597 research outputs found
Multipath Separation-Direction of Arrival (MS-DOA) with Genetic Search Algorithm for HF channels
Cataloged from PDF version of article.Direction-of-Arrival (DOA) defines the estimation of arrival angles of an electromagnetic wave impinging on a set of sensors. For
dispersive and time-varying HF channels, where the propagating wave also suffers from the multipath phenomena, estimation of
DOA is a very challenging problem. Multipath Separation-Direction of Arrival (MS-DOA), that is developed to estimate both the arrival
angles in elevation and azimuth and the incoming signals at the output of the reference antenna with very high accuracy, proves itself as a
strong alternative in DOA estimation for HF channels. In MS-DOA, a linear system of equations is formed using the coefficients of the
basis vector for the array output vector, the incoming signal vector and the array manifold. The angles of arrival in elevation and azimuth
are obtained as the maximizers of the sum of the magnitude squares of the projection of the signal coefficients on the column space of the
array manifold. In this study, alternative Genetic Search Algorithms (GA) for the maximizers of the projection sum are investigated
using simulated and experimental ionospheric channel data. It is observed that GA combined with MS-DOA is a powerful alternative
in online DOA estimation and can be further developed according to the channel characteristics of a specific HF link.
(C) 2009 COSPAR. Published by Elsevier Ltd. All rights reserve
SAR image reconstruction and autofocus by compressed sensing
Cataloged from PDF version of article.A new SAR signal processing technique based on compressed sensing is proposed for autofocused image reconstruction on subsampled raw SAR data. It is shown that, if the residual phase error after INS/GPS corrected platform motion is captured in the signal model, then the optimal autofocused image formation can be formulated as a sparse reconstruction problem. To further improve image quality, the total variation of the reconstruction is used as a penalty term. In order to demonstrate the performance of the proposed technique in wide-band SAR systems, the measurements used in the reconstruction are formed by a new under-sampling pattern that can be easily implemented in practice by using slower rate A/D converters. Under a variety of metrics for the reconstruction quality, it is demonstrated that, even at high under-sampling ratios, the proposed technique provides reconstruction quality comparable to that obtained by the classical techniques which require full-band data without any under-sampling. (C) 2012 Elsevier Inc. All rights reserved
Cross-ambiquity function domain multipath channel parameter estimation
Cataloged from PDF version of article.A new array signal processing technique is proposed to estimate the direction-of-arrivals (DOAs), time delays, Doppler shifts and amplitudes of a known waveform impinging on an array of antennas from several distinct paths. The proposed technique detects the presence of multipath components by integrating cross-ambiguity functions (CAF) of array outputs, hence, it is called as the cross-ambiguity function direction finding (CAF-DF). The performance of the CAF-DF technique is compared with the space-alternating generalized expectation-maximization (SAGE) and the multiple signal classification (MUSIC) techniques as well as the Cramer-Rao lower bound. The CAF-DF technique is found to be superior in terms of root-mean-squared-error (rMSE) to the SAGE and MUSIC techniques. (C) 2011 Elsevier Inc. All rights reserved
A robust compressive sensing based technique for reconstruction of sparse radar scenes
Cataloged from PDF version of article.Pulse-Doppler radar has been successfully applied to surveillance and tracking of both moving and
stationary targets. For efficient processing of radar returns, delay–Doppler plane is discretized and FFT
techniques are employed to compute matched filter output on this discrete grid. However, for targets
whose delay–Doppler values do not coincide with the computation grid, the detection performance
degrades considerably. Especially for detecting strong and closely spaced targets this causes miss
detections and false alarms. This phenomena is known as the off-grid problem. Although compressive
sensing based techniques provide sparse and high resolution results at sub-Nyquist sampling rates,
straightforward application of these techniques is significantly more sensitive to the off-grid problem.
Here a novel parameter perturbation based sparse reconstruction technique is proposed for robust delay–
Doppler radar processing even under the off-grid case. Although the perturbation idea is general and can
be implemented in association with other greedy techniques, presently it is used within an orthogonal
matching pursuit (OMP) framework. In the proposed technique, the selected dictionary parameters are
perturbed towards directions to decrease the orthogonal residual norm. The obtained results show that
accurate and sparse reconstructions can be obtained for off-grid multi target cases. A new performance
metric based on Kullback–Leibler Divergence (KLD) is proposed to better characterize the error between
actual and reconstructed parameter spaces. Increased performance with lower reconstruction errors are
obtained for all the tested performance criteria for the proposed technique compared to conventional
OMP and 1 minimization techniques.
© 2013 Elsevier Inc. All rights reserve
Perturbed Orthogonal Matching Pursuit
Cataloged from PDF version of article.Compressive Sensing theory details how a sparsely
represented signal in a known basis can be reconstructed with
an underdetermined linear measurement model. However, in reality
there is a mismatch between the assumed and the actual
bases due to factors such as discretization of the parameter
space defining basis components, sampling jitter in A/D conversion,
and model errors. Due to this mismatch, a signal may
not be sparse in the assumed basis, which causes significant performance
degradation in sparse reconstruction algorithms. To
eliminate the mismatch problem, this paper presents a novel
perturbed orthogonal matching pursuit (POMP) algorithm that
performs controlled perturbation of selected support vectors to
decrease the orthogonal residual at each iteration. Based on detailed
mathematical analysis, conditions for successful reconstruction
are derived. Simulations show that robust results with much
smaller reconstruction errors in the case of perturbed bases can
be obtained as compared to standard sparse reconstruction techniques
Sparse ground-penetrating radar imaging method for off-the-grid target problem
Cataloged from PDF version of article.Spatial sparsity of the target space in subsurface or through-the-wall imaging applications has been successfully used within the compressive-sensing framework to decrease the data acquisition load in practical systems, while also generating high-resolution images. The developed techniques in this area mainly discretize the continuous target space into grid points and generate a dictionary of model data that is used in image-reconstructing optimization problems. However, for targets that do not coincide with the computation grid, imaging performance degrades considerably. This phenomenon is known as the off-grid problem. This paper presents a novel sparse ground-penetrating radar imaging method that is robust for off-grid targets. The proposed technique is an iterative orthogonal matching pursuit-based method that uses gradient-based steepest ascent-type iterations to locate the off-grid target. Simulations show that robust results with much smaller reconstruction errors are obtained for multiple off-grid targets compared to standard sparse reconstruction techniques. (c) 2013 SPIE and IS&
Time-frequency analysis of signals using support adaptive Hermite-Gaussian expansions
Cataloged from PDF version of article.Since Hermite–Gaussian (HG) functions provide an orthonormal basis with the most compact time–
frequency supports (TFSs), they are ideally suited for time–frequency component analysis of finite energy
signals. For a signal component whose TFS tightly fits into a circular region around the origin, HG
function expansion provides optimal representation by using the fewest number of basis functions.
However, for signal components whose TFS has a non-circular shape away from the origin, straight
forward expansions require excessively large number of HGs resulting to noise fitting. Furthermore, for
closely spaced signal components with non-circular TFSs, direct application of HG expansion cannot
provide reliable estimates to the individual signal components. To alleviate these problems, by using
expectation maximization (EM) iterations, we propose a fully automated pre-processing technique which
identifies and transforms TFSs of individual signal components to circular regions centered around the
origin so that reliable signal estimates for the signal components can be obtained. The HG expansion
order for each signal component is determined by using a robust estimation technique. Then, the
estimated components are post-processed to transform their TFSs back to their original positions.
The proposed technique can be used to analyze signals with overlapping components as long as the
overlapped supports of the components have an area smaller than the effective support of a Gaussian
atom which has the smallest time-bandwidth product. It is shown that if the area of the overlap
region is larger than this threshold, the components cannot be uniquely identified. Obtained results on
the synthetic and real signals demonstrate the effectiveness for the proposed time–frequency analysis
technique under severe noise cases.
© 2012 Elsevier Inc. All rights reserved
Multipath channel identification by using global optimization in ambiguity function domain
Cataloged from PDF version of article.A new transform domain array signal processing technique is proposed for identification of multipath communication channels. The received array element outputs are transformed to delay-Doppler domain by using the cross-ambiguity function (CAF) for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components can be identified by using a simple amplitude thresholding in the delay-Doppler domain. Particle swarm optimization (PSO) can be used to identify parameters of the multipath components in each cluster. The performance of the proposed PSO-CAF technique is compared with the space alternating generalized expectation maximization (SAGE) technique and with a recently proposed PSO based technique at various SNR levels. Simulation results clearly quantify the superior performance of the PSO-CAF technique over the alternative techniques at all practically significant SNR levels. (C) 2011 Elsevier B.V. All rights reserved
Detection of sparse targets with structurally perturbed echo
Cataloged from PDF version of article.In this paper, a novel algorithm is proposed to achieve robust high resolution detection in sparse multipath channels. Currently used sparse reconstruction techniques are not immediately applicable in multipath channel modeling. Performance of standard compressed sensing formulations based on discretization of the multipath channel parameter space degrade significantly when the actual channel parameters deviate from the assumed discrete set of values. To alleviate this off-grid problem, we make use of the particle swarm optimization (PSO) to perturb each grid point that reside in each multipath component cluster. Orthogonal matching pursuit (OMP) is used to reconstruct sparse multipath components in a greedy fashion. Extensive simulation results quantify the performance gain and robustness obtained by the proposed algorithm against the off-grid problem faced in sparse multipath channels. © 2013 Elsevier Inc
Adaptive tracking of narrowband HF channel response
Estimation of channel impulse response constitutes a first step in computation of scattering function, channel equalization, elimination of multipath, and optimum detection and identification of transmitted signals through the HF channel. Due to spatial and temporal variations, HF channel impulse response has to be estimated adaptively. Based on developed state-space and measurement models, an adaptive Kalman filter is proposed to track the HF channel variation in time. Robust methods of initialization and adaptively adjusting the noise covariance in the system dynamics are proposed. In simulated examples under good, moderate and poor ionospheric conditions, it is observed that the adaptive Kalman filter based channel estimator provides reliable channel estimates and can track the variation of the channel in time with high accuracy
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