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Direction of Arrival (DOA) Estimation Using Smooth Music Algorithm

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Direction of Arrival (DOA) Estimation Using Smooth Music Algorithm International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 2 Issue 8, August - 2013 Direction Of Arrival (DOA) Estimation Using Smooth Music Algorithm Gowtham Prasad TVS 1 T. Ravi Kumar Naidu 2 1. Assistant Professor, Dept of ECE, SVEC, Tirupati, Andhra Pradesh, 2. Assistant professor, Dept. of ECE, SVEC, Tirupati, Andhra Pradesh ABSTRACT Time Division Multiple Access (TDMA) and An array antenna system with innovative signal Code Division Multiple Access (CDMA) can be processing can enhance the resolution of a signal enlarged by adding a new parameter ―space‖ or direction of arrival (DOA) estimation. Super ―angle‖, which results in MAT known as ―Space resolution algorithms take advantage of smart Division Multiple Access‖ (SDMA)[9,3,5]. At the antenna structures to better process the incoming receiver’s side, the transmitted signal is received signals. Thus, the smart antenna system becomes with its multipath components plus interferers capable to locate and track signals by the both users signal, as well as with present noise. Thus, detection and interferers and dynamically adapts the antenna of the desired signal is a challenging task. pattern to enhance the reception in Signal-Of- Interest direction and minimizing interference in The Smart Antenna Signal-Of-Not-Interest direction. They also have the System (SAS) employs the antenna elements and ability to identify multiple targets. This paper the digital signal processing which enables it to explores the Eigen-analysis category of super form a beam to a desired direction taking into resolution algorithm. A class of Multiple Signal account the multipath signal components. This can Classification (MUSIC) algorithms known as a be achieved by the adaptive beamforming technique Smooth-MUSIC algorithm is presented in this In this way, Signal-to-Interference-and-Noise Ratio paper. The Smooth-MUSIC method is based on the (SINR) improves by producing nulls towards the eigenvectors of the sensor array correlation matrix. interferers Signal-Of–No-Interest (SONI).The It obtains the signal estimation by examining the performance of SAS greatly depends on the peaks in the spectrum. Statistical analysis of the performance on DOA estimation. performance of the processing algorithm and processing resource requirements are discussed in In this paper, we are investigating the this paper. Extensive computer simulations are used performance of MUSIC, ROOT-MUSIC and to show the performance of the algorithms. The IJERTSmooth-MUSIC algorithms simulated by using resolution of the DOA techniques improves asIJERT MATLAB as a tool. The performance of these number of array elements increases. algorithms is analysed by considering parameters like number of array elements, user space Keywords --MUSIC, Adaptive Beamforming, distribution, which results in optimum array design DOA, Smooth-MUSIC, Smart antenna. in SAS. 2. Adaptive beam forming 1. Introduction The high demand on the usage of As the name indicates, an adaptive beam the wireless communication system calls for higher former is able to automatically adapt its response to system capacities. The system capacity can be different situations. Some criterion has to be set up improved either enlarging its frequency bandwidth to allow the adaption to proceed such as minimizing or allocating new portion of frequency spectrum to the total noise output. Because of the variation of wireless services. But since the electromagnetic noise with frequency, in wide band systems it may spectrum is a limited resource, it is not easy to get be desirable to carry out the process in new spectrum allocation without the international the frequency domain. Beam forming can be coordination on the global level[1,7] . One of the computationally intensive. Sonar phased array has a approaches is to use existing spectrum more data rate low enough that it can be processed in real- efficiently, which is a challenging task. Efficient time in software, which is flexible enough to source and channel coding as well as reduction in transmit and/or receive in several directions at once. transmission power or transmission bandwidth or In contrast, radar phased array has a data rate so both are possible solutions to the challenging issue. high that it usually requires dedicated hardware With the advances in digital techniques, the processing, which is hard-wired to transmit and/or frequency efficiency can be improved by multiple receive in only one direction at a time. However, access technique (MAT), which gives mobile users newer field programmable gate arrays are fast access to scarce resource (base station) and hence enough to handle radar data in real-time, and can be improves the system’s capacity. Family of existing quickly re-programmed like software, blurring the Frequency Division Multiple Access (FDMA), hardware/software distinction. The above mentioned IJERTV2IS80333 www.ijert.org 899 International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 concept is related to the process of beam forming 3.2 Parametric Method: Vol. 2 Issue 8, August - 2013 and it doesn’t exploit the possibility to steer the It requires simultaneous search of all parameters. nulls of the antenna beam in the direction of the It requires prior information about data to be RFIs (adaptive beam forming). These evolutions of generated. Eliminates need for window functions standard beamformers are conceived to separate and assumptions. In this section, we review the (analogically, digitally or both) a desired signal maximum likelihood methodology for the case of from one or more interfering signals (Spatial additive Gaussian noise of zero mean and variance Filtering) by means of automatic and continuous matrix. characterization of the components of the weighting The two methods as follows: vector. This can be performed using a wide variety (i) Deterministic Maximum Likelihood of different algorithms designed for many specific (ii) Stochastic Maximum Likelihood applications. Deterministic maximum likelihood: 3. DOA ESTIMATION According to the Deterministic ML (DML) the signals are considered as unknown, deterministic Adaptive signal processing sensor arrays, known quantities that need to be estimated in conjunction also as smart antennas, have been widely adopted in with the direction of arrival. This is a natural model third-generation (3G) mobile systems because of for digital communication applications where the their ability to locate mobile users with the use of signals are far from being normal random variables, DOA estimation techniques. Adaptive antenna and where estimation of the signal is of equal arrays also improve the performance of cellular interest. systems by providing robustness against fading channels and reduced collateral interference. The Stochastic maximum likelihood: goal of direction-of-arrival (DOA) estimation is to In general, the SML estimate (SML) cannot be use the data received on the downlink at the base- found analytically. Hence, numerical procedures station sensor array to estimate the directions of the must be employed to carry out the required signals from the desired mobile users as well as the optimization. Several optimization methods have directions of interference signals. The results of DOA estimation are then used by to adjust the been proposed in the literature, including the weights of the adaptive beamformer so that the Alternating Projection method, several Newton type radiated power is maximized towards the desired techniques and the Expected Maximization (EM) users, and radiation nulls are placed in the directions method. The SML likelihood function is regular and of interference signals. Hence, a successful design the SML estimator is consistent and asymptotically of an adaptive array depends highly on the choice of efficient, i.e., the covariance of the estimates the DOA estimation algorithm which should be asymptotically attains the stochastic Cramer Rao highly accurate and robust. There are three methods to find Direction OfIJERT IJERTBound. Arrival (DOA) 3.3 Sub Spaced-Based Method: 1 .Spectral based method, In this method the DOA estimation is based on the 2. Parametric method, Eigen values and depends upon the steering vectors. 3. Sub space-based method. (i) MUSIC Family algorithms 3.1 Spectral Based Method: (ii) ESPIRIT Algorithm The DOA estimation can be done by computing spatial spectrum and then determining local In this paper we focussed on MUSIC Family maximas. algorithms. The MUSIC family consists of (i) Conventional MUSIC Algorithm (ii) Capon’s & Bartlett ROOT-MUSIC Algorithm Conventional: SMOOTH-MUSIC Algorithm The conventional beam former is a natural The mathematical models of MUSIC family as extension of classical Fourier-based spectral analysis to sensor array data. For an array of follows: arbitrary geometry, this algorithm maximizes the 3.3.1 MUSIC: power of the beam forming output for a given input Consider the signal model of M signals incident on signal. the array, corrupted by noise, i.e. Capon’s &Bartlett: In this method a rectangular window of uniform weighting is applied to the time series data to be analyzed. Using the above data ] IJERTV2IS80333 www.ijert.org 900 International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 2 Issue 8, August - 2013 Let n=noise 2 1 0 0 0 0 S is a matrix of M steering vectors. Assuming that 0 2 0 0 0 the different signals to be uncorrelated the 2 correlation matrix of x can be written as 2 2 R Q 0 0 M 0 0 0 0 0 2 0 2 0 0 0 0 Where Based on Eigen decomposition we can partition the Eigen vector matrix Qs with M columns, 2 corresponding to M signal Eigen values, and a E[det 1 ] 0 ............... 0 matrix Qn with (N-M) columns, corresponding the 2 A 0 E[det 2 ] ............... 0 noise Eigen values . 2 Note that Qn, N× (N-M) matrix of 0 0 0 E[det m ] Eigen vectors corresponding to the noise Eigen The signal covariance matrix Rs is clearly a N×N matrix with rank M. It therefore has N-M Eigen value is exactly the same as the matrix of vectors corresponding to the Eigen value.
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