World Journal of Science and Technology 2012, 2(10):191193 ISSN: 2231 – 2587 Available Online: www.worldjournalofscience.com
Novel angle of arrival algorithms in smart antenna
Shivakumar Raga,
Vijaykumar Katgi, Sharan Gowda and Prashant R S
Asst.Prof. Dept of E&CE, BKIT (REC) Bhalki, Karnataka, India
Abstract
Adaptive array smart antenna manipulate the signals produced on various antenna elements in such way that the main beam directing towards the desired signal. Such that they can increase the channel capacity and coverage range. In Adaptive array smart antenna, to locate the desired signal, various angle of arrival (AOA) estimation algorithms are used and depending on values of these algorithms beam will be formed in desired location. This paper compares different Angle of Arrival Algorithms in terms of resolution. Simulation in this project shows that MUSIC algorithm is highly accurate and stable and provides high angular resolution compared to other algorithms and hence MUSIC algorithm can be widely used in mobile communication to estimate the AOA of the arriving signals.
Keywords:
AOA, MUSIC algorithm, smart antenna
INTRODUCTION
In the last few years, lot of research has been taken place in array antennas which are smart enough to distinguish between desired and interference signal. Currently, the use of smart antennas in mobile communication to increase the capacity of communication channels has reignited research and development in this very exciting field. One such innovation is Smart Antenna (SA) and the type of multiple accesses it works on is Space Division Multiple Access (SDMA).Spatial Division Multiple Access (SDMA) concept is different from Frequency Division Multiple Access (FDMA) and Time Division Multiple Access (TDMA). Smart antennas involve processing of signals induced on an array of sensors such as antennas, microphones, and hydrophones. Smart antennas have the property of spatial filtering, which makes it possible to receive energy from a particular direction while simultaneously block energy from other direction. This property makes smart antennas a very effective tool in detecting, locating sources and finally forming the main beam in the look direction and nulls in the interfering signal directions. This paper explains about various AOA algorithms. In Chapter 2 we discussed different Angle of Arrival Algoritms. Chapter 3 covers simulation results of ifdferent AOA algorithms.Chapter 4 describes conclusions.
ANGLE OF ARRIVAL (AOA) Algorithms Maximum Eigen Value (MEV) Method
This method finds a power spectrum such that its Fourier transform equals the measured correlation subjected to the constraint that its entropy is maximized. For estimating DOA from the measurements using an array of sensors, the Maximum Eigen value(ME) method finds a continuous function P
MEv
(θ) > 0 such that it maximizes the entropy function. The Maximum Eigen Value (MEV) method power spectrum is given by
)()(
1
θ θ
a E E a
P
H sS H MEV
=
Where,
vectorseigenimum E
s
max
=
vector steeringof transpose Hermitiana
H
=
)(
θ
Maximum Entropy Method (MEM)
This method finds a power spectrum such that its Fourier transform equals the measured correlation subjected to the constraint that its entropy is maximized. For estimating DOA from the measurements using an array of sensors, the Maximum Entropy (ME) method finds a continuous function P
ME
(θ) > 0 such that it maximizes the entropy function.
∫
=
π
θ θ
20
)(ln)(
d PP H
ME
Subject to the constraint that the measured correlation between the i
th
and the j
th
elements r
ij
satisfies
∫
=
π
θ θ πτ θ
20
))(2cos()(
d Pr
ij ME ij
Where,
)(
θ τ
ij
denotes the differential delay between elements i and j due to a source in θ direction. The solution to this problem requires an infinite dimensional search. The problem has to be transformed to a finite dimensional search. One of the algorithms proposed by Lang and McClellan has power spectrum given by
*Corresponding Author Shivakumar Raga Asst.Prof. Dept of E&CE, BKIT (REC) Bhalki, Karnataka, India Email: conference@recb.com
Shivakumar Raga
et al.,
192
][1
θ θ
S C C S
P
H
H ME
=
Where, C is column of R
1
and
θ
S
is the steering vector. P
ME
(θ) is based on selecting one of L
th
array elements as a reference and attempting to find weights to be applied to the remaining L1 received signals to permit their sum with a minimum mean square error fit to the reference. Since there are L possible references, there are L generally different P
ME
(θ) obtained from the L possible column selections of R

Multiple signal classification (Music)
MUSIC is an acronym which stands for MUltiple SIgnal Classification. MUSIC promises to provide unbiased estimates of the number of signals, the angles of arrival and the strengths of the waveforms. MUSIC makes the assumption that the noise in each channel is uncorrelated making the noise correlation matrix diagonal. The incident signals may be correlated creating a non diagonal signal correlation matrix. However, under high Signal correlation the traditional MUSIC algorithm breaks down and other methods must be implemented to correct this weakness. One must know in advance the number of incoming signals or one must search the Eigen values to determine the number of incoming signals. If the number of signals is M, the number of signal Eigen values and eigenvectors is M and the number of noise Eigen values and eigenvectors are LM (L is the number of array elements). Because MUSIC exploits the noise eigenvector subspace. The Eigen values and eigenvectors for correlation matrix
R
is found. M eigenvectors associated with the signals and L−M eigenvectors associated with the noise are separated. The eigenvectors associated with the smallest Eigen values are chosen to calculate power spectrum. For uncorrelated signals, the smallest Eigen values are equal to the variance of the noise. The L× (L− M) dimensional subspace spanned by the noise eigenvectors is given by
[ ]
M L N
eeee E
−
=
.................
321
Where,
i
e
is the i
th
Eigen Value. The noise subspace Eigen vectors are orthogonal to the array steering vectors at the angles of arrival
M
θ θ θ
.,.........,
21
. Because of this orthogonality condition, one can show that the Euclidean distance
0)()(
2
==
θ θ
a E E ad
H N N H
for each and every angle of arrival
M
θ θ θ
.,.........,
21
.Placing this distance expression in the denominator creates sharp peaks at the angles of arrival. The MUSIC pseudo spectrum is given by
)()(
1
θ θ
a E E a
P
H N N H MUSIC
=
Where,
)(
θ
a
is steering vector for an angle
θ
and
N
E
is L x LM matrix comprising of noise Eigen vectors.
SIMULATION AND TEST RESULTS
Table 1:
Algorithm No.of Antenna Elements Ampliude Direction No.of Mobile Users MEV 100 [1 2 3]v [5 8 11] 3 MEM 100 [1 2 3]v [5 8 11] 3 MUSIC 100 [1 2 3]v [5 8 11] 3
Fig 1: MEV Method for Closely Spaced Sources & More Antenna Elements
From the fig 1 MEV Method is can detect all the mobile users. The resolution of MEV method is improved as number of antenna elements increases.
Fig 2: MEM Method for Closely Spaced Sources & More Antenna Elements
From the Fig 2 MEM Method is capable of detecting the mobile user’s which are closely spaced. The resolution of MEM method is improved as number of antenna elements increases.
Fig 3: MUSIC Method for Closely Spaced Sources & More Antenna Elements
From the Fig 3 MUSIC Method is capable of detecting the mobile user’s for closely spaced sources and more antenna elements.
CONCLUSION
Above results shows that MEV Algorithm is worst in terms of Resolution. MEM method have better Resolution as compare to MEV and MUSIC algorithm is highly accurate and stable and provides high angular resolution compared to MEV and MEM and hence MUSIC algorithm can be widely used to estimate the AOA of the arriving signals in Smart Antennas.
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” ,
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World Journal of Science and Technology 2012, 2(10):191193
193
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