Monitoring small drones is significant for security requirements, but it is challenging because of small drones' low radar cross section (RCS) and hovering ability. In order to analyze the effect of radar waveform on the echo spectra and optimize the waveform design, an analytical expression of the rotor's temporal RCS is desired. Integral model, a widely used radar echo model of the rotor's temporal RCS, does not involve the electromagnetic scattering, thus it cannot be applied to all frequency bands. The method of moments (MoM) is a strictly numerical method based on the Maxwell's equations, but cannot obtain the analytical expression. Hence, this paper proposes a new method combing the integral model and MoM to model the rotor's temporal RCS in very high frequency (VHF) band. The linear frequency modulation (LFM) signal which has a high Doppler tolerance and a large gain bandwidth product is adopted in optimizing waveform parameters design. Based on the new method, the analytical expression of LFM echo is also derived. Moreover, the spectral spread over range and Doppler dimension caused by the rotating rotors is analyzed in detail. A criterion for optimizing the LFM waveform parameters for small drone detection is presented. Field experiments confirm the validity of the echo model and waveform parameters optimization criterion, and the full Acrylonitrile Butadiene Styrene small drone is successfully detected in VHF band. In addition, experiments are implemented to verify the applicability of the echo model and waveform parameters optimization method to other drones with different size.INDEX TERMS Small drone detection, electromagnetic scattering, method of moments (MoM), numerical simulation, linear frequency modulation (LFM) signal.
Compact high-frequency surface wave radar (HFSWR) plays a critical role in ship surveillance. Due to the wide antenna beam-width and low spatial gain, traditional constant false alarm rate (CFAR) detectors often induce a low detection probability. To solve this problem, a joint detection algorithm based on time-frequency (TF) analysis and the CFAR method is proposed in this paper. After the TF ridge extraction, CFAR detection is performed to test each sample of the ridges, and a binary integration is run to determine whether the entire TF ridge is of a ship. To verify the effectiveness of the proposed algorithm, experimental data collected by the Ocean State Monitoring and Analyzing Radar, type SD (OSMAR-SD) were used, with the ship records from an automatic identification system (AIS) used as ground truth data. The processing results showed that the joint TF-CFAR method outperformed CFAR in detecting non-stationary and weak signals and those within the first-order sea clutters, whereas CFAR outperformed TF-CFAR in identifying multiple signals with similar frequencies. Notably, the intersection of the matched detection sets by TF-CFAR and CFAR alone was not immense, which takes up approximately 68% of the matched number by CFAR and 25% of that by TF-CFAR; however, the number in the union detection sets was much (>30%) greater than the result of either method. Therefore, joint detection with TF-CFAR and CFAR can further increase the detection probability and greatly improve detection performance under complicated situations, such as non-stationarity, low signal-to-noise ratio (SNR), and within the first-order sea clutters.
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