Robust Adaptive Beamforming Based on Particle Filter With Noise Unknown

Liu, Yu; Gu, Yujie; Shi, Zhiguo; Chen, Kang Sheng

doi:10.2528/pier09010302

Cited by 32 publications

(26 citation statements)

References 20 publications

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“…The idea of PHD/CPHD filter is to represent the targets and the uncertain measurements as random finite sets (RFSs) and use finite set statistics (FISST) to solve MTT problems under the Bayesian framework. Furthermore, similar to the particle filter (PF) [13][14][15][16][17][18][19], the particle PHD/CPHD mainly consists of three steps, namely, Generation of Particles (Prediction), Weight Computation (Update) and Resampling. Based on our analysis in this paper, the resampling is a major bottleneck to increase the real-time performance of the particle PHD filter, where, due to its feature of sequential processing, the main difficulty lies in reducing the processing latency.…”

Section: Introductionmentioning

confidence: 99%

Threshold-Based Resampling for High-Speed Particle PHD Filter

Shi¹,

Zheng²,

Bian³

et al. 2013

PIER

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Abstract-In recent years, particle probability hypothesis density (PHD) filtering has become an active research topic for multiple targets tracking in dense clutter scenarios. However, it is highly required to improve the real-time performance of particle PHD filtering because it is a kind of Monte Carlo approach and the computational complexity is very high. One of major difficulties to improve the realtime performance of particle PHD filtering lies in that, resampling, which is usually a sequential process, is crucial to the fully-parallel implementation of particle PHD filter. To overcome this difficulty, this paper presents a novel threshold-based resampling scheme for the particle PHD filter, in which the particle weights are all set below a proper threshold. This specific threshold is determined using a distinguishing feature of the particle PHD filters: The weight sum of all particles in weight update is equal to the total target number in the current iteration. This proposed resampling scheme allows the use of fully-pipelined architecture in the hardware design of particle PHD filter. Theoretical analysis indicates that the particle PHD filter employing the proposed resampling technique can reduce the time complexity by 33% around in a typical multi-target tracking (MTT) scenario compared with that employing the traditional systematic resampling technique, while simulation results show that it can maintain the almost same performance of estimation accuracy.

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Section: Introductionmentioning

confidence: 99%

Threshold-Based Resampling for High-Speed Particle PHD Filter

Shi¹,

Zheng²,

Bian³

et al. 2013

PIER

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although the DL is effective, the main drawback is that choosing the required loading factor is not an easy task. For example, in [10], the loading factor is found by using particle filters. The particle who has the highest posterior probability is chosen as the optimal loading factor.…”

Section: Introductionmentioning

confidence: 99%

“…Diagonal loading (DL) is one of the widely used techniques to improve robustness of the SMI against the errors [4,[8][9][10][11][12], where a scaled identity matrix is added to the sample correlation matrix. Although the DL is effective, the main drawback is that choosing the required loading factor is not an easy task.…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Beamforming Using a Fully Data-Dependent Loading Technique

Lee

2012

PIER B

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Abstract-This paper deals with adaptive array beamforming in the presence of errors due to steering vector mismatch and finite sample effect. Diagonal loading (DL) is one of the widely used techniques for dealing with these errors. However, the main drawback of DL techniques is that there is not an easy and reliable manner to determine the required loading factor. Recently, serval DL approaches proposed the so-called automatic scheme on computing the required loading factor. In this paper, we propose a fully data-dependent loading to overcome the difficulties. The novelty is that the proposed method does not require any additional sophisticated scheme to choose the required loading. The loading factor can be completely obtained from the received array data. Analytical formulas for evaluating the performance of the proposed method under random steering vector error are further derived. Simulation results are provided to confirm the validity of the proposed method and make comparison with the existing DL methods.

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“…Since radar tracking is of great importance to both civilian and military applications, multifarious new techniques are continuously applied in various radar tracking systems to improve the performance [1][2][3][4][5][6][7]. Among various radar tracking problems, multiple-target tracking (MTT) is an important topic with wide applications [8,9].…”

Section: Introductionmentioning

confidence: 99%

Simplified Particle PHD Filter for Multiple-Target Tracking: Algorithm and Architecture

Hong¹,

Wang²,

Shi

et al. 2011

PIER

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Abstract-In this paper, we propose a simplified particle probability hypothesis density (PHD) filter and its hardware implementation for multiple-target tracking (MTT). In the proposed algorithm, the update step of particle PHD filter is simplified and the time-varying number of measurements is arranged in combination series/parallel mode. This may result in fixed hardware architecture and therefore present a convenient hardware implementation of particle PHD filter. Simulation results indicate that for the MTT problems, this proposed simplified algorithm shows similar performance with the standard particle PHD filter but has faster processing rate. Experiment study shows that the proposed simplified algorithm can be efficiently implemented in hardware and can effectively solve the MTT problems.

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Robust Adaptive Beamforming Based on Particle Filter With Noise Unknown

Cited by 32 publications

References 20 publications

Threshold-Based Resampling for High-Speed Particle PHD Filter

Threshold-Based Resampling for High-Speed Particle PHD Filter

Robust Adaptive Beamforming Using a Fully Data-Dependent Loading Technique

Simplified Particle PHD Filter for Multiple-Target Tracking: Algorithm and Architecture

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