Joint Detection and Localization with an Efficient Censoring Method for Distributed Monopulse Radars under Communication Constraints

Artikel i vetenskaplig tidskrift, 2025

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.