Direction of Estimation

Only available on StudyMode
  • Topic: Estimation theory, Signal processing, Maximum likelihood
  • Pages : 5 (1447 words )
  • Download(s) : 79
  • Published : May 29, 2013
Open Document
Text Preview
In wireless communication using omnidirectional antennas communication is made in directions not pointing towards the recipient resulting in energy waste, and producing traffic collisions and reducing the battery life. One way of reducing this problem is to use of smart antenna that uses selectable radiation patterns depending on the situations. These antennas provide the ability to sense the direction of incoming signals. Smart antennas are the antenna arrays with smart signal processing algorithms used to identify spatial signal signature such as the Direction of arrival (DOA) and used to calculate the beam forming vectors, to tract and locate the antenna beam on the mobile/target. Smart has the main function of DOA estimation. Keywords: Smart antenna, DOA ,Wireless communication.

1. Introduction: The smart antenna system estimates the direction of arrival of the signal, using techniques such as MUSIC (Multiple Signal Classification), estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithms. They Involve finding a spatial spectrum of the antenna/sensor array, and calculating the DOA from the peaks of this spectrum. These calculations are computationally intensive. 2.Direction Of arrival Estimation Algorithms

The array-based direction-of-algorithm estimation techniques can be broadly divided into four different types conventional techniques, subspace based techniques, maximum likelihood techniques and the integrated techniques which combine property restoral techniques with subspace based techniques. Conventional methods are based on classical beam forming techniques and require a large number of elements to achieve high resolution. Subspace based methods are high resolution sub-optimal techinques which exploit the eigen structure of the input data matrix. Maximum likelihood techniques are optimal techniques which can perform well even under low signal-to-noise ratio conditions, but are in general computationally very intensive. The integrated approach use property restoral based techniques to separate multiple signals and estimate their spatial signatures from which their directions of arrival can be determined using sub space techniques. 2.1 Conventional Methods for DOA Estimation

Conventional methods for direction-of-arrival estimation are based on the concepts of beam forming and null steering and don’t exploit the nature of the model of the received signal vector or the statistical model of the signals and noise. Given the knowledge of array manifold, an array can be steered electronically just as a fixed antenna can be steered mechanically. Conventional techniques used for DOA estimation consists of electronically steering beams in all possible directions, and looking for peaks in the output power. The conventional methods here are the delay-and-sum method (classical beam former) and Capon’s minimum variance method. 2.1.1 Delay-and-Sum Method

The delay and sum method, also termed as classical beam former method or Fourier method, is one of the simplest techniques for DOA estimation. The figure below shows the classical narrow band beam former structure, where the output signal y(k) is given by a linearly weighted sum of the sensor element outputs. That is, y(k) = wH x(k) (2.1)
FIGURE 1: Illustration of classical beam former method

The total output power of the conventional beam former can be expressed as Pcbf = E[|y(k)|2] = E[|wHx(k)|2] =wHE[x(k)xH(k)]w = wHRxxw
where Rxx is the autocorrelation matrix of the array input data. The auto correlation matrix Rxx contains useful in information about both the array response vectors and the signals themselves, and it is possible to estimate signal parameters by careful interpretation of Rxx. In this classical beam forming approach to DOA estimation, the beam is scanned over the angular region of interest in discrete steps forming weights w=a(ϴ) for...
tracking img