Adaptive Sidelobe Reduction Research Articles

A Modified Adaptive Sidelobe Reduction Method for Through-the-Wall Radar Imaging

Sidelobe reduction is important to improve the quality of through-the-wall radar imagery, and adaptive sidelobe reduction (ASR) is the widely used method for that. However, similar to coherent factor (CF)-based methods, the major defect of ASR is to suppress the weak targets in the imaging scene. Moreover, in near-field radar applications, including through-the-wall imaging, ASR cannot achieve the global optimum. In most letters about ASR, the three crucial parameters, the filter order, the interpolation factor, and the constraint threshold, are selected empirically. This letter analyzes their relationships and value ranges and then proposes a modified ASR (M-ASR) method, which has a more reasonable strategy of parameter assignment. Both simulative and experimental results validate the feasibility of M-ASR in the near-field application and its improvement in weak target detection.

The Analyzation of SAR Image Adaptive Sidelobe Reduction Algorithms and Experiments

Synthetic aperture radar is an active coherent high resolution imaging system. The classical imaging algorithms process the SAR received data by matched filter in two-dimensional frequency domain, and get reconstructed SAR images. In classical imaging algorithms, Fast Fourier Transform is used broadly. Because of the limited frequency domain support of the SAR systems, the system impulse responses in the range and azimuth directions are sinc functions. And the SAR images have great dynamic domain. The ratio of the returned power between the bright and the weak targets reached 50 dB, even higher. The sidelobe of a bright point target can easily obscure and violate the mainlobe of weaker points. So SAR images often demands sidelobe reduction. The conventional sidelobe reduction applies the fixed weighting functions on the whole aperture data, and the conventional weighting approach have to compromise between the degree of sidelobe reduction and imaging resolution. In this paper discusses two adaptively sidelobe reduction algorithms, and compared their processing results with conventional sidelobe reduction methods by the simulated data and the IECAS L\|SAR system raw data.

SAR imaging via modern 2-D spectral estimation methods

This paper discusses the use of modern 2D spectral estimation algorithms for synthetic aperture radar (SAR) imaging. The motivation for applying power spectrum estimation methods to SAR imaging is to improve resolution, remove sidelobe artifacts, and reduce speckle compared to what is possible with conventional Fourier transform SAR imaging techniques. This paper makes two principal contributions to the field of adaptive SAR imaging. First, it is a comprehensive comparison of 2D spectral estimation methods for SAR imaging. It provides a synopsis of the algorithms available, discusses their relative merits for SAR imaging, and illustrates their performance on simulated and collected SAR imagery. Some of the algorithms presented or their derivations are new, as are some of the insights into or analyses of the algorithms. Second, this work develops multichannel variants of four related algorithms, minimum variance method (MVM), reduced-rank MVM (RRMVM), adaptive sidelobe reduction (ASR) and space variant apodization (SVA) to estimate both reflectivity intensity and interferometric height from polarimetric displaced-aperture interferometric data. All of these interferometric variants are new. In the interferometric contest, adaptive spectral estimation can improve the height estimates through a combination of adaptive nulling and averaging. Examples illustrate that MVM, ASR, and SVA offer significant advantages over Fourier methods for estimating both scattering intensity and interferometric height, and allow empirical comparison of the accuracies of Fourier, MVM, ASR, and SVA interferometric height estimates.

Sidelobe reduction via adaptive FIR filtering in SAR imagery

The paper describes a class of adaptive weighting functions that greatly reduce sidelobes, interference, and noise in Fourier transform data. By restricting the class of adaptive weighting functions, the adaptively weighted Fourier transform data can be represented as the convolution of the unweighted Fourier transform with a data adaptive FIR filter where one selects the FIR filter coefficients to maximize signal-to-interference ratio. This adaptive sidelobe reduction (ASR) procedure is analogous to Capon's (1969) minimum variance method (MVM) of adaptive spectral estimation. Unlike MVM, which provides a statistical estimate of the real-valued power spectral density, thereby estimating noise level and improving resolution, ASR provides a single-realization complex-valued estimate of the Fourier transform that suppresses sidelobes and noise. Further, the computational complexity of ASR is dramatically lower than that of MVM, which is critical for large multidimensional problems such as synthetic aperture radar (SAR) image formation. ASR performance characteristics can be varied through the choice of filter order, l(1)- or l(2)-norm filter vector constraints and a separable or nonseparable multidimensional implementation. The author compares simulated point scattering SAR imagery produced by the ASR, MVM, and MUSIC algorithms and illustrates ASR performance on three sets of collected SAR imagery.