Yu Ze

Effects of noise, sampling rate and signal sparsity for compressed sensing Synthetic Aperture Radar pulse compression

The traditional radar system needs large bandwidth, and the increasing number of channels brings huge amount of data. These data can easily overflow the memory of the sensor or the bandwidth of the signal which transferred to the ground station. In order to solve this problem, a new method of acquiring Synthetic Aperture Radar (SAR) raw data and compressing pulse which based on the theory of Compressive Sensing (CS) theory are presented. In this method, CS SAR imaging is affected by noise, sampling rate and the sparsity of signal. Furthermore, Donoho-Tanner phase transition diagram is applied to show the performance of CS pulse compression. Engineers can intuitively find the scene and the sampling rate which is suitable for using compressed sensing synthetic aperture radar pulse compression.

Azimuth ambiguity suppression for spaceborne SAR based on PRF micro-variation

Spaceborne synthetic aperture radar (SAR) has been proved highly significant for remote sensing of the Earth. The image quality is constrained by the system design and the image processing technology, azimuth ambiguity is one of the most important factors. We know the pulse repetition frequency (PRF) determine the sampling rate of the azimuth, but the spectrum of the azimuth is non-bandlimited, it is hard to find a suitable solution with the conventional approach. A new method is proposed based on PRF micro-variation to suppress the azimuth ambiguity for spaceborne SAR, which changes the sampling mode and makes the azimuth signal no longer uniform in frequency domain. At last it can improve the image quality and make the ambiguity energy disperses randomly. In this paper, the basic theory of azimuth signal sampling is reviewed, and the reason of azimuth ambiguity is also introduced in detail. Through analysis we discover the PRF micro-variation has a scope, finally, some simulation results demonstrate this method is effective and feasible.

A fast method for compressive sensing SAR imaging via nonlinear chirp scaling

In traditional SAR system, the sidelobes of strong scatting targets maybe cover the mainlobes of weak ones even if they are farther than the 3dB mainlobe width of impulse response. In order to reduce target sidelobes availably and improve the imaging quality, traditional processes use weighting method for sidelobes suppressing, however the mainlobes will be broadened. For CS recovery algorithm is a non-linear process, the results of CS-imaging are impact functions with no sidelobes. It is this specialty that disable range cell migration correction (RCMC) after CS pulse compression. Therefore, if CS-imaging is used in both range and azimuth at the case of large squint angle, the recovery vector of CS recovery algorithm must include the whole targets of the sense which needs quite large memory and powerful computing capability. There are some pagers associated CS with SAR imaging [1], [2], [3], [4], [5], but few ones discussed RCMC with large squint angle. In the process, we found that CS was also beneficial to enhance imaging quality, even if we took Nyquist samplings. Then nonlinear chirp scaling can be used to achieve RCMC with large squint angle. This page introduces a fast SAR imaging method based on compressive sensing, which using nonlinear chirp scaling to achieve RCMC for echo first, and then carrying CS compression out on both range and azimuth separately. Thus the length of recovery vector is cut down to the number of points in range or azimuth. This new method can reduce the requirement of computing memory significantly and make parallel computing easy to implement, while suppress sidelobes notably.

Parallel Distributed CFAR Detection Optimization Based on Genetic Algorithm with Interval Encoding

Aiming at parallel distributed constant false alarm rate (CFAR) detection employing K/N fusion rule, an optimization algorithm based on the genetic algorithm with interval encoding is proposed. N-1 local probabilities of false alarm are selected as optimization variables. And the encoding intervals for local false alarm probabilities are sequentially designed by the person-by-person optimization technique according to the constraints. By turning constrained optimization to unconstrained optimization, the problem of increasing iteration times due to the punishment technique frequently adopted in the genetic algorithm is thus overcome. Then this optimization scheme is applied to spacebased synthetic aperture radar (SAR) multi-angle collaborative detection, in which the nominal factor for each local detector is determined. The scheme is verified with simulations of cases including two, three and four independent SAR systems. Besides, detection performances with varying K and N are compared and analyzed.