Tracking the Splitting and Combination of Group Target With $\delta$ -Generalized Labeled Multi-Bernoulli Filter

Low altitude small target group has the special motion characteristics of orderly as a whole and flexibly as individuals. The performances of traditional target tracking algorithms (such as centroid tracking, geometric centre tracking, etc.) decrease obviously when the group outline changes. Hence in this paper, a robust tracking algorithm for target group with dynamic outline is studied. Aiming at the two key issues of group division and equivalent tracking point estimation, a robust group division algorithm, which can maintain the stability of group division results under any reference point selection, is firstly introduced. Then, on this basis, a new optimal tracking point estimation method is proposed by introducing the group structure into the likelihood function. The simulation results show that this proposed method can effectively improve the robustness of low altitude small target group tracking in the scene of group merging and group splitting. INDEX TERMS Dynamic outlines, equivalent point estimation, group division, group tracking.

Section: ) Novel Methods Based On Mglementioning

confidence: 99%

“…These methods have achieved good performances in extended targets/formation group tracking [21]- [24], but they have two main problems when modelling group outlines of LSTG:…”

Section: Introductionmentioning

confidence: 99%

Robust Tracking for Small Target Groups With Dynamic Outlines

Zhou

Wang

2020

“…However, the nominalQ k and the nominalR k may be inaccurate. It can be seen that an inaccurateQ k yields an inaccurate predicted state covariance P k|k−1 through (8), an inaccurate P k|k−1 and an inaccurateR k induce an inaccurate K k through (12), and an inaccurate K k and an inaccurate P k|k−1 yield an inaccurate m k and an inaccurate P k through (10)- (12). As a result, an inaccurateQ k and an inaccurateR k may lead to erroneous estimate x k , which is determined by the Gaussian parameters m k and P k .…”

Section: B Inaccurate Process and Measurement Noise Covariances In Tmentioning

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.

“…Equation (16) provides the GLMB filtering density at time k . After the steps of new target adaptive generation and joint prediction and update, the GLMB filtering density at time 1 k  is…”

Section: B Prediction and Updatementioning

confidence: 99%

“…RFS is rigorous, elegant, and suitable for the multidimensional heterogeneous information fusion and semantic target information fusion scenarios to which traditional methods are difficult to apply. Led by Mahler and Vo-Vo, a group of outstanding scholars engaged in this work and successively proposed the implementation methods of probability hypothesis density (PHD) filter [11] and its cardinalized version, cardinalized PHD (CPHD) [12], the multi-target multi-Bernoulli (MeMBer) filter [8], [13], and the newly derived generalized labeled multi-Bernoulli (GLMB) filter [14]- [16] and its special case, the labeled multi-Bernoulli (LMB) filter [17]- [19]. Among them, GLMB filtering is based on the labeled RFS theory and strictly derived, which has better cardinality estimation accuracy and OSPA metric [20], [21] performances than PHD, CPHD, and MeMBer.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Information Fusion in Active Sonar via the Generalized Labeled Multi-Bernoulli Filter

Sun

Zhou

2020

Under the conditions of low detection probabilities, high clutter rates, low data-sampling rates, large measurement errors, and unknown prior information of the target position, multi-object tracking is difficult. This paper proposes a multidimensional information fusion method in active sonar via the generalized labeled multi-Bernoulli (GLMB) filter. After modeling the position measurement, radial velocity measurement and amplitude measurement of the target and clutter, new target births are adaptively generated by the measurement-driven model, predictions are made by the target motion model and updates are performed via multidimensional measurements with the generalized likelihood in the GLMB filter, which enables the measurement information of different dimensions to be elegantly applied to information fusion and significantly improves the filter performance. The contribution of specific dimension measurement to fusion can be evaluated by the Kullback-Leibler (KL) divergence. In the efficient implementation, we propose flat Gibbs sampling to realize multiple hypothesis optimization. Moreover, the filtering recursion is derived from Gaussian mixtures. Simulations are presented to verify the proposed method. INDEX TERMS Random finite sets, generalized labeled multi-Bernoulli filter, multidimensional information fusion, flat Gibbs sampling.