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The Chaotic Phase-modulated Linear Frequency modulation Short-range Detection System(CPLF-SrDS) is a new type of composite detection system. This composited system makes good use of the short-term randomness and long-term determinism of chaos, it has strong precision determination ability and anti-recognition ability. In order to solved the problem of precise distance determination of this system under the emphasized amplitude modulation(AM) interference, this paper proposed a signal denoising method based on singular spectrum decomposition and reconstruction. The singular value distribution characteristics of CPLF-SrDS echo and AM interference has been studied, and the size of the window length during singular spectral decomposition is also been optimized. The method of the paper realized the denoising of the echo detected by different AM interference, and recovered the echo from the doping interference. From the simulation results, this method can better realize the recovery of the echo signals in the case of lower JSR, and can well realized the anti-interference of the CPLF-SrDS. [ABSTRACT FROM AUTHOR]

Abstract Classification of ground vehicles from the unmanned aerial vehicles (UAVs) is the basis for local precision strikes. In this paper, a method based on micro-Doppler effect which provides unique information of targets is proposed for ground vehicles classification. Firstly, models describing the air-to-ground relationship between ground vehicles and the UAV are built to derive mathematical expressions of radar echo signals. Secondly, Singular Value Decomposition (SVD) is utilized to analyze micro-Doppler components of the ground wheeled vehicle and the ground tracked vehicle respectively. Thirdly, according to classify requirements, specific signal components are restored by Singular Value Reconstruction (SVR). Seven micro-Doppler features are extracted from reconstructed signals. At last, these features are sent to Support Vector Machine (SVM) classifier. Compared with current related methods such as Empirical Mode Decomposition (EMD) and multi-level wavelet decomposition (MWD), experimental results in different cases illustrate the effectiveness of this method. Discrimination performance under various signal-to-noise ratios (SNRs) also proves the robustness of proposed method. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) which can work as a new platform have been applied widely in traffic detection. Accurate identification of ground vehicles from UAVs is the premise. As the unique information of targets, micro-Doppler effect is increasingly used to distinguish different targets. Focusing on typical rotating micro-Doppler, this paper firstly builds a model illustrating the relationship between a ground wheeled vehicle and an UAV. Mathematical expression of Doppler signal in the model is also derived. Then, according to micro-Doppler modulation in echo signal, an arithmetic estimating rotating micro-Doppler parameters based on smooth pseudo-Wegner distribution (SPWD) and image enhancement is proposed to deal with echo signals of the ground wheeled vehicle when the UAV are hovering and moving linearly respectively. By saving the ridge of the spectrogram obtained by SPWD as an image, approaches in image enhancement are utilized. At last, comparison between proposed method and traditional generalized Hough transform is given. Results show that micro-Doppler parameters estimated via proposed arithmetic has higher precision while needs less computation so that vehicles with rotating movement can be identified correctly. [ABSTRACT FROM AUTHOR]