Adaptive MIMO radar detection algorithm in a spatially correlated clutter environment

被引:0
作者
Chen, Wei-Jen [1 ]
Narayanan, Ram M. [1 ]
机构
[1] Penn State Univ, Dept Elect Engn, University Pk, PA 16802 USA
来源
WIRELESS SENSING AND PROCESSING III | 2008年 / 6980卷
关键词
multiple-input multiple-output (MIMO) radar; spatially correlated clutter; K-distribution; adaptive detection algorithm;
D O I
10.1117/12.777395
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
In recent years, multi-input and multi-output (MIMO) radar systems have captured the attention of many researchers. At the very beginning, these works focused on increasing the signal-to-noise power ratio (SNR) by transmitting coherent signals. In 2004, a new concept called spatial diversity was introduced for MIMO radar, which achieved the same objective using widely separated transmitters and/or receivers and independent transmitted signals. This technology dramatically enhances the detection probability and accuracy in estimating direction of arrival (DOA) by efficiently constraining target scintillations and collecting more information carried in distinguishable signals. Moreover, since the transmitted signals are independent in MIMO radar, considerable mechanisms such as beamforming technologies and coherent processing, can be developed and applied to improve its performance. However, MIMO radar detection performance in a spatially correlated clutter environment does not get adequate attention that it deserves. Therefore, in this paper, received signals considered include reflections from the target, K-distributed clutter, and thermal noise. Moreover, beamforming technology and coherent processing are applied to estimate the reflectivity of and distinguish between target and clutter reflections. As a result, when echoes from target and clutter are unresolvable, the detection problem can be formulated as a two hypotheses test. According to the Bayesian approach, we develop the ratio test for this situation. In addition, we observe that if highly correlated information is utilized by adaptively modifying clutter local mean power probability density function (PDF), the uncertainty of clutter local mean power decreases and detection performance can be further improved. Last, we also compare the power based detection algorithm with the ratio test. Even though the power based detection algorithm has advantage in simple computation, its comparable performance is achieved only when number of transmitters is large.
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页数:12
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