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Performance Analysis of NNF-class Target Tracking Algorithms Applied to Benchmark Problem

Title
Performance Analysis of NNF-class Target Tracking Algorithms Applied to Benchmark Problem
Author
송택렬
Keywords
Performance analysis; Data structures; Boolean functions; Target tracking; Radar tracking; Filters; Neural networks; State estimation; Nearest neighbor searches; Phased arrays
Issue Date
2004-07
Publisher
IEEE
Citation
2004 5th Asian Control Conference, Page. 1602-1607
Abstract
In target tracking system, accurate target state estimation is required to accomplish efficient radar beam pointing control even in a cluttered environment. State estimation accuracy is degraded by false alarms due to clutters and jamming by intelligent targets. The NNF(Nearest Neighbor Filter) is widely used for tracking a target in a cluttered environment for its computational simplicity. One drawback of the NNF stems from the fact that the selected NN would be the false measurement. To improve the performance of the NNF, the PNNF is suggested to consider the probability of the event that the selected NN is the target-originated measurement. The PNNF-m is a new data association with the NN by incorporating the number of validated measurements into design of the PNNF. In this paper, tracking filter algorithms combined with nearest neighbor data association have been applied to the benchmark problem of target tracking algorithms for MPAR(Monopulse Phased Array Radar) to compare the tracking performance in the realistic situation where the spatial density of false measurements in the validation region is unknown. Also the performance comparison of the NNF-class filters and the PDAF is included.
URI
https://ieeexplore.ieee.org/abstract/document/1426880/keywords#keywordshttps://repository.hanyang.ac.kr/handle/20.500.11754/151191
ISBN
0-7803-8873-9
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
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