To address the challenges posed by structure identification, data transmission and information fusion in distributed group target tracking, this paper proposes a novel distributed structure identification and tracking algorithm for resolvable group targets with circular formations. The proposed algorithm combines the Joint Probabilistic Data Association algorithm and the K-medoids clustering method within each sensor to estimate all target states and partition them into different subgroups. Then, the circular formation of each subgroup is identified based on its geometric features. In addition, the Consensus on Information is introduced to fuse each local information after matching the states of group targets across multi-sensor networks. Simulation results demonstrate the effectiveness of the proposed algorithm.

—paper addresses communication bandwidth reduction and energy efficiency enhancement of a peer-to-peer sensor network for distributed target detection and tracking. A distributed Bernoulli filter with event-triggered communication is developed where each node broadcasts only local posteriors that achieve significant information gain. Specifically, for the cases where the Bernoulli density is no-target or single-target, the corresponding event-triggered strategies are constructed, respectively, in which the information discrepancy is measured via the Jeffreys divergence, and the triggering threshold is determined by the local information confidence coefficient. In addition, the presented method is combined with flooding protocol for internode communication, and weighted conservative fusion approaches are used to fuse the target existence probabilities and spatial distributions. Finally, simulation results demonstrate the effectiveness and superiority of the proposed approach.

A hybrid sampling strategy is considered in multimode sampling based particle filter to alleviate the degeneracy as one of the most typical problems in the particle filter. However, to achieve high accuracy, expensive computation cost is inevitable when generating the hybrid distribution. To overcome this problem, a novel framework of particle filter is proposed in this paper with an improved hybrid sampling strategy. The main novelty is that this framework can simplify the generation of the hybrid distribution and makes the selection of particles more reasonable, in which the likelihood of particle is used to select the particles and determine the weights of multiple basic proposal distributions. Two simulation examples are implemented to test performances of the proposed filter algorithm. The obtained results show that the proposed framework has several superior performances in comparison with the standard particle filter, the unscented particle filter and the multimode sampling based particle filter.

The Particle filter (PF) provides a general numerical tool to deal with the non-Gaussian filtering problems, but it has the particle depletion problem and so on. The unscented particle filter (UPF) can solve the problem of particle depletion, but it has the computationally intensive problem and so on. To overcome these problems, the unscented particle double layer filter (UPDLF) is proposed. The proposed algorithm uses the PF algorithm to replace the state transition density function in the UKF algorithm, and updates the weights of each deterministic sampling point based on the new measurements. Finally, the state estimation at each time is obtained. The numerical simulation with two examples shows that the proposed filter outperforms the PF algorithm and the UPF algorithm.