization performance in distributed radars. However, the extensive data transmission bandwidth required between each local station and the FC is unfeasible, especially in wireless communication-constrained environments. To tackle this challenge, leveraging the theoretical groundwork of the JDL-UCR, we further propose a JDL algorithm with an efficient censoring method (JDL-ECM). In this algorithm, we formulate a regular censoring rule and derive both closed-form and adaptive censoring threshold expressions to effectively exclude the non-essential data of each local station. Additionally, we establish a three-step data updating operation to effectively retain the informative data including moving targets as much as possible. Consequently, the proposed JDL-ECM focuses on alleviating the data communication burden while maintaining superior detection and localization performance. Finally, we provide a comprehensive performance analysis, employing both simulation and real experimental data, to demonstrate the effectiveness of the proposed JDL-ECM algorithm. The analysis showcases its capability to achieve accurate detection and localization results under stringent communication constraints.