期刊
SIGNAL PROCESSING
卷 188, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.sigpro.2021.108174
关键词
Transmit waveform; Receive filter; Low sidelobe level; Dinkelbach algorithm; Majorization minimization
资金
- China Scholarship Council
- Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University
- Key Research and Development Program of Shaanxi [2021SF-166]
- Natural Science Foundation of China [61471295]
- Central University Funds [G2016KY0308, G2016KY0002, 17GH030144]
- Academy of Finland [299243, 319822]
This paper aims to jointly design transmit waveform and mismatched filter to achieve low sidelobe level in radar systems, using an Lp-norm metric for PC model and a new iterative method based on Dinkelbach's scheme and majorization minimization method. Numerical examples demonstrate that waveforms and filters designed by the proposed method produce lower PSL than existing techniques.
Low sidelobe level is required for pulse compression (PC) radar systems since it can reduce false alarm triggered by the high sidelobe and improve the ability to detect weak targets nearby strong scattering points. In most of the existing literature, sidelobe suppression is achieved by either transmit waveform design at the transmitter or mismatched filter optimization at the receiver. Joint design of waveform and filter is an approach with limited discussion. Thus, this paper aims to jointly design transmit waveform and mismatched filter to achieve low sidelobe level. We first formulate an Lp-norm metric for PC model which suits for either the integrated sidelobe level or peak sidelobe level (PSL) minimization (only with different initializations of p). Then we present a new iterative method to deal with the problem by using Dinkelbach's scheme and majorization minimization method. The proposed method is shown to be convergent and the computational complexity is also analyzed. Numerical examples demonstrate that waveforms and filters designed by using the proposed method produce lower PSL than the existing techniques. (C) 2021 Elsevier B.V. All rights reserved.
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