Generalized Labeled multi-Bernoulli Filter Research Articles

Multi-object tracking with an adaptive generalized labeled multi-Bernoulli filter

The challenges in multi-object tracking mainly stem from the random variations in the cardinality and states of objects during the tracking process. Further, the information on locations where the objects appear, their detection probabilities, and the statistics of the sensor’s false alarms significantly influence the tracking accuracy of the filter. However, this information is usually assumed to be known and provided by the users. In this paper, we propose an adaptive generalized labeled multi-Bernoulli (GLMB) filter which can track multiple objects without prior knowledge of the aforementioned information. Experimental results show that the performance of the proposed filter is comparable to an ideal GLMB filter supplied with correct information of the tracking scenarios.

Decomposed POMDP Optimization-Based Sensor Management for Multi-Target Tracking in Passive Multi-Sensor Systems

This paper presents an efficient information-theoretic sensor management method to maximize the performance of the passive multi-sensor system for multi-target tracking. We model the multi-target state as a generalized labeled multi-Bernoulli (GLMB) random finite set and formulate the dynamic sensor selection process as a partially observable Markov decision process (POMDP). The optimization objective is to maximum the information gain obtained from the observed data, which is measured by the Cauchy-Schwarz divergence. This is accomplished with two main technical innovations. The first is a tractable decomposed POMDP based sensor selection solution, in which the informative sensors are selected sequentially from the candidates based on the Cauchy-Schwarz divergence. The second is a novel dual-stage multi-sensor fusion strategy based on the iterated-corrector GLMB filter. Since the uncertainty of the passive multi-sensor system is generally large, the performance of the iterated-corrector scheme can be greatly influenced by the order of sensor updates. To fix this problem, the selected sensors are ranked in order of the Cauchy-Schwarz divergence obtained in sensor selection, followed by the iterated-corrector update. For the multi-sensor fusion, the effect of poor performance sensors is weakened and that of better performance sensors is enhanced. Simulation studies demonstrate the effectiveness and efficiency of the proposed method in challenging passive multi-target tracking scenarios.

Time-Varying DOA Tracking Algorithm Based on Generalized Labeled Multi-Bernoulli

Direction of arrival (DOA) tracking for multi-sources is a hot issue in array signal processing. To deal with the problem that sources DOA and their number are time-varying, a DOA tracking algorithm based on Generalized Labeled Multi-Bernoulli (GLMB) filter is proposed. Since the measurement value has only one set of data, the measurement association mapping (MAM) does not match, which leads to deviations in the GLMB filter update step. In this regard, we used the estimated sources number of the previous time step as the measurement number of the current time step, and successfully achieved MAM matching. Subsequently, particle filtering is used to approximate the posterior distribution of DOA, where the particle likelihood function can be calculated by the multi-signal classification (MUSIC) spatial spectrum function. In addition, by exponentially weighting the likelihood function, the number of particles in the high likelihood region of the posterior distribution increases, which makes the GLMB filter pruning and merging operations more effective. Simulation results show that the method is better than the probability hypothesis density DOA (PHD-DOA) algorithm in tracking state sources and estimating the number of targets.

Improved generalized labeled multi-Bernoulli filter for non-ellipsoidal extended targets or group targets tracking based on random sub-matrices

For non-ellipsoidal extended targets and group targets tracking, using an ellipsoid to approximate the target extension may not be accurate enough because of lacking shape and orientation information. In consideration of this, we model a non-ellipsoidal extended target or target group as a combination of multiple ellipsoidal sub-objects, each represented by a random matrix. Besides, in practical tracking scenes, the knowledge of detection probability is of critical importance, which, however, is prior unknown due to the fluctuation of target parameters. Thus based on the aforementioned random sub-matrices model, an improved generalized labeled multi-Bernoulli (GLMB) filter is presented for the tracking of non-ellipsoidal extended targets or group targets in the case of unknown detection probability. In addition, a cheaper Gibbs sampling based joint prediction and updating implementation of the proposed GLMB filter is given, within which the extended targets or group targets are modeled using Beta gamma Gaussian inverse Wishart (BGGIW) distributions, achieving the estimation to detection probability, measurement rate, and extension state of each sub-object and the kinematic state with the positions of all sub-objects and unified kinematics for each extended target or target group. The simulation results demonstrate the effectiveness of the proposed filter.

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

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.

GLMB Tracker with Partial Smoothing.

In this paper, we introduce a tracking algorithm based on labeled Random Finite Sets (RFS) and Rauch–Tung–Striebel (RTS) smoother via a Generalized Labeled Multi-Bernoulli (GLMB) multi-scan estimator to track multiple objects in a wide range of tracking scenarios. In the forward filtering stage, we use the GLMB filter to generate a set of labels and the association history between labels and the measurements. In the trajectory-estimating stage, we apply a track management strategy to eliminate tracks with short lifespan compared to a threshold value. Subsequently, we apply the information of trajectories captured from the forward GLMB filtering stage to carry out standard forward filtering and RTS backward smoothing on each estimated trajectory. For the experiment, we implement the tracker with standard GLMB filter, the hybrid track-before-detect (TBD) GLMB filter, and the GLMB filter with objects spawning. The results show improvements in tracking performance for all implemented trackers given negligible extra computational effort compared to standard GLMB filters.