Distributed Joint Emitter Detection and Tracking With Parallel Consensus on Likelihood and Prediction

Abstract

Passive localisation and tracking of a radio emitter is of significant interest for both civilian and defence applications. Among the existing methods, received signal strength indicator (RSSI)-based localisation is widely used due to its low cost and simplicity. However, most RSSI-based techniques make simplifying assumptions, such as relying on basic path-loss models and presuming the emitter has already been detected, overlooking complex environmental effects like shadowing caused by obstacles. These limitations hinder the practical application of RSSI-based methods. In this paper, we propose an advanced RSSI-based framework for joint detection and tracking (JDT) of a radio emitter, integrating a more accurate propagation model that accounts for both path-loss and shadowing effects. The emitter is modelled as a Bernoulli random finite set (RFS) characterised by its existence probability (EP) and spatial probability density function (SPDF), addressing the challenges of emitter detection uncertainty. A key innovation of this paper is the development of a fully distributed JDT algorithm, which overcomes the computational and communication challenges associated with centralised tracking systems. The proposed algorithm leverages parallel consensus on likelihood and prediction (PCLP), allowing for scalable and efficient operation across sensor networks. Simulation results validate the proposed method's performance in real-time emitter tracking.