KEYWORDS: Time-frequency analysis, Fourier transforms, Nonlinear optics, Signal processing, Signal analyzers, Modulation, Frequency modulation, Wavelets, Signal detection
The synchrosqueezing transform(SST), a kind of reassignment method, aims to sharpen the time-frequency representation. In this paper, we consider synchrosqueezing transform based short-time fourier transform with instantaneous frequency rate of change to analyze nonlinear and nonstationary signal, called the adaptive synchrosqueezing transform (ASST). Compared with SST, the window width of ASST is adaptively adjusted with the instantaneous frequency rate estimation which is extracted at the signal ridge. The proposed method can generate a more energy concentrated TF representation for the non-stationary signals with fast-varying frequencies. Simulation results are provided to demonstrate the effectiveness of the proposed method.
KEYWORDS: Signal to noise ratio, Detection and tracking algorithms, Chemical elements, Statistical analysis, Signal processing, Radar signal processing, Radar, Lithium, Computer simulations
This paper presents a novel matrix completion algorithm, penalty decomposition method based augmented Lagrange multipliers (PD-ALM), to improve the performance of Direction Of Arrival (DOA) in sparse array. In PD-ALM algorithm, we apply the penalty decomposition method to solve low-rank matrix completion problem directly. Firstly, we reconstruct a low rank matrix using the special structure of received signals of uniform linear array (ULA). Then, PD-ALM algorithm is used to complete the received signals of the sparse array. Finally, we apply Multiple Signal Classification (MUSIC) algorithm to estimate direction of arrival. The numerical experiments are provided to validate the effectiveness of the proposed algorithm.
In this paper, a new approach for classifying targets captured by low-resolution Ground Surveillance Radar is proposed. Radar target is detected by the Doppler effect in radar echo signal. Those signals can be disposed in various domains to gain unique features of targets which can be used in radar target classification and enhance its effectiveness. The proposed approach consists of two steps, transforming original signals from 1D to 2D and constructing deep 2D convolution neural networks(CNN). In first step, Toeplitz matrix is made use of reconstructing Radar signal, to build a 2D plane of data. Reconstruction does not change the characteristic distribution of the signal but maps the signal from one to two dimensions in a rearranged method. Whilst,it makes possible of using 2D CNN to train the data. In second step, we take advantage of the “bottleneck” block to create 2D CNN, which guarantee the depth of CNN and ease the problem of vanishing/exploding gradients in back propagation process. method was tested on actual collected database including human and car, which achieve 99.7% accuracy on the original test set and 97% accuracy after adding noise.
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