In this paper, an effective solution is proposed for joint beam and power scheduling (JBPS) in the netted Colocated MIMO (C-MIMO) radar systems used for distributed multi-target tracking (MTT). At its core, the proposed solution includes a distributed fusion architecture that reduces the communication requirements while maintaining the overall robustness of the system. The distributed fusion architecture employs the covariance intersection (CI) fusion to address the unknown information correlations among radar nodes. Each C-MIMO radar node in the network can generate a time-varying number of beams with controllable transmitting power by waveform synthesis, thus is capable of accomplishing multiple tracking tasks simultaneously. To maximize the global MTT performance of the radar network, the proposed JBPS solution implements an online resource scheduling, regarding both the generated beams and the transmitted power of all radar nodes, based on the feedback of the MTT results. A scaled accuracy-based objective function is designed to quantify the global MTT performance while properly taking into account different target priorities on resource allocation. The Bayesian Cramer-Rao lower bound (BCRLB) for CI fusion rule is derived and utilized as the constituent of the objective function since it provides a lower bound on the accuracy of the target state estimates. The formulated JBPS problem is a non-convex optimization problem that when solved, determines two types of coupled parameters for the beam controlling and power allocation. Then, we propose a fast reward-based iterative descending approach to solve this problem effectively. Numerical results show that the proposed JBPS solution can deliver superior performance in terms of maximizing the overall MTT performance while possessing high flexibility on the resource allocation regarding different target priorities.

In this paper, we propose a distributed multiobject tracking algorithm through the use of multi-Bernoulli (MB) filter based on generalized covariance intersection (G-CI). Our analyses show that the G-CI fusion with two MB posterior distributions does not admit an accurate closed-form expression. To solve this problem, we first approximate the fused posterior as the unlabeled version of δ-generalized labeled MB distribution, referred to as generalized MB (GMB) distribution. Then, to allow the subsequent fusion with another MB posterior distribution, e.g., fusion with a third sensor node in the sensor network, or fusion in the feedback working mode, we further approximate the fused GMB posterior distribution as an MB distribution which matches its first-order statistical moment. The proposed fusion algorithm is implemented using sequential Monte Carlo technique and its performance is highlighted by numerical results.

Multi-frame track-before-detect (MF-TBD) is a model-based batch processing method. Assuming a particular model for the evolution of target states (e.g. a constant velocity model) within a batch processing time, MF-TBD integrates the target energy by taking advantage of the space-time correlations between a number of consecutive frames. Its performance is known to be heavily dependent on the accuracy of the motion model, and to substantially degrade when target maneuvers occur and motions do not follow the presumed model. We make two contributions towards addressing this problem. Firstly, we analyze and summarize the direct strategies to incorporate the effect of target maneuvers into MF-TBD. Our analysis shows that although these strategies are straightforward to implement, they either suffer from considerable performance loss or are computationally expensive. Motivated by the analysis, we propose a general measurement-directed (MD) strategy to address target maneuvers. It carries out an on-line study of target dynamics from the observations, and is capable of achieving both low computational complexity and high adaptability to different target maneuvers. Secondly, as the proposed MD strategy is a general framework without any particular model assumptions, we further derive its detailed implementation equations for linear motion and measurement models. Simulation results for various tracking scenarios are presented to demonstrate the effectiveness of the proposed MD strategy.

This paper considers the problem of the distributed fusion of multiobject posteriors in the labeled random finite set filtering framework, using a generalized covariance intersection (GCI) method. Our analysis shows that GCI fusion with labeled multiobject densities strongly relies on label consistencies between local multiobject posteriors at different sensor nodes, and hence suffers from a severe performance degradation when perfect label consistencies are violated. Moreover, we mathematically analyze this phenomenon from the perspective of the principle of minimum discrimination information and the so-called yes-object probability. Inspired by the analysis, we propose a novel and general solution for the distributed fusion with labeled multiobject densities that is robust to label inconsistencies between sensors. Specifically, the labeled multiobject posteriors are first marginalized to their unlabeled posteriors, which are then fused using the GCI method. We also introduce a principled method to construct the labeled fused density and produce tracks formally. Based on the developed theoretical framework, we present tractable algorithms for the family of generalized labeled multi-Bernoulli (GLMB) filters including $\delta$-GLMB, marginalized $\delta$-GLMB, and labeled multi-Bernoulli filters. The robustness and efficiency of the proposed distributed fusion algorithm are demonstrated in challenging tracking scenarios via numerical experiments.