The Labeled Multi-Bernoulli Filter for Multitarget Tracking With Glint Noise

Self Cite

In the estimation of distributed sensor networks, process noise and measurement noise may have outliers which have heavy-tailed characteristics. To solve this problem, this paper proposes a distributed consensus estimating method for sensor networks based on Student-t distribution. In the state space model, both process noise and measurement noise are modeled as Student-t distributions with heavytailed characteristics. First, for the assumption that the process noise and measurement noise have the same degree of freedom parameters, an exact distributed consensus Student-t filtering algorithm is derived. In practical applications, this assumption is often not true, and due to the increasing degrees of freedom, the method will quickly converge to the traditional distributed consensus Kalman filter. Therefore, it is necessary to relax the assumption of the same degree of freedom and keep the degree of freedom unchanged within a certain range. Based on this, an approximate distributed consensus Student-t filter algorithm is proposed. Simulation results verify the effectiveness of the proposed algorithm.

Section: ) Consensus For Student-t Distributionmentioning

confidence: 99%

“…According to the approximation, we can use the consensus steps (23) and (24) directly. After the the consensus steps, we should do some inverse operations to change the DOF back to ν, and specific steps are given in Sec.…”

Section: ) Consensus For Student-t Distributionmentioning

confidence: 99%

Distributed Consensus Student-t Filter for Sensor Networks With Heavy-Tailed Process and Measurement Noises

Wang¹,

Dong

Shen

et al. 2020

Self Cite

“…The joint posterior density can be truncated by using Markov Chain Monte Carlo to simulate a set of significant positive 1-1 vectors and using the Gibbs sampler to solve the ranked assignment problem. For more details, we refer the reader to [14].…”

Section: Truncationmentioning

confidence: 99%

“…In this case, the performance of conventional algorithms for the Gaussian measurement noise may be severely degraded due to the inconsistency of measurement noise statistics. Several studies applying the LMB filter to multi-object tracking under glint noise have been proposed [14], [15] to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

The Labeled Multi-Bernoulli Filter for Jump Markov Systems Under Glint Noise

Liu

Huang

2019

This paper proposes a novel labeled multi-Bernoulli (LMB) filter for jump Markov systems (JMS) to track the multiple maneuvering objects under glint noise. By modeling the glint noise as a Student's t-distribution and using the variational Bayesian method to acquire the approximate state distribution, we present an efficient implementation of the LMB filter with joint prediction and update for JMS. Simulation results illustrate that the proposed filter outperforms the existing filters for multi-object tracking under glint noise.INDEX TERMS Multi-object tracking, glint noise, labeled multi-Bernoulli filter, variational Bayesian method, jump Markov systems.

“…As a result, suitable conjugate prior distributions should be chosen to solve the problem. In the existing literature, the inverse gamma distribution has been chosen for MTT applications that only consider inaccurate measurement noise covariance, such as the robust PHD [25], [26], CBMeMBer [27] and LMB [28], [29] filters. For MTT applications that consider both inaccurate process and measurement noise covariances, the inverse Wishart (IW) distribution has been used in the adaptive Kalman filter (AKF) [30] and GLMB [31] filters.…”

Section: Introductionmentioning

confidence: 99%

Robust Poisson Multi-Bernoulli Mixture Filter With Inaccurate Process and Measurement Noise Covariances

2020

This paper proposes a robust Poisson multi-Bernoulli mixture (PMBM) filter with inaccurate process and measurement noise covariances. A derivation of the robust PMBM filter is provided for jointly estimating the kinematic state, the predicted state covariance, and the measurement noise covariance. By modeling the augmented state as a Gaussian inverse Wishart inverse Wishart (GIWIW) distribution, a computationally feasible implementation of the robust PMBM (GIWIW-PMBM) filter is given for linear Gaussian systems. To guarantee the conjugacy of the GIWIW distribution, the variational Bayesian (VB) approach is employed to approximate the posterior density. Finally, simulation results show that the GIWIW-PMBM filter has the best overall performance compared to existing state-of-the-art filters regarding computational cost and filtering performance. INDEX TERMS Robust Poisson multi-Bernoulli mixture filter, Gaussian inverse Wishart inverse Wishart, conjugate prior, variational Bayesian, inaccurate process and measurement noise covariances.