This paper addresses the challenges posed by imperfect detection and uncertain parameters, such as detection probability and noise covariances, in target tracking. We introduce an Adaptive Bernoulli Filter (ABF) capable of handling multiple sources of uncertainty simultaneously. The ABF employs a Gaussian Inverse Gamma Inverse Wishart Mixture (GIGIWM) to represent the spatial probability density function of the augmented state. Using a variational Bayesian approach, we derive a closed-form solution for the filter, providing estimates for target existence probability, kinematic and feature states, measurement noise covariance matrix, and predicted error covariance matrix. Additionally, we extend the ABF to incorporate prior knowledge through constrained distributions. In a distributed multi-sensor scenario, we propose a fusion approach to combine local posteriors, extending existing fusion techniques to handle local posteriors that depend on both global and local variables. Simulation results show the effectiveness and robustness of the proposed filter and distributed fusion framework.

The cubature Kalman filter (CKF) has poor performance in strongly nonlinear systems while the cubature particle filter has high computational complexity induced by stochastic sampling. To address these problems, a novel CKF named double-Layer cubature Kalman filter (DLCKF) is proposed. In the proposed DLCKF, the prior distribution is represented by a set of weighted deterministic sampling points, and each deterministic sampling point is updated by the inner CKF. Finally, the update mechanism of the outer CKF is used to obtain the state estimations. Simulation results show that the proposed algorithm has not only high estimation accuracy but also low computational complexity, compared with the state-of-the-art filtering algorithms.

This letter presents a novel distributed Bernoulli filter for tracking a target over a peer-to-peer sensor network. For the case where the Bernoulli random finite set is either empty or singleton, two types of fusion weights are designed based on the information of sensor capabilities and environmental factors. Further, novel weighted conservative fusion approaches including geometric average and arithmetic average are derived to fuse local Bernoulli densities. The Gaussian mixture implementations of the proposed algorithms are given, which are subsequently combined with distributed flooding protocol for distributed averaging. Simulation results demonstrate the outperformance of the proposed approaches compared to the state-of-the-art approaches

This paper addresses communication bandwidth reduction and energy efficiency enhancement of a peer-to-peer sensor network for distributed multitarget tracking under unknown clutter rate and detection profile. We propose an event-triggered distributed bootstrap cardinalized probability hypothesis density (CPHD) filter, where the local filter consists of an estimator that estimates the clutter and detection parameters, and a tracker that estimates the number and states of targets. Specifically, the proposed approach enforces each sensor to broadcast its local posterior density only when it has high estimation quality and significant information gain, wherein the former is determined by the outputs of the local estimator and tracker, while the latter is measured via the Jensen-Shannondivergence. Then, we develop a modified weighted arithmetic average fusion method to separately fuse the cardinality distributions and intensity functions, in which the fusion weights are constructed based on estimation qualities and triggering thresholds, which is incorporated into the distributed flooding scheme for distributed averaging. Additionally, the Gaussian mixture implementations of the proposed event-triggered distributed bootstrap CPHD filter are given, where only Gaussian components with high weights are applied, thus reducing the effect of false alarms and, consequently, achieving a partial consensus across the network. Finally, the effectiveness of the proposed algorithm is validated through simulation experiments.