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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Study on Important Parameters of Tracking and Detecting RNWT Based on GMPHDA in Radar Networking
verfasst von : Hai-Long Ding, Wen-Bo Zhao, Guo-Chun Zhu
Erschienen in: Intelligent Computing Theories and Application
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Abstract
Gaussian mixture probability hypothesis density filter (PHDF), which is suitable for tracking RNWT (Targets in false alarm and clutter area such as stealth aircraft and UAV), has rigorous mathematical foundation. But the distribution covariance P and truncation threshold T of Gaussian elements in PHDF have not got reasonable calculation rules as yet, which bring bad influence to PHDF. Because the residual covariance S will be inverse calculated when it is involved in the gain calculation. If S is non-positive definite, it would lead to divergence calculation. To determine the P and T calculation rules we do probability statistics derivation. To solve the S calculation problem we do Cholesk and QR decomposition. The simulation compares demonstrate that PHDF in radar networking, using the proposed calculation rules of P, T and S, can precisely track RNWT multi-target, containing exist, birth and spawn targets, bring no extra calculation burden.