JOURNAL ARTICLE

Particle initialization effect of particle filter based track-before-detect algorithm

Abstract

The problem of detecting and tracking weak, stealthy or dim targets is a challenging one. The particle filter based track before detect (TBD) algorithm is appealing for its generality and potential for solving non-linear non-Gaussian problem. However, real-time implementation can be hardly realized by this class of algorithm. Current state-of-the-art research ubiquitously deal with computationally intensive problem by improving the algorithm itself. This paper presents a recent study on the effect of the initial velocity range and position range of the particles on the existing particle filter based TBD methods to decrease the computational time. Moreover, it points out that a suitable velocity range and position range will improve detection ability and tracking performance.

Keywords:
Initialization Particle filter Track-before-detect Tracking (education) Range (aeronautics) Computer science Algorithm Position (finance) Gaussian Track (disk drive) Auxiliary particle filter Filter (signal processing) Artificial intelligence Kalman filter Computer vision Ensemble Kalman filter Extended Kalman filter Engineering Physics

Metrics

3
Cited By
0.00
FWCI (Field Weighted Citation Impact)
17
Refs
0.09
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
Infrared Target Detection Methodologies
Physical Sciences →  Engineering →  Aerospace Engineering
Distributed Sensor Networks and Detection Algorithms
Physical Sciences →  Computer Science →  Computer Networks and Communications

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