JOURNAL ARTICLE

Multitarget Filtering With Unknown Clutter Density Using a Bootstrap GMCPHD Filter

Michael BeardBa-Tuong VoBa‐Ngu Vo

Year: 2013 Journal:   IEEE Signal Processing Letters Vol: 20 (4)Pages: 323-326   Publisher: Institute of Electrical and Electronics Engineers

Abstract

It was recently demonstrated that the Gaussian Mixture Cardinalised Probability Hypothesis Density (GMCPHD) filter can be used when the clutter density is unknown. Here we examine the performance of this filter, and as one would expect, it does not do as well as the conventional GMCPHD with matched clutter density. To improve the performance, we propose a bootstrap filtering scheme, and demonstrate by simulations on a bearings-only multitarget filtering scenario, that it is capable of performing almost as well as the matched GMCPHD filter.

Keywords:
Clutter Filter (signal processing) Probability density function Computer science Gaussian Artificial intelligence Matched filter Pattern recognition (psychology) Algorithm Mathematics Statistics Computer vision Radar Physics Telecommunications

Metrics

30
Cited By
4.24
FWCI (Field Weighted Citation Impact)
15
Refs
0.95
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Target Tracking and Data Fusion in Sensor Networks
Physical Sciences →  Computer Science →  Artificial Intelligence
Structural Health Monitoring Techniques
Physical Sciences →  Engineering →  Civil and Structural Engineering
Underwater Acoustics Research
Physical Sciences →  Earth and Planetary Sciences →  Oceanography

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