This paper considers the problem of tracking a timevarying number of non-cooperative emitters based on singlechannel 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 multiple emitters 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 posterior of multiple emitters. Moreover, an efficient implementation strategy is devised for the proposed algorithm and its performance is assessed via simulations.

This paper proposes a method for tracking 3D extended target based on superquadric models.

This paper deals with multi-object fusion in the presence of misbehaving sensor nodes, for example due to faults or adversarial attacks. In this setting, the main challenge is to identify and then remove messages coming from corrupted nodes. To this end, a three-step method is proposed, where the first step consists of choosing a reference density among the received ones on the basis of a minimum upper median divergence criterion. Then, thresholding on the divergence from the reference density is performed to derive a subset of densities to be fused. Finally, the remaining densities are fused following either the generalized covariance intersection (GCI) or minimum information loss (MIL) criterion. The implementation of the proposed method for resilient fusion of label multi-Bernoulli densities is also discussed. Finally, the performance of the proposed approach is assessed via simulation experiments on a multi-target tracking case study