Unmanned aerial vehicles (UAVs) are used in different scenarios. The need for intelligent and accurate recognition of targets using UAV-based radars is increasingly urgent. As unique information of targets, micro-Doppler characteristics have become an important basis for target recognition. For three typical ground targets including pedestrians, wheeled vehicles and tracked vehicles, the micro-Doppler modulation of the UAV vibration on echo signals is analyzed firstly in this paper. Secondly, singular value decomposition (SVD) is adopted to suppress ground clutter and environmental noise. In order to decrease the computation and increase the contrast, all colored time-frequency diagrams of micro-Doppler signals are transferred into grayscale images. Thirdly, a multi-level target recognition method based on the transfer learning of deep convolutional neural networks (DCNNs) is proposed. The first-level recognition is designed to accurately distinguish pedestrians, wheeled vehicles and tracked vehicles, while the purpose of the second-level of recognition is to determine the three states of pedestrians including stepping, walking and jogging. At last, experiments are carried out using a designed Doppler radar to prove the effectiveness of proposed method. Results indicate that method in this paper not only avoids the complex feature extraction process, but also has higher accuracy and better robustness.

In order to achieve precise operations on specified targets from the unmanned aerial vehicles (UAVs), classifying ground targets correctly is especially important. Micro-Doppler effect which provides unique information of targets has been the basis for targets classification. Due to the effect of ground clutter, noise and complex signal modulation, enhancing micro-Doppler features of UAV-to-ground targets is necessary for accurate classification. This paper firstly establishes the models of UAV-to-ground targets including wheeled vehicles, tracked vehicles and pedestrians to analyze their micro-Doppler differences. Secondly, Principal Components Analysis (PCA) is utilized to remove the ground clutter. Compared with other algorithms, PCA can use a small amount of calculation to remove the ground clutter while retain nearby micro-Doppler signals. Then, micro-Doppler signals are sparsely represented based on Fourier basis. Orthogonal Matching Pursuit (OMP) is chosen to reconstruct micro-Doppler components and refine spectral lines after random projection. The three steps make up Compressed Sensing (CS) together. At last, non-linear transform of Doppler spectrum is conducted to further enhance the distinction of micro-Doppler spectral lines. Distinguishing micro-Doppler features are extracted from pre-processed micro-Doppler signals, which eventually contributes to the accurate targets classification. Comparison with other methods is also made to prove the robustness and anti-noise performance of proposed method.

The rapid development of flight control technology has made unmanned aerial vehicles (UAVs) widely used in high-precision strikes on the battlefield. The premise of this is to achieve accurate target recognition using UAV-based radars. Aiming at three typical ground targets, including pedestrians, wheeled vehicles, and tracked vehicles, the micro-Doppler modulation caused by the random vibration of the UAV is analyzed in this article for the first time. To improve the recognition accuracy under low signal-to-noise ratios (SNRs), Doppler signals are transformed into time–frequency images, and a deep convolutional denoising encoder (DCDE) is designed to effectively remove the noise without suppressing micro-Doppler characteristics. To avoid the complicated micro-Doppler feature extraction, deep residual learning that can reduce the burden of network training and gain higher learning efficiency compared with traditional deep convolutional neural networks (DCNNs) is adopted. Recognition results under various occasions using denoised micro-Doppler images and designed residual learning network indicate that the proposed method has higher precision and better robustness than current methods. Even when the SNR is only −16 dB, the overall recognition accuracy still exceeds 90%.

In recent years, blind deconvolution has been widely used in machinery condition monitoring and has been proven to be an effective method for impact detection. Considering that the ultrawideband (UWB) measuring system adopts Gaussian impulses as the transmitting signal, the improved deconvolution algorithm explained in this article extracts the UWB impulse series from the mixture of real signal, noise, and jamming signals. Then, the ability of entropy to characterize the impulse is investigated to improve the deficiency of traditional methods, which depend on kurtosis. Last but not least, using approximate negative entropy as the sparsity measure function of the deconvolution, the sensitivity of kurtosis is avoided, and a fast deconvolution algorithm is proposed on this basis. Experiments show that the negative entropy deconvolution can better recover the UWB impulse characteristics under strongly suppressive jamming. The root-mean-square error (RMSE) between the source and the recovered signal is less than 0.05 even when the average signal-to-jamming radio (ASJR) is below −30 dB. Moreover, iterations can be reduced to 1/3 or lower compared with the classic deconvolution algorithm.

The target detection problem for a triangle wave frequency modulation (TWFM) detection system in the presence of the noise amplitude modulation (NAM) interference is considered in this paper. The range measuring principle based on Chirp Z Transform (CZT) and velocity measuring principle based on Moving Target Detection (MTD) are respectively introduced. Exploiting the observation that the NAM interference often affects the TWFM detector's detection performance, we present an anti-interference method based on Fractional Fourier Transform (FrFT) to suppress the NAM jamming. Simulations show that the method proposed in this paper can excise the NAM interference effectively. Moreover, all proposed methods are realized by FPGA for the first time, and then the outside field experiments verify the feasibility of these techniques.