3D Localization and Tracking Methods for Multi-Platform Radar Networks

TL;DR

This paper reviews recent methods for 3D localization and multitarget tracking in multi-platform radar networks, proposing combined algorithms that enhance surveillance accuracy in complex scenarios.

Contribution

It introduces a novel integration of ARCE and SPA-based MTT algorithms for improved 3D localization and tracking in MPRNs.

Findings

Combined ARCE and SPA algorithms outperform baseline methods.

Simulation results demonstrate enhanced accuracy in 3D scenarios.

The approach effectively fuses localization and tracking for better surveillance.

Abstract

Multi-platform radar networks (MPRNs) are an emerging sensing technology due to their ability to provide improved surveillance capabilities over plain monostatic and bistatic systems. The design of advanced detection, localization, and tracking algorithms for efficient fusion of information obtained through multiple receivers has attracted much attention. However, considerable challenges remain. This article provides an overview on recent unconstrained and constrained localization techniques as well as multitarget tracking (MTT) algorithms tailored to MPRNs. In particular, two data-processing methods are illustrated and explored in detail, one aimed at accomplishing localization tasks the other tracking functions. As to the former, assuming a MPRN with one transmitter and multiple receivers, the angular and range constrained estimator (ARCE) algorithm capitalizes on the knowledge of the transmitter antenna beamwidth. As to the latter, the scalable sum-product algorithm (SPA) based MTT technique is presented. Additionally, a solution to combine ARCE and SPA-based MTT is investigated in order to boost the accuracy of the overall surveillance system. Simulated experiments show the benefit of the combined algorithm in comparison with the conventional baseline SPA-based MTT and the stand-alone ARCE localization, in a 3D sensing scenario.