Title :
Target tracking with glint noise using an RBF neural network
Author :
Yan, WeI ; Zhu, Zhaoda
Author_Institution :
Dept. of Electron. Eng., Nanjing Univ. of Aeronaut. & Astronaut., China
Abstract :
In this paper, the problem of target tracking with glint noise is considered. We apply a radial basis function (RBF) neural network to evaluate the nonlinear score function, which is used as the correction term in the state estimation of robust Kalman filter. Simulation results are presented to demonstrate the performance of the evaluation of the score function
Keywords :
adaptive Kalman filters; adaptive estimation; adaptive signal detection; convolution; feedforward neural nets; radar cross-sections; radar detection; radar interference; radar signal processing; radar tracking; recursive estimation; state estimation; target tracking; unsupervised learning; Gaussian density function; adaptive estimation; convolution; glint noise; kernel functions; maneuvering target dynamics; non-Gaussian noise; nonlinear score function; radial basis function neural network; recursive algorithm; robust Kalman filter; simulation; state estimation; system model; target tracking; time series samples; unsupervised learning; Convolution; Degradation; Differential equations; Linear systems; Neural networks; Noise robustness; Probability density function; State estimation; Target tracking; Vectors;
Conference_Titel :
Aerospace and Electronics Conference, 1996. NAECON 1996., Proceedings of the IEEE 1996 National
Conference_Location :
Dayton, OH
Print_ISBN :
0-7803-3306-3
DOI :
10.1109/NAECON.1996.517649
