Direct Multi-Emitter Tracking Based on the Generalized Labeled Multi-Bernoulli Filter

This paper considers the problem of tracking a time-varying number of non-cooperative emitters based on single-channel passive sensors, which is traditionally carried out via sequential processing, i.e., detection, measurement extraction and tracking. However, in such a framework, tracking performance is severely affected by the pre-processing steps especially in low signal-to-noise ratio (SNR) scenarios. To remedy this drawback, in this paper we develop an algorithm that directly performs tracking based on the received signals. The states of multi-emitter are represented by a labeled random finite set (LRFS), which is further modeled by a generalized labeled multi-Bernoulli (GLMB) density. A new likelihood model, which inherently exploits time difference of arrival (TDOA) of signals coming from different sensors, is proposed. Then, a new GLMB filter is derived to iteratively compute the multi-emitter posterior. Moreover, an efficient implementation strategy is devised for the proposed algorithm and its performance is assessed via simulations.