Biblio

By broadcasting false Global Navigation Satellite System (GNSS) signals, spoofing attacks will induce false position and time fixes within the victim receiver. In this article, we propose a Sparse Decomposition (SD)-based spoofing detection algorithm in the acquisition process, which can be applied in a single-antenna receiver. In the first step, we map the Fast Fourier transform (FFT)-based acquisition result in a two-dimensional matrix, which is a distorted autocorrelation function when the receiver is under spoof attack. In the second step, the distorted function is decomposed into two main autocorrelation function components of different code phases. The corresponding elements of the result vector of the SD are the code-phase values of the spoofed and the authentic signals. Numerical simulation results show that the proposed method can not only outcome spoofing detection result, but provide reliable estimations of the code phase delay of the spoof attack.

We propose a method for analysis of surveillance video by using low rank and sparse decomposition (LRSD) with low latency combined with compressive sensing to segment the background and extract moving objects in a surveillance video. Video is acquired by compressive measurements, and the measurements are used to analyze the video by a low rank and sparse decomposition of a matrix. The low rank component represents the background, and the sparse component, which is obtained in a tight wavelet frame domain, is used to identify moving objects in the surveillance video. An important feature of the proposed low latency method is that the decomposition can be performed with a small number of video frames, which reduces latency in the reconstruction and makes it possible for real time processing of surveillance video. The low latency method is both justified theoretically and validated experimentally.