Current Issue : April - June Volume : 2017 Issue Number : 2 Articles : 6 Articles
The estimation of direction-of-arrival (DOA) of signals is a basic and important problem in sensor array signal processing. To\nsolve this problem, many algorithms have been proposed, among which the Stochastic Maximum Likelihood (SML) is one of\nthe most concerned algorithms because of its high accuracy of DOA. However, the estimation of SML generally involves the\nmultidimensional nonlinear optimization problem. As a result, its computational complexity is rather high. This paper addresses\nthe issue of reducing computational complexity of SML estimation ofDOAbased on theAlternatingMinimization (AM) algorithm.\nWe have the following two contributions. First using transformation of matrix and properties of spatial projection, we propose an\nefficient AM (EAM) algorithm by dividing the SML criterion into two components. One depends on a single variable parameter\nwhile the other does not. Second when the array is a uniform linear array, we get the irreducible form of the EAM criterion\n(IAM) using polynomial forms. Simulation results show that both EAM and IAM can reduce the computational complexity of\nSML estimation greatly, while IAMis the best. Another advantage of IAMis that this algorithm can avoid the numerical instability\nproblem which may happen in AM and EAM algorithms when more than one parameter converges to an identical value....
Windowing applied to a given signal is a technique commonly used in signal processing\nin order to reduce spectral leakage in a signal with many data. Several windows\nare well known: hamming, hanning, beartlett, etc. The selection of a window is\nbased on its spectral characteristics. Several papers that analyze the amplitude and\nwidth of the lobes that appear in the spectrum of various types of window have been\npublished. This is very important because the lobes can hide information on the frequency\ncomponents of the original signal, in particular when frequency components\nare very close to each other. In this paper it is shown that the size of the window can\nalso have an impact in the spectral information. Until today, the size of a window has\nbeen chosen in a subjective way. As far as we know, there are no publications that\nshow how to determine the minimum size of a window. In this work the frequency\ninterval between two consecutive values of a Fourier Transform is considered. This\ninterval determines if the sampling frequency and the number of samples are adequate\nto differentiate between two frequency components that are very close. From\nthe analysis of this interval, a mathematical inequality is obtained, that determines in\nan objective way, the minimum size of a window. Two examples of the use of this\ncriterion are presented. The results show that the hiding of information of a signal is\ndue mainly to the wrong choice of the size of the window, but also to the relative\namplitude of the frequency components and the type of window. Windowing is the\nmain tool used in spectral analysis with nonparametric periodograms. Until now,\noptimization was based on the type of window. In this paper we show that the right\nchoice of the size of a window assures on one hand that the number of data is enough\nto resolve the frequencies involved in the signal, and on the other, reduces the number\nof required data, and thus the processing time, when very long files are being\nanalyzed....
In recent years, much attention has been focused on difference co-array perspective in DOA estimation field due to\nits ability to increase the degrees of freedom and to detect more sources than sensors. In this article, a fractional\ndifference co-array perspective (FrDCA) is proposed by vectorizing structured second-order statistics matrices instead\nof conventional zero-lag covariance matrix. As a result, not only conventional virtual sensors but also the fractional\nones can be utilized to further increase the degrees of freedom. In a sense, the proposed perspective can be viewed\nas an extended structured model to generate virtual sensors. Then, as a case study, four DOA estimation algorithms\nfor wideband signal based on the FrDCA perspective are specifically presented. The fractional virtual sensors can be\ngenerated by dividing the wideband signal into many sub-band signals. Accordingly, the degree of freedom and the\nmaximum number of resolvable sources are increased. The corresponding numerical simulation results validate the\nadvantages and the effectiveness of the proposed perspective....
Different from the phased-array using the same carrier frequency for each transmit element, the frequency diverse\narray (FDA) uses a small frequency offset across the array elements to produce range-angle-dependent transmit\nbeampattern. FDA radar provides new application capabilities and potentials due to its range-dependent transmit\narray beampattern, but the FDA using linearly increasing frequency offsets will produce a range and angle coupled\ntransmit beampattern. In order to decouple the range-azimuth beampattern for FDA radar, this paper proposes a\nuniform linear array (ULA) FDA using Costas-sequence modulated frequency offsets to produce random-like energy\ndistribution in the transmit beampattern and thumbtack transmit-receive beampattern. In doing so, the range and\nangle of targets can be unambiguously estimated through matched filtering and subspace decomposition algorithms\nin the receiver signal processor. Moreover, random-like energy distributed beampattern can also be utilized for low\nprobability of intercept (LPI) radar applications. Numerical results show that the proposed scheme outperforms the\nstandard FDA in focusing the transmit energy, especially in the range dimension....
A new technique is proposed to reduce the computational complexity of the multiple signal classification (MUSIC)\nalgorithm for direction-of-arrival (DOA) estimate using a uniform linear array (ULA). The steering vector of the ULA is\nreconstructed as the Kronecker product of two other steering vectors, and a new cost function with spatial aliasing at\nhand is derived. Thanks to the estimation ambiguity of this spatial aliasing, mirror angles mathematically relating to\nthe true DOAs are generated, based on which the full spectral search involved in the MUSIC algorithm is highly\ncompressed into a limited angular sector accordingly. Further complexity analysis and performance studies are\nconducted by computer simulations, which demonstrate that the proposed estimator requires an extremely reduced\ncomputational burden while it shows a similar accuracy to the standard MUSIC...
In recent years, the frequency diverse array (FDA) radar concept has attracted extensive attention, as it may benefit\nfrom a small frequency increment, compared to the carrier frequency across the array elements and thereby achieve\nan array factor that is a function of the angle, the time, and the range which is superior to the conventional phase\narray radar (PAR). However, limited effort on the subject of FDA in electronic countermeasure scenarios, especially in\nthe presence of mainbeam deceptive jamming, has been published. Basic FDA is not desirable for anti-jamming\napplications, due to the range-angle coupling response of targets. In this paper, a novel method based on subarrayed\nFDA signal processing is proposed to counteract deceptive ECM signals. We divide the FDA array into multiple\nsubarrays, each of which employs a distinct frequency increment. As a result, in the subarray-based FDA, the desired\ntarget can be distinguished at subarray level in joint range-angle-Doppler domain by utilizing the fact that the\njammer generates false targets with the same ranges to each subarray without reparations. The performance\nassessment shows that the proposed solution is effective for deceptive ECM targets suppression. The effectiveness is\nverified by simulation results....
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