Unknown Target Detection for Cognitive Radar

ZOU Kun

doi:10.11999/JEIT170254

Volume 40 Issue 1

Jan. 2018

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ZOU Kun. Unknown Target Detection for Cognitive Radar[J]. Journal of Electronics & Information Technology, 2018, 40(1): 166-172. doi: 10.11999/JEIT170254

Citation:

ZOU Kun. Unknown Target Detection for Cognitive Radar[J]. Journal of Electronics & Information Technology, 2018, 40(1): 166-172. doi: 10.11999/JEIT170254

ZOU Kun. Unknown Target Detection for Cognitive Radar[J]. Journal of Electronics & Information Technology, 2018, 40(1): 166-172. doi: 10.11999/JEIT170254

Citation:

ZOU Kun. Unknown Target Detection for Cognitive Radar[J]. Journal of Electronics & Information Technology, 2018, 40(1): 166-172. doi: 10.11999/JEIT170254

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Unknown Target Detection for Cognitive Radar

doi: 10.11999/JEIT170254

ZOU Kun¹

Funds:

The National Natural Science Foundation of China (61571456), The Natural Science Foundation of Shaanxi Province (2016JM0644)

Received Date: 2017-03-29
Rev Recd Date: 2017-10-23
Publish Date: 2018-01-19

Abstract

Abstract

For the cognitive radar system, the system parameters can be optimized to match the current environment during the detection procedure, so as to improve the radar detection performance. For the unknown target detection problem, the useful signal component estimate and its covariant matrix are updated using the current returns, then the transmit waveform and receive filter are optimized based on the information of the useful signal. A closed loop processing is formed. The two optimization approaches are proposed. For the first approach, the transmit waveform design is based on the estimate of the useful signal, and the generalized match filter is used at the receiver. For the second approach, the estimate error of the useful signal is equivalent to the signal- dependent noise, and the transmit waveform and receive filter are jointly designed. The computer simulation result show that, the proposed methods are asymptotically equivalent, and they can improve the detection performance further, compared with the performance gain of the coherent accumulation.
- Cognitive radar,
- Target detection,
- Close loop process,
- Convex optimization

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References(15)

References

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