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Linear And Nonlinear Programming

IEEE Workshop on Statistical Signal Processing (SSP'14), Cold Coast, Australia, June 29 - July 2, 2014

Synthetic aperture LADAR (SAL) allows high resolution imaging of distant objects. Basic SAL image processing is based on fast Fourier transform (FFT) techniques originally developed for use in radar. These techniques can amplify noise and limit resolution. More advanced reconstruction techniques have been proposed for synthetic aperture radar (SAR), but have not been adapted for SAL. In addition, both conventional SAL and advanced SAR algorithms reconstruct the complex-valued reflection coefficient instead of the real-valued reflectance which leads to speckled images. In this paper, we present a model-based iterative reconstruction (MBIR) algorithm designed specifically for SAL. Rather than estimating the reflection coefficient, we propose estimating its variance which is equal to the reflectance, a function that more closely resembles conventional optical images. A Bayesian framework is used to find the maximum a posteriori (MAP) estimate for the reflectance using a Q-Generalized Gaussian Markov random field (QGGMRF) prior model. The QGGMRF is able to model complex correlations between neighboring pixels which promotes a smooth and more natural looking image. The expectation-maximization (EM) algorithm is used to derive a surrogate for the MAP cost function. Finally, the proposed MBIR algorithm is tested on both simulated and experimental data. Results show significant and consistent improvements over existing reconstruction techniques in terms of image contrast, speckle reduction, autofocus, and low signal-to-noise ratio performance.

Synthetic Aperature LADAR: A Model-Based Approach

The performance of optically coherent imaging systems can be limited by measurement and speckle noise In this paper, we develop an image formation framework for computing the maximum a posteriori estimate of an object's reflectivity when imaged using coherent illumination and detection The proposed approach allows for the use of Gaussian denoising algorithms (GDAs), without modification, to mitigate the exponentially distributed and signal-dependent noise that occurs in coherent imaging Several GDAs are compared using both simulated and experimental data The proposed framework is shown to be robust to noise and significantly reduce reconstruction error compared to the standard inversion technique

Optically coherent image formation and denoising using a plug and play inversion framework.

Low-frequency long-range radars (LFLRRs) are assumed to lack the ability of extracting targets micro motion signature, due to their low and nonuniform track update rate, as well as the weak micro Doppler (m-D) owing to their large wave length. The recently proposed sinusoidal frequency modulated (SFM) Fourier transform can achieve a longer integral period gain, and consequently provides a new perspective for extracting weak m-D signature. However, its direct application is unavailable for LFLRRs, since their track update rate is very low and may not even be constant. This paper derives a new approach, named sinusoidal frequency modulation sparse recovery (SFMSR) for m-D analysis with LFLRR, by exploiting the micro motion spectrum sparsity in SFM signal space. SFMSR employs the Fourier modulation dictionary, which is determined only by the frequency in SFM signal space. Unlike other sparse representation-based methods whose dictionary is discretization of a 3-D space parameterized by the micro motion amplitude, frequency, and initial phase, the SFMSR reduces the m-D analysis to 1-D parameter optimization, and therefore can enhance the precision, stability, and computational efficiency simultaneously. The temporally correlated sparse Bayesian learning in SFM signal space is employed to decompose the signal and produce highly sparse solutions. The simulation results indicate that the proposed method outperforms the existing methods in accuracy and robustness, which can provide satisfactory performance even when the carrier frequency is 430 MHz and the average data rate is 0.5 Hz.

Sinusoidal Frequency Modulation Sparse Recovery for Precession Rate Estimation Using Low-Frequency Long-Range Radar

Based on the analysis of range-azimuth coupling in two dimensional frequency domain, a modified RD algorithm is proposed to account for the problem of SAR imaging with high squint angle. The measures of decoupling consist of second range compression (SRC), third order coupling compensation and range cell migration correction (RCMC). In addition, the type of RCMC is modified to eliminate target's movement in range direction which will cause the blur of distance. Simulation results verify the proposed algorithm and perfectly focused image is obtained with a squint angle as large as 80 degree.

High squint mode SAR imaging using modified RD algorithm

The ISAR (inverse synthetic aperture radar) imaging technology is an important tool for the ballistic missile midcourse target recognitions. Considering the rotationally symmetric targets, the sparse representation model of the ballistic midcourse targets with micro-motion is established. The sparse recovery algorithm named SBL (Sparse Bayesian Learning) is analyzed, which can provide a much sparser solution than the general sparse recovery algorithms. Based on the newly developed CS (Compress sensing) theory, the ISAR imaging of the ballistic missile is reconstructed by using only a few echoes. Simulation results verify the validity and superiority of the proposed method.

The ISAR imaging of ballistic midcourse targets based on Sparse Bayesian Learning

SAR image formation algorithms have implicit or explicit dependence on the mathematical model of the image observation process. Inaccuracies in the image model will bring phase error, which may cause various quality degradations in the reconstructed images, especially in the millimeter-wave or terahertz-waves radar. In this paper, we propose a sparse Bayesian approach for joint SAR imaging and phase error correction. It uses an iterative algorithm, which cycles through steps of target reconstruction and phase error estimation. A sparse Bayesian recovering method, which named the expansion-compression variance-component based method (ExCoV), is used for image reconstruction. The proposed method can significantly improve the quality of the reconstructed image, and the phase errors can be estimated accurately. Simulation results show the effectiveness of the proposed method.

A sparse Bayesian approach for joint SAR imaging and phase error correction

http://ieeexplore.ieee.org/iel7/5971804/6756006/06756014.pdf

Hybrid micromotion-scattering center model for synthetic aperture radar micromotion target imaging

The compressive sensing (CS) has been successfully used in inverse synthetic aperture radar (ISAR) imaging. Since the sparse reconstruction based on l 1 norm is sensitive to the regularized factor and makes it inconvenient to be used in practice, the sparse Bayesian learning (SBL) is considered in this situation, which retains a preferable property of the l 0 norm and has no user parameter. In this paper, we proposed the ISAR imaging approach based on enhanced sparse Bayesian learning (ESBL), the ESBL determines the signal support by applying the statistical thresholding to accept the active components of the model, which is integrated into the SBL and improving its accuracy and reducing convergence time. The simulation result show that an accurate reconstruction of high-resolution ISAR images can be obtained than most its counterparts in low SNR, and resulting in lower mean square error (MSE).

Wuge Su

Papers

High squint mode SAR imaging using modified RD algorithm

The ISAR imaging of ballistic midcourse targets based on Sparse Bayesian Learning

A sparse Bayesian approach for joint SAR imaging and phase error correction

Hybrid micromotion-scattering center model for synthetic aperture radar micromotion target imaging

High resolution of ISAR imaging based on enhanced sparse Bayesian learning