False Target Discrimination of Air Surveillance Radar Based on Track Velocity Modification and Frequency Spectrum Analysis
Wang Hua-bin Xu Mu Wang Cheng Zhao Qing-quan
(Radar Research Institute of Air Force Equipment Academy, Beijing 100085, China)
Abstract In this paper, the false target discrimination strategy of air surveillance radar is studied. Aiming to distinguish false target which can form certain track such as clutter residual false track or false track deception, a novel method based on track velocity modification and frequency spectrum analysis is proposed here, which can provide a great potential approach for solving the false target discrimination puzzle. The discrimination issue is concluded as a problem of bivariate test of hypothesis, and the likelihood ratio formula is deduced with the way to calculate the judge threshold also proposed. The validity of the novel method is verified by experimental results of real surveillance radar data.
Key words ： False target discrimination
Air surveillance radar
Frequency spectrum analysis
Received: 2013-05-08;
Published: 2013-08-12
Cite this article:
Wang Hua-bin,Xu Mu,Wang Cheng et al. False Target Discrimination of Air Surveillance Radar Based on Track Velocity Modification and Frequency Spectrum Analysis[J]. JOURNAL OF RADARS, 2013, 2(3): 382-388.
[1]
Si Qi, Wang Yu, Deng Yunkai, Li Ning, Zhang Heng. A Novel Cluster-Analysis Algorithm Based on MAP Framework for Multi-baseline InSAR Height Reconstruction
[2]
Tang Jiangwen, Deng Yunkai, Wang Robert, Zhao Shuo, Li Ning. High-resolution Slide Spotlight SAR Imaging by BP Algorithm and Heterogeneous Parallel Implementation
[3]
Yu Han, Shui Penglang, Yang Chunjiao, Shi Sainan. Whitening Degree Evaluation Method to Test Estimate Accuracy of Speckle Covariance Matrix
[4]
Yang Haifeng, Xie Wenchong, Wang Yongliang. Modeling and Analysis of Multiple AEWs Coordinated Detection Radar System with Different Transmit Waveform
[5]
Ding Hao, Wang Guoqing, Liu Ningbo, Guan Jian. Adaptive Detectors for Two Types of Subspace Targets in an Inverse Gamma Textured Background
[6]
Chen Xiaolong, Guan Jian, He You, Yu Xiaohan. High-resolution Sparse Representation and Its Applications in Radar Moving Target Detection
[7]
Xiong Dingding, Cui Guolong, Kong Lingjiang, Yang Xiaobo. Micro-motion Parameter Estimation in Non-Gaussian Noise via Mutual Correntropy
[8]
Chen Shuailin, Luo Feng, Zhang Linrang, Hu Chong, Chen Shichao. Weighted Adaptive Step Coherent Integration Method for Maneuvering Target Based on Dynamic Programming
[9]
Qian Lichang, Xu Jia, Hu Guoxu. Long-time Integration of a Multi-waveform for Weak Target Detection in Non-cooperative Passive Bistatic Radar
[10]
Wen Xuejiao, Qiu Xiaolan, You Hongjian, Lu Xiaojun. Focusing and Parameter Estimation of Fluctuating Targets in High Resolution Spaceborne SAR
[11]
Wen Gongjian, Zhu Guoqiang, Yin Hongcheng, Xing Mengdao, Yang Hu, Ma Conghui, Yan Hua, Ding Baiyuan, Zhong Jinrong. SAR ATR Based on 3D Parametric Electromagnetic Scattering Model
[12]
Hu Dingsheng, Qiu Xiaolan, Lei Bin, Xu Feng. Analysis of Crosstalk Impact on the Cloude-decomposition-based Scattering Characteristic
[13]
Ding Baiyuan, Wen Gongjian, Yu Liansheng, Ma Conghui. Matching of Attributed Scattering Center and Its Application to Synthetic Aperture Radar Automatic Target Recognition
[14]
Zhao Feixiang, Liu Yongxiang, Huo Kai. Radar Target Recognition Based on Stacked Denoising Sparse Autoencoder
[15]
Zhao Junxiang, Liang Xingdong, Li Yanlei. Change Detection in SAR CCD Based on the Likelihood Change Statistics
2013, Vol. 2 
