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Low-radar cross section and capability to fly at low speeds make drones challenging targets for radar detection. In the presence of ground moving targets the frequency spectrum is also crowded, which makes the detection of the drones more difficult. Micro-Doppler effect is the main feature used to discriminate drone from other targets and clutter. Typically discrimination is performed after the detection of all the targets. Especially in target dense environments, such as cities, typical approach requires high processing power in order to detect and classify all of the targets. Coverage is also another problem of the typical monostatic radar-based drone detection in cities. Coverage of monostatic radar is easily blocked by buildings. In order to cope with these problems distributed multi-input multi-output (MIMO) continuous wave (CW) radar using MIMO cyclic spectral density (CSD) analysis (MCSD) method is proposed in this article. The MCSD method detects and classifies drones using the cyclic frequency information. In order to make the system simple and low cost, a network of CW radars is used and the localization is performed based on Doppler only localization approach. The simulations and experimental results demonstrate the proof of the concept. Performance and cost analyzes of the MCSD method are also elaborated in this article.
Yazc et al. (Tue,) studied this question.