Compressive sensing for radar target detection

Download
2014
Çağlıyan, Firuze
Compressive sampling, also known as compressive sensing and sparse recovery, is a new type of sampling theory, which predicts that sparse signals and images can be reconstructed from far less amount of data than what was traditionally considered necessary (i.e. Nyquist/Shannon sampling theory). The theory has many applications such as design of new imaging systems, cameras, sensor networks and analog to digital converters. Several algorithms have been proposed for the measurement and recovery process of the theory. The theory uses only a small amount of measurements which are linear, nonadaptive and suitably designed. The reconstruction process is nonlinear and simply depends on searching for the sparsest vector that is coherent with the measurements. The compressive sensing theory and its key points are explained in detail. In this thesis, compressive sensing (CS) is used to reconstruct the target scene of a radar. The target scene is discretized so that a total of N possible target locations exist. The number of targets K is assumed to be small (i.e., K<<N) meaning that the target scene is sparsely populated. A theoretical lower bound on the number of measurements M depending on the sparsity K and the total number of data N is presented based on the results in the literature. The target scene reconstruction results for different noise levels are compared. Three different compressive sensing reconstruction methods are described and their performances are compared. The compressive sensing radar target detection and the classical radar detection performance difference is investigated. The change in probability of detection due to SNR variation under constant false alarm rate (FAR) is analyzed. Finally, the effect of Doppler to the compressive sensing radar target detection is analyzed. When the number of measurements is limited, i.e., M<N, there is an SNR loss in detection performance. The CS method roughly attains the performance of classical detection when received SNR is boosted by M/N either with higher power at the transmitter or lower noise figure at the receiver.

Suggestions

Radar propagation modelling using the split step parabolic equation method
Türkboyları, Alpaslan; Koç, Seyit Sencer; Department of Electrical and Electronics Engineering (2004)
This document describes radar propagation modelling using split step parabolic wave equation (PWE) method. A computer program using Fourier split-step (FSS) marching technique is developed for predicting the electromagnetic wave propagation in troposphere. The program allows specification of frequency, polarization, antenna radiation pattern, antenna altitude, elevation angle and terrain profile. Both staircase terrain modelling and conformal mapping are used to model the irregular terrain. Mixed Fourier tr...
An Adaptive fast time radar receiving filter for minimization of clutter and time side-lobes
Özdemir, Seçil; Tanık, Yalçın; Department of Electrical and Electronics Engineering (2013)
In this thesis, a maximum likelihood receiver to obtain the target range profile that uses the clutter prediction derived from the target-free previous observations is suggested as a fast time processor for pulse compression radar systems. The maximum likelihood receiver is proposed to overcome the range sidelobe problem, which is introduced by the pulse compression method. Conventional methods, such as the matched filter receiver, as fast time processor result in the targets with high radar cross sec- tion...
Monte Carlo analysis of the effects of the material and shape uncertainties on radar cross section by the finite difference time domain method
Kazar, Ali Kemal; Kuzuoğlu, Mustafa; Özgün, Özlem; Department of Electrical and Electronics Engineering (2013)
The aim of this research is to analyze the variations in Radar Cross Section (RCS) values of dielectric and conducting objects due to material and shape uncertainties by employing the Finite Difference Time Domain Method and the Monte Carlo approach in electromagnetic scattering problems. MATLAB codes are developed and validated to solve the electromagnetic scattering problem involving two and three dimensional arbitrarily-shaped objects. Basic principles of FDTD and its implementation in MATLAB are describ...
Analysis and design of conformal frequency selective surfaces /
Dalkılıç, Akın; Alatan, Lale; Department of Electrical and Electronics Engineering (2014)
An efficient analysis and design approach for conformal frequency selective surface (FSS) structures is developed. The design methodology involves the analysis of both the planar and curved FSS structures. First, planar unit cell analysis of conformal FSS models are accomplished for normal and oblique incidence cases. To prove conformal applicability of planar designs, a semi-finite analysis method is utilized. This method is based on solution of a singly periodic curved FSS structure of semi-cylinder shape...
Compressive sensing methods for multi-contrast magnetic resonance imaging
Güngör, Alper; Yarman Vural, Fatoş Tunay; Çukur, Tolga; Department of Computer Engineering (2017)
Compressive sensing (CS) is a signal processing tool that allows reconstruction of sparse signals from highly undersampled data. This study investigates application of CS to magnetic resonance imaging (MRI). In this study, first, an optimization framework for single contrast CS MRI is presented. The method relies on an augmented Lagrangian based method, specifically alternating direction method of multipliers (ADMM). The ADMM framework is used to solve a constrained optimization problem with an objective fu...
Citation Formats
F. Çağlıyan, “Compressive sensing for radar target detection,” M.S. - Master of Science, Middle East Technical University, 2014.