Sparse Step-Frequency MIMO Radar Design for Autonomous Driving
| Autor: | Shunqiao Sun, Nathan Seongheon Jeong, Lifan Xu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Radar cross-section
Computer science Frequency band MIMO Bandwidth (signal processing) Particle swarm optimization 020206 networking & telecommunications 02 engineering and technology Interference (wave propagation) Weighting law.invention law 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Radar Algorithm |
| Zdroj: | 2021 IEEE Radar Conference (RadarConf21). |
| DOI: | 10.1109/radarconf2147009.2021.9455274 |
| Popis: | To accommodate a high number of automotive radars operating at the same frequency band while avoiding mutual interference, we propose a sparse step-frequency waveform (SSFW) radar to synthesize a large effective bandwidth to achieve high range resolution profiles (HRRP). To mitigate high range sidelobes in the SSFW radars, we propose a joint sparse carriers selection and weighting approach, where the sparse carriers are first optimally selected via the particle swarm optimization (PSO) techniques, and then a weighting vector is optimized and applied such that the peak sidelobe level of range spectrum is minimized. As a result, targets with relatively small radar cross section are detectable without introducing high probability of false alarm. We extend the SSFW concept to multi-input multi-output (MIMO) radar by applying phase codes along slow-time to synthesize a large virtual array aperture. Numerical simulations are conducted to demonstrate the performance of the proposed SSFW MIMO radar. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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