NDLI: Monte Carlo-based filter for target tracking with feature measurement

Content Provider	IEEE Xplore Digital Library
Author	Angelova, D. Vassileva, B. Semerdjiev, Tz.
Copyright Year	2002
Description	Author affiliation: Central Lab. for Parallel Process., Bulgarian Acad. of Sci., Sofia, Bulgaria (Angelova, D.; Vassileva, B.; Semerdjiev, Tz.)
Abstract	Monte Carlo-based algorithm for tracking maneuvering target with a feature measurement is proposed in the paper Amplitude Information (AI) is used as a feature for state estimation of relatively low observable target (low Signal-to-Noise Ratio (SNR)) in the presence of high rate of false alarms. Rayleigh distributed noise amplitude and Swerling 3 type target model are assumed. The stochastic filter combines the Multiple Model (MM) approach with switching models for dealing with maneuvers and probabilistic association of features and measured kinematic data. The filter performance is analyzed by simulation. Results show that the suggested algorithm can track targets with SNR down to 10 dB with acceptable percentage of lost tracks, while the filter without AI works down to 13 dB. In the case of nonmaneuvering target these limits are at lower levels.
Starting Page	1499
Ending Page	1505
File Size	394235
Page Count	7
File Format	PDF
ISBN	0972184414
DOI	10.1109/ICIF.2002.1020994
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-07-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Int. Soc. of Information Fusion
Subject Keyword	Filters Target tracking Artificial intelligence Signal to noise ratio State estimation Noise level Stochastic resonance Kinematics Performance analysis Analytical models
Content Type	Text
Resource Type	Article

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