Browsing by Subject "Compressive Sensing"

Now showing 1 - 5 of 5

Open Access
A compressive measurement matrix design for detection and tracking of direction of arrival using sensor arrays
(2016-07) Özer, Berk
Direction of Arrival (DoA) estimation is extensively studied in the array signal processing with many applications areas including radar, sonar, medical diagnosis and radio astronomy. Since, in sparse target environments, Compressive Sensing (CS) provides comparable performance with the classical DoA estimation techniques by using fewer number of sensor outputs, there are a multitude of proposed techniques in the literature that focus on surveillance (detection) and tracking (estimation) of DoA in CS framework. Many of such works elaborate on recovery of compressed signal and employ random measurement matrices, such as Bernoulli or Gaussian matrices. Although random matrices satisfy Restricted Isometry Property (RIP) for reconstruction, the measurement matrices can be designed to provide improved performance in search sectors that they are designed for. In this thesis, a novel technique to design compressive measurement matrices is proposed in order to achieve enhanced DoA surveillance and tracking performance using sensor arrays. Measurement matrices are designed in order to minimize the Cramer-Rao Lower Bound (CRLB), which provides a lower bound for DoA estimation error. It is analytically shown that the proposed design technique attains the CRLB under mild conditions. Built upon the characteristics of proposed measurement design approach, a sequential surveillance technique using interference cancellation is introduced. A novel partitioning technique, which provides a greedy type solution to a minmax optimization problem, is also developed to ensure robust surveillance performance. In addition, an adaptive target tracking algorithm, which adaptively updates measurement matrices based on the available information of targets, is proposed. Via a comprehensive set of simulations, it is demonstrated that the proposed measurement design technique facilities significantly enhanced surveillance and tracking performance over the widely used random matrices in the compressive sensing literature.
Open Access
Off-grid sparse blind sensor calibration
(Institute of Electrical and Electronics Engineers, 2018) Çamlıca, S.; Yetik, I. Ş.; Arıkan, Orhan
Compressive Sensing (CS) based techniques generally discretize the signal space and assume that the signal is sparse and has support only on the discretized grid points. Due to continuous nature of the signals, representing the signal on a discretized grid results in the off-grid problem. Improper calibration is also another issue which can cause performance degradation. In this paper, a CS based blind calibration method is proposed for the multiple off-grid signal case. Proposed method is capable of estimating the off-grid signal parameters and correcting the gain and the phase errors simultaneously. Simulation analysis is performed and comments are drawn. Results show that the proposed method have superior performance in terms of the calculated metrics.
Open Access
Robust compressive sensing techniques
(2014) Teke, Oğuzhan
Compressive Sensing theory details how a sparsely represented signal in a known basis can be reconstructed from an underdetermined linear measurements. However, in reality there is a mismatch between the assumed and the actual dictionary 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 signifi- cant performance degradation in sparse reconstruction algorithms. To eliminate the mismatch problem, this thesis presents two novel robust algorithm and an adaptive discretization framework that can obtain successful sparse representations. In the proposed techniques, the selected dictionary atoms are perturbed towards directions to decrease the orthogonal residual norm. The first algorithm named as Parameter Perturbed Orthogonal Matching Pursuit (PPOMP) targets the off-grid problem and the parameters of the selected dictionary atoms are perturbed. The second algorithm named as Perturbed Orthogonal Matching Pursuit (POMP) targets the unstructured basis mismatch problem and performs controlled rotation based perturbation of selected dictionary atoms. 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 both parametric and unstructured basis mismatch problem can be obtained as compared to standard sparse reconstruction techniques. Different from the proposed perturbation approaches, the proposed adaptive framework discretizes the continuous parameter space depending on the estimated sparsity level. Once a provisional solution is obtained with a sparse solver, the framework recursively splits the problem into sparser sub-problems so that each sub-problem is exposed to less severe off-grid problem. In the presented recursive framework, any sparse reconstruction technique can be used. As illustrated over commonly used applications, the error in the estimated parameters of sparse signal components almost achieve the Cram´er-Rao lower bound in the proposed framework.
Open Access
Sıkıştırılmış algılama kullanarak Uzaklık-Doppler radar hedef tespiti
(IEEE, 2014-04) Sevimli, R. Akın; Tofighi, Mohammad; Çetin, A. Enis
Sıkıştırılmış algılama(SA) fikri, az sayıda ölçümlerden seyrek bir sinyalin geri çatımını mümkün kılar. SA yaklaşımı bir çok farklı alanda uygulamalara sahiptir. Bu alanlardan birisi de radar sistemleridir. Bu makalede, radar belirsizlik fonksiyonu (Ambiguity Function) SA çatısı altında gürültüden arındırılmıştır. Bu amaç için dışbükey fonksiyonun epigraf kümesine izdüşüm tabanlı yeni bir gürültüden arındırma metodu geliştirilmiştir. Bu yaklaşım, diğer SA geri çatım algoritmalarıyla karşılaştırılmıştır. Deneysel sonuçlar sunulmuştur.
Open Access
Target detection and imaging on passive bistatic radar systems = Pasif bistatik radar sistemleri üzerinde hedef tespiti ve görüntülenmesi
(2014) Sevimli, Rasim Akın
Passive Bistatic Radar (PBR) systems have become more popular in recent years in many research communities and countries. Papers related to PBR systems have increasingly received significant attention in research. There are many target detection methods for PBR system in the literature. This thesis assumes a system scenario based on stereo FM signals as transmitters of opportunity. Ambiguity function (AF) is a function that determines the locations of targets in range-Doppler map turns out to be noisy in practice. This can cause a problem with low SNR-valued targets because they cannot be visible. To solve this problem, compressive sensing (CS) and projection onto the epigraph set of the `1 ball (PES-`1) are used to denoise the range-Doppler map. Some CS methods are applied to the system scenario, which are Basis Pursuit (BP), Orthogonal Matching Pursuit (OMP), Compressed Sampling Matching Pursuit (CoSaMP), Iterative Hard Thresholding (IHT). In addition, AF is generally used to determine the similarities between two signals. Therefore, different correlation methods can be also used to compare the surveillance and time delayed frequency shifted replica of the reference signal. Maximal Information Coefficient (MIC), Pearson correlation coefficient, Spearman’s rank correlation coefficient are used for the target detection. This thesis proposes a least squares (LS) based method which outperforms other correlation algorithms in terms of PSNR and SNR. Two LS coefficients are obtained from the real and imaginary parts of predicting the surveillance signal using the modulated reference signal. Norm of LS coefficients exhibit a peak at target locations. The proposed method detects close targets better than the ordinary AF method and decreases the number of sidelobes on multiple FM channels based the PBR system.