Joint Detection and DOA Tracking with a Bernoulli Filter Based on Information Theoretic Criteria

Zhang, Guangpu; Zheng, Ce; Sun, Sibo; Liang, Guolong; Zhang, Yifeng

doi:10.3390/s18103473

Cited by 4 publications

(2 citation statements)

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“…In this section, we describe the particle filter implementation of the UT-MeMBer algorithm. From [22], if the multi-target probability density parameter set at time k−1 is {(rk−1|k−1j,sk−1|k−1j)}j=1Jk−1, then the spatial posterior probability density at time k−1 and can be expressed as:sk−1|k−1(j)(x)=true∑i=1Nk−1ωk−1(i,j)xk-1(i,j),j=1,…,Jk−1 where sk−1|k−1j is the spatial posterior probability density, which can be approximated as the weighted particle set {…”

Section: Ut-member Doa Particle Filter Tracking Algorithmmentioning

confidence: 99%

“…A tutorial on Bernoulli filters is introduced in [21]. A track-before-detect (TBD) Bernoulli filter based on RFS is proposed for DOA tracking in single dynamic system in [22], but it cannot solve the DOA tracking in multiple target dynamic system. The Multi-target Multi-Bernoulli (MeMBer) filtering [23] is a filter developed under the RFS framework.…”

Section: Introductionmentioning

confidence: 99%

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DOA Tracking Based on Unscented Transform Multi-Bernoulli Filter in Impulse Noise Environment

Zhao

Dong

et al. 2019

Sensors

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Aiming at the problem of multiple-source direction of arrival (DOA) tracking in impulse noise, this paper models the impulse noise by using the symmetric α stable (SαS) distribution, and proposes a DOA tracking algorithm based on the Unscented Transform Multi-target Multi-Bernoulli (UT-MeMBer) filter framework. In order to overcome the problem of particle decay in particle filtering, UT is adopted to select a group of sigma points with different weights to make them close to the posterior probability density of the state. Since the α stable distribution does not have finite covariance, the Fractional Lower Order Moment (FLOM) matrix of the received array data is employed to replace the covariance matrix to formulate a MUSIC spatial spectra in the MeMBer filter. Further exponential weighting is used to enhance the weight of particles at high likelihood area and obtain a better resampling. Compared with the PASTD algorithm and the MeMBer DOA filter algorithm, the simulation results show that the proposed algorithm can more effectively solve the issue that the DOA and number of target are time-varying. In addition, we present the Sequential Monte Carlo (SMC) implementation of the UT-MeMBer algorithm.

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