Abstract
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.
Highlights
In the complex and changeable marine environment, the acoustic scattering caused by an uneven seabed [1]-[2], rolling surfaces [3], fish in the water, and the noise of human activities, such as navigation, fishing, and underwater mining, lead to substantial clutter interference in submarine detection using active sonar
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]
We study the multidimensional information fusion and multi-object tracking in active sonar based on GLMB
Summary
In the complex and changeable marine environment, the acoustic scattering caused by an uneven seabed [1]-[2], rolling surfaces [3], fish in the water, and the noise of human activities, such as navigation, fishing, and underwater mining, lead to substantial clutter interference in submarine detection using active sonar. The multi-object tracking in active sonar faces four challenges: low detection probabilities, high clutter rates, low data rates and large measurement errors. [20], [21] performances than PHD, CPHD, and MeMBer. we study the multidimensional information fusion and multi-object tracking in active sonar based on GLMB. Under the conditions of low detection probabilities, high clutter rates, low data-sampling rates, and large measurement errors in active sonar, the performance of the GLMB filter is seriously degraded. In [24], [25], the echo amplitude information was used to propose the A-LMB filter, which was applied to radar target tracking and achieved better processing results than the LMB filter. Using the multidimensional measurement information, we propose a multidimensional information fusion method via the generalized labeled multi-Bernoulli (MDI-GLMB) filter.
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