Navigation satellite signals have the advantages of all-day, all-weather, and global coverage, and the use of navigation signals for the detection of moving targets has significant application prospects. However, the GNSS signal is very weak, and the signal power is greatly attenuated after being scattered by the target. In order to detect the echo signal, a long integration time is required. However, the movement of the target will cause the echo signal to produce unpredictable range migration and a Doppler frequency shift, which will weaken the cumulative effect of long-term integration. This paper proposes a weak signal processing method with amplitude and phase self-correction for moving target detection in the GNSS-S radar. First, the phase consistency of the echoes of a single GNSS satellite is realized by the block expansion compression and phase differential correction method to improve the coherent accumulation gain; then, multi-star joint accumulation is carried out after the signal amplitudes of multiple satellites are corrected by the improved keystone method, so as to obtain a stable echo signal track. This method can effectively improve the integral gain of the scattering signal of the moving target and realize target detection. The simulation results and field tests show that this method can effectively improve the SNR of the GNSS-S signal and can realize the detection of small moving targets such as cars with GNSS-S radar.

In order to reduce the complexity of the receiving system for wideband signals, a novel space-borne high-resolution synthetic aperture radar (SAR) system with the non-uniform hybrid sampling technology is proposed in this paper. The non-uniform hybrid sampling technology is firstly applied in the SAR imaging system for the detection of space targets. The non-uniform hybrid sampling technology is able to optimize the transmitted and receiving timing of SAR signals, reducing the requirement of the sampling rate of the analog-to-digital converter (ADC) for the reception of the wideband echoes from space targets. Meanwhile, according to the oversampling requirement of SAR imaging in the azimuth direction, a theoretical model of non-uniform hybrid sampling parameters and relative velocity between the SAR system and the space targets is established. A series of simulation experiments with different targets and different non-uniform hybrid parameters are performed, and an X-band SAR imaging experimental system is constructed to verify the effectiveness of the proposed non-uniform hybrid sampling technology. The experimental results show that the imaging resolution is better than 8 cm. When the non-uniform hybrid sampling interval is 15 us, the imaging quality is consistent with imaging results of the Nyquist real-time sampling, and it is easier to implement in the high-resolution imaging for space targets.

Remote sensing of moving targets, particularly pedestrians on the road, is crucial for advanced driver assistance systems. However, pedestrian detection using the radar system remains an ongoing challenge due to the radar cross section (RCS) of pedestrians being much smaller than that of the clutter. Existing radar systems and pedestrian detection methods predominantly rely on the single-polarization radar, while research on the fully polarized radar for pedestrian detection is relatively limited. In this paper, the L-band fully polarimetric radar system is developed for pedestrian detection, and based on the full polarized radar echo HH, HV, VH, and VV, a novel clutter suppression method is proposed, which integrates the optimal polarization states of antennas and optimal scattering characteristics of pedestrians. Moreover, the field experiment has been conducted, and the results demonstrate that the signal-to-clutter-plus-noise ratio (SCNR) of the total power signal of full-polarization echoes is higher than that of single-polarization echoes, and the proposed clutter suppression method is able to reduce the non-stationary clutter and the interference signal generated by the multipath effect, thereby improving the SCNR. Furthermore, the OTSU algorithm is employed to detect pedestrian targets using radar data before and after clutter suppression, and the results demonstrate that the proposed method yields superior detection performance. These findings justify the potential of fully polarimetric radar in enhancing pedestrian detection.

The GNSS-S radar utilizes the signals of a global navigation satellite system (GNSS) to carry out target detection. Due to the very low power of GNSS signals, long-term accumulation is needed to improve the gain of the echo signals. However, when it is used for moving object detection, the random movement of the target will cause residual Doppler frequency after the echoes are correlated and compressed through the direct signal. The residual Doppler frequency will cause two problems: on the one hand, the signal coherence will deteriorate, affecting the coherent accumulation gain; on the other hand, the amplitude of the signal after compression will decrease due to the sensitivity of GNSS signals to Doppler frequency. Therefore, how to increase the signal amplitude and eliminate the phase fluctuation caused by the Doppler frequency shift in the GNSS echoes of moving targets is an important issue for GNSS-S radar to detect moving targets. This paper proposes a dual-frequency GNSS echo enhancement method that uses the dual-frequency signals transmitted by the GNSS satellites to enhance and regularize the target echo. First, the phase relationship model of the GNSS dual-frequency echo is constructed, and the phase difference is made to the compressed dual-frequency echo signal to obtain the differential phase without fluctuation; then, the amplitudes of the dual-frequency echo signals are added together; and finally, a new signal with enhanced amplitude and consistent phase is constructed by using the dual-frequency additive amplitude and differential phase, and the long-term coherent accumulation of the signal is carried out, which can improve the processing gain of the weak echo signal of the moving target. The simulation and field experiments show that this method makes full use of the energy of the GNSS dual-frequency signal and eliminates the phase fluctuation in the echo signal of the moving target so that the compressed signal energy remains consistent in the slow-time dimension. After long-term coherent accumulation, the echo SNR was greatly improved, which enabled the detection of two high-speed cars by GNSS-S radar in the experiment.

Non-imaging radar can achieve the detection and localization of moving targets, but it faces difficulties in the classification of targets. Aiming for the application of moving target classification by low/medium resolution non-imaging radar, this paper proposes a novel feature extraction method, based on the statistical characteristics of echo amplitude with the L-band fully polarimetric radar. A feature plane, composed of the 3rd-order central moment (skewness) and 4th-order central moment (kurtosis) as the statistical characteristics of the echo envelope, is established. In addition, two types of moving targets, pedestrians and non-motorized vehicles, are able to be effectively distinguished according to whether the echo signals of different polarizations have overlapping areas in the feature plane. The L-band fully polarimetric radar has been developed and field experiments have been conducted. The experimental results verify that the kurtosis and skewness of the fully polarimetric echo amplitude of the pedestrians are very close, and there is an overlapping area in the feature plane, while the statistical characteristics of the fully polarimetric echo signal of non-motor vehicles are different, and there is no overlapping area in the feature plane. This proposed feature extraction method has the advantage of being simple and robust, compared to the traditional imaging approach.

The interference of sidelobe often causes different targets to exhibit similar features, diminishing fine-grained classification accuracy. This effect is particularly pronounced when the available data are limited. To address the aforementioned issues, a novel classification framework for sidelobe-affected SAR imagery is proposed. First, a method based on maximum median filtering is adopted to remove sidelobe by exploiting local grayscale differences between the target and sidelobe, constructing a high-quality SAR dataset. Second, a deep metric learning network is constructed for fine-grained classification. To enhance the classification performance of the network on limited samples, a feature extraction module integrating a lightweight attention mechanism is designed to extract discriminative features. Then, a hybrid loss function is proposed to strengthen intra-class correlation and inter-class separability. Experimental results based on the FUSAR-Ship dataset demonstrate that the method exhibits excellent sidelobe suppression performance. Furthermore, the proposed framework achieves an accuracy of 84.18% across five ship target classification categories, outperforming the existing methods, significantly enhancing the classification performance in the context of sidelobe interference and limited datasets.