Abstract
Passive bistatic radar detects targets by exploiting available local broadcasters and communication transmissions as illuminators, which are not designed for radar. The signal usually contains a time-varying structure, which may result in high-level range ambiguity sidelobes. Because the mismatched filter is effective in suppressing sidelobes, it can be used in a passive bistatic radar. However, due to the low signal-to-noise ratio in the reference signal, the sidelobe suppression performance seriously degrades in a passive bistatic radar system. To solve this problem, a novel mismatched filtering algorithm is developed using worst-case performance optimization. In this algorithm, the influence of the low energy level in the reference signal is taken into consideration, and a new cost function is built based on worst-case performance optimization. With this optimization, the mismatched filter weights can be obtained by minimizing the total energy of the ambiguity range sidelobes. Quantitative evaluations and simulation results demonstrate that the proposed algorithm can realize sidelobe suppression when there is a low-energy reference signal. Its effectiveness is proved using real data.
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Gang CHEN and Jun WANG designed the research. Gang CHEN processed the data and drafted the manuscript. Jun WANG helped organize the manuscript. Gang CHEN and Jun WANG revised and finalized the paper.
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Gang CHEN and Jun WANG declare that they have no conflict of interest.
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Project supported by the National Natural Science Foundation of China (No. 61401526), the 111 Project, China (No. B18039), and the National Key Laboratory of Science Foundation of Science and Technology on Space Microwave, China (No. 614241103030617)
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Chen, G., Wang, J. Robust mismatched filtering algorithm for passive bistatic radar using worst-case performance optimization. Front Inform Technol Electron Eng 21, 1074–1084 (2020). https://doi.org/10.1631/FITEE.1900150
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DOI: https://doi.org/10.1631/FITEE.1900150
Key words
- Passive bistatic radar
- Range sidelobes
- Low signal-to-noise ratio
- Mismatched filtering
- Worst-case performance optimization