SYNTHESIS OF ANALOG AND STEERINGmance of smart antenna in mobile network depends on the accu-racy of...

SYNTHESIS OF ANALOG ANDDIGITAL STRUCTURES FORDIRECTION OF ARRIVALESTIMATION AND BEAM

STEERING

Dr.S.Syed Ameer AbbasDepartment of Electronics and Communication

EngineeringMepco Schlenk Engineering College

(Autonomous),Sivakasi-626005.abbas [email protected]

August 4, 2018

Abstract

Array processing is an area of study devoted to process-ing the signals received from antenna array and extractinginformation of interest. It has played an important rolein wide spread application like radar, sonar and wirelesscommunication. It has been focused on the development ofbeam steering and direction of arrival (DOA) estimation.The resolution of a signal direction of arrival estimationcan be enhanced by an array antenna system with innova-tive signal processing. DOA estimation is one of the cru-cial parameters for next generation mobile and broadbandapplications. DOA estimation essentially concerns the esti-mation of direction of arrival of signals, either in the formof electromagnetic or acoustic waves , impinging on a sen-sor or antenna array. The phase and amplitude based DOA

1

International Journal of Pure and Applied MathematicsVolume 120 No. 6 2018, 10961-10982ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

10961

techniques fail for input signals coming from different direc-tions with same frequency. The subspace based super reso-lution methods are best alternatives for conventional DOAmeasurement techniques. Super resolution algorithms takeadvantage of array structures to better process the incom-ing signals. They also have the ability to identify multipletargets. It explores the Eigen analysis category of super res-olution algorithm that has been worked here and the wholeframework has been simulated using Modelsim and imple-mented in Spartan 6.

Keywords:Array signal processing, Direction of arrival(DOA) estimation, MUSIC (Multiple Signal Classification)algorithm.

1 Introduction

Array signal processing is an active area of research in the broadeld of signal processing and focuses on the problem of estimatingsignal parameters from data collected over the spatial aperture ofan antenna array, where the sensors are placed at distinct spatiallocations. The given estimation task is usually associated withthe extraction of desired information from impinging signals in thepresence of noise and interference. The antenna array addressesthe estimation problem by exploiting the spatial separation of thesensor elements to capture the propagating wavefronts, which orig-inate from energy-radiating sources. Common signal parameters ofinterest to be estimated are the signal content itself, the directionsof arrival of the signals, and their power. To obtain this infor-mation, the sensor array data has been processed using statisticaland adaptive signal processing techniques. These schemes includeparameter estimation and adaptive ltering applied to array signalprocessing.

Array processing has expected to play an important role in ful-lling the increased demands of various mobile communications ser-vices. Array signal processing has always aimed to detect the pres-ence of a signal in the presence of noise and interference, demod-ulate the signal and estimate the information waveform, a binarycommunication signal arrives over multiple paths, detect the infor-mation sequence, estimate the direction of arrival of multiple plane

2

International Journal of Pure and Applied Mathematics Special Issue

10962

wave signals in the presence of noise, construct temporal and spa-tial spectral estimate of the incoming signal and noise field, directthe transmitted signal to a specific location. The underlying set ofprinciples and techniques for sensor array signal processing is ap-plicable in many versatile areas including wireless communications,radar, sonar, biomedicine, seismology and astronomy. Its capabili-ties of steering nulls to reduce co-channel interferences and pointingindependent beams toward various mobiles, as well as its ability toprovide estimates of directions of radiating sources, make it at-tractive to a mobile communications system designer. In antennatheory, a phased array usually means an electronically scanned ar-ray; a computer-controlled array of antennas which creates a beamof radio waves which can be electronically steered to point in differ-ent directions, without moving the antennas. In an array antenna,the radio frequency current from the transmitter is fed to the in-dividual antennas with the correct phase relationship so that theradio waves from the separate antennas add together to increasethe radiation in a desired direction, while cancelling to suppressradiation in undesired directions. In a phased array, the powerfrom the transmitter is fed to the antennas through devices calledphase shifters, controlled by a computer system, which can alterthe phase electronically, thus steering the beam of radio waves toa different direction. Since the array must consist of many smallantennas (sometimes thousands) to achieve high gain, phased ar-rays are mainly practical at the high frequency end of the radiospectrum, in the UHF and microwave bands, in which the antennaelements are conveniently small.

A smart antenna composed of various sensors whose signal isdiagnosed automatically. By locating the main beam in the user di-rection and forming nulls towards the interference signal direction,smart antenna can provide higher signal to noise (SNR) , minimumco-channel interference and multipath fading with higher the sys-tem capabilities . The smart antenna employs antenna arrays alongthe units of digital signal processing to enhance transmission andreception of antenna patterns for the signal environment responsein [4] . The block diagram of a smart antenna is shown in Figure1.

3


10963

Figure 1 Smart antenna block

DOA methods can be used to design and adapt the directivity ofarray antennas .For example,an antenna array can be used designedto detect the number of incoming signals and accept signals fromcertain directions only,while rejecting signals.This spationtemporalestimation and filtering capability can be exploited for multiplexingco channel users and rejecting harmful co channel interference thatmay occur because of jamming or multipath effects. The estima-tion of angle of arrival is done via a function which is traditionallyknown as the pesudo spectrum PMU().A potential approach usedhere to define that function is MUSIC algorithm via DOA estima-tion and beamsteering. The rest of the paper is organized as follows:Section II includes the literature survey done in this field; SectionIII describes the Antenna Receiver Model, Section IV presents theMUSIC Algorithm, Section V gives a detailed implementation pro-cedure of the work; Section VI provides detailed resultsfollowed bythe conclusion in Section VII.

2 LITERATURE SURVEY

The possibiltiy of optimization of some key parameters of the MU-SIC algorithm that enhance the performance of the estimation pro-cess and leads to an increased accuracy in determining the direc-tions of multiple users and beamforming in [1]. Compared to clas-sical method such as FFT which is failed to locate the DOA forsingle source in the accurate direction, the highe resolution tech-nique such as Pisarenko Harmonic Decomposition(PHD), MultipleSignal Classification (MUSIC) and Eigen vector (EV) methods have

4


10964

better performance in [2]. Estimation of an arbitrary location ofsignal source can be performed with moderate accuracy if the datacollection time is sufficiently long or the SNR is adequately highand the signal model is sufficiently accurate in [3]. The Perfor-mance of smart antenna in mobile network depends on the accu-racy of determination of angle of arrival (AOA) or the direction ofarrival (DOA). The performances of the Multiple Signal Classifica-tion(MUSIC ) algorithm and improved MUSIC algorithm are usedfor DOA estimation in [4].

3 ANTENNA RECEIVER MODEL

Spatial spectrum estimation is a specialized signal estimation tech-nology that uses space arrays to achieve a space signal parameter.The entire spatial spectrum system should be composed of threeparts: the incident signal space, spatial array receiver and param-eter estimation. The space can be divided into three correspond-ing spaces, namely target stage, observation stage, and estimationstage.

Figure 2: System structure of DOA estimation

The system architecture shown in Figureure 2 has got three stages.Target stage which is a stage that consists of signal source pa-rameters and a complex environment. For the spatial spectrumestimation system, it uses some particular methods to estimate theunknown parameters of signals which come from this complex tar-get stage. Observation stage is a stage which receives the radiationsignals from the target stage. Due to the complexity of the envi-ronment, the received data may contain some signal characteristics(azimuth, distance, polarization, etc.) and the space environment

5


10965

characteristics (noise, miscellaneous waves, interference, etc.). Inaddition, due to the influence of spatial array elements, the datareceived also contain some features of space array element (mu-tual coupling, channel inconsistent, frequency band inconsistency,etc.). This observation stage is multidimensional where the systemreceiving data is composed of a number of channels, and the tra-ditional time domain processing method is usually used for onlya single channel. Estimation stage is a stage which uses spatialspectrum estimation techniques (including array signal processingtechniques such as array correction and spatial filtering techniques)to extract the signal character parameters from the complex envi-ronment. Estimation stage is equivalent to the reconstruction ofthe target stage. The accuracy of reconstruction is determined bymany factors, such as the complexity of the environment, the mu-tual coupling of spatial array, different channels, frequency bandinconsistency, etc. Spatial spectrum expresses the energy distribu-tion of signals in all spatial directions. If one can get the spatialspectrum of the signal, the direction of arrival (DOA) of the sig-nal can be obtained. DOA is for the direction of array antenna ofthe radio wave. If the radio wave received meets the condition offar field narrowband, it can take the front of the radio wave as aplane. The angle between the array normal and the direction vec-tor of the plane wave is the DOA. The estimated target of DOAgives N snapshots of data using an algorithm to estimate the valueof multiple signals DOA (). For generally far and wide signals, awave-way difference exists when the same signal reaches differentarray elements. This wave-way difference leads to a phase differ-ence between the arrival array elements. Using the phase differencebetween the array elements of the signal one can estimate the sig-nal azimuth, which is the basic principle of DOA estimation. Theantenna receiver model is shown in Figureure 3 and Figureure 4. Itis assumed that a system consists of uniform linear array with Mreceiver elements with an inter-element spacing of dcm and D inci-dent signals in [1].The incident signals from D users are representedin amplitude and phase at some arbitary reference point (origin ofthe co ordinate system) by the complex quantities S1,S2,..SD.alsowhite gaussian is added to the signals as vector(n).Directions of theincident signals represented by the steering vector a(i) for ith userso we have matric A of order MxD.(i.e)the first column a(1) is the

6


10966

steering vector for 1st user and so on,where a(1) can be given as

Where β is the incident wave number=2π/ λ

Figure 3. Uniform linear array antenna Rx model

Figure 4. Uniform linear array antennaThe signals that reach the antenna is modeled as in equation(1)

7


10967

In general this could be written as in equation (2) ...(2)Here x(k) of order [M x K] is the amplitude of signal + noise in ithelement. The S(k)=vector of incident signals at sample at sampletime k with matrix size [D x K]. Hence n(k) is the noise vector ateach element m for matrix [M x K]. The parameter a(θi) is the M-element array steering vector- for matrix [Mx1]. The parameter A is[ M x D] matrix of steering vectors a(θi). It is initially assumed thatthe arriving signals are monochromatic and the number of arrivingsignals D < M. It is understood that the arriving signals are timevarying and thus our calculations are based upon time snapshotsof the incoming signal.

4 THE MUSIC ALGORITHM

The basic idea of MUSIC algorithm is to conduct characteristic de-composition for the covariance matrix of any array output data, re-sulting in a signal subspace orthogonal with a noise subspace corre-sponding to the signal components. Then these two orthogonal sub-spaces are used to constitute a spectrum function, be got throughby spectral peak search and detect DOA signals. It is because MU-SIC algorithm has a high resolution, accuracy and stability undercertain conditions that it attracts a large a large number of schol-ars to conduct in depth research and analyses. In general ,it hasthe following advantages like the ability to simultaneously measuremultiple signals, High precision measurement, High resolution forantenna beam signals, applicable to short data circumstances andit can achieve real time processing after using high-speed processingtechnology. In order to analyze and derive more conveniently, nowassume the following conditions for the ideal mathematical model ofDOA problems. The first condition is that, each test signal sourcehas the same but unrelated polarization. Generally consider thatthe signal sources are narrow bands, and each source has the same

8


10968

centre frequency .The number of testing signal source is consid-ered to be D. The second condition is that the antenna array is aspaced linear array which consists of M (M>D)array elements; eachelement has the same characteristics, and it is isotropic in each di-rection. The third condition is in the spacing is d , and the arrayelement interval is not large than half the wavelength of the highestsignal frequency. The other conditions are like each antenna ele-ment in the far field source, namely an antenna array receiving thesignals coming from the signal source is a plane wave. And botharray elements and test signals are uncorrelated; variance 2 is zeromean Gaussian noise nm (t). Also each receiving branch has thesame characteristics.

MUSIC Algorithm deals with the decomposition of correlationmatrix into two orthogonal matrices, signal subspace and noise sub-space. Estimation of direction is performed from one of these sub-spaces, assuming that noise in each channel is highly uncorrelated.This makes the correlation matrix diagonal. Implementation blocksof MUSIC is show in Figure 5

Figure 5 MUSIC implementation flow chart

From equation (3) H means Hermitian which is conjugate transpose,

9


10969

E is the Expected value is the statistical average , Rss is D x Dsource correlation matrix, Rnn= M x M noise correlation matrix.The array correlation matrix has M Eigen values (λ1,λ2,.λM) alongwith M associated Eigen vectors E=[e1,e2,.eM]. If the Eigen valuesare sorted from largest to smallest, we can divide the matrix E intotwo subspaces [EN ES]. The first subspace EN is called the noisesubspace and is composed of M-D eigenvectors associated with thenoise. The second subspace ES is called the signal subspace andis composed of D eigenvectors associated with the arriving signals.The noise subspace is an M x (M-D) matrix. The signal subspace isan M x D matrix. The noise subspace eigenvectors are orthogonalto the array steering vectors at the angles of arrival θ1 ,θ2,..θD.Placing this distance expression in the denominator creates sharppeaks at the angles of arrival .The MUSIC pseudo spectrum is nowgiven as

PMU(θ) =a(θ)Ha(θ)

a(θ)HEN~EHN a(θ)

equation− 4

Now higher angular resolution is achieved by the Pseudo spectrumof the MUSIC using equation (4) .

5 IMPLEMENTATION OF Proposed

Architecture in FPGA

subspace based music algorithm in Xilinx FPGA. Xilinx tools areused for the implementation of MUSIC algorithm in verilog. Theblock diagram has shown in Figure 6, consist of two main partswhich are Analog part and Digital part. In analog part, all theblocks of the AOA system that use analog signals have been mod-eled in this part which consist of the signal generators, the antennaarray, the front-ends and the ADC. In digital block, all the blockshas been worked only with digital signals, which basically has thedifferent stages of the MUSIC algorithm, where the AOAs have beencalculated. Hence, the validation of the FPGA implementation hasdone against this part of the model. Note that this part does notmodel the behavior of the FPGA, it just models the algorithm (MU-SIC) that has been implemented in the FPGA. The modelling of

10


10970

the MUSIC algorithm has divided into 4 different blocks of CORblock, EVD block, En block, PMU block.

Figure 6: DOA block diagram

The antenna frontend array has electromagnetic field sensor circuitas shown in Figure7. It depicts a very simple circuit that can beused to sense electromagnetic radiations. The circuit can even de-tect hidden wrings. A 1mH inductor has been used for sensing theelectric field. The electric field has induced a small voltage in thesensor inductor and this induced voltage has been amplified by theopamp. The POT R4 can be used to adjust the gain of the ampli-fier. The circuit can be powered from a 9V PP3 battery. It is betterto have a radial type inductor for L1.The POT R4 can be used toadjust the gain. The switch S1 can be a slide type ON/OFF switch.The IC1 must be mounted on a holder. All electrolytic capacitorsmust be rated at least 15V.

Figure 7: RF detector circuit

Next is the ADC. The ADS8634 is a 12-bit analog-to-digital con-verters (ADCs) capable of measuring inputs up to 10V at 1MSPS.Using a successive approximation register (SAR) core, these ADCsprovide a sample-and-hold front-end with no latency in conversions.The ADS8634 includes an input multiplexer (mux) for measuringup to four inputs. The ADS8634 can measure up to eight inputs.

11


10971

In addition to the input multiplexer, the ADS8634 feature an inter-nal temperature sensor, voltage reference, and a digital comparatorfor setting alarm thresholds on each input; therefore, a minimalamount of external components have been required. A simple SPI-compatible interface provides for communication and control. Thedigital supply operates from 5V all the way down to 1.8V for directconnection to a wide range of processors and controllers

The flow for whole implementation in FPGA shown in flow di-agram in the Figure 8. The uniform linear array of 3(M) receivingelements have placed at a distance (d) of 9.9cm in order to avoidaliasing according to the formula in equation (5) d¡ λ/2 and =2/λ (5) where λ is the wavelength of the light and λ=c/f c is thespeed of the light and f is the frequency Considering 3 receivingelements with one source of The wavelength has considered in therange between 10-14 to 1015 m and the radio frequency in the rangeof 20kHz to 300GHz. Now wavelength of 10-1 m has assumed forthe receiving signal.β=(2*3.14)/10-1and βd=2*3.14(6)The received signal matrix has denoted as a multiplication of steer-ing matrix and signal matrix. Since steering matrix has dividedinto real and imaginary steering matrix, signal matrix multipliedwith individually with real and imaginary steering matrix. Thearchitecture of multiplication of signal and steering matrix givenin the Figureure.9 This architecture has been invoked for 24 timesfor the entire process shown in the Figure 10 which describes thecomplete multiplier block.

12


10972

Figure 8: Flow diagram of DOA in FPGA

Figure 9: Matrix multiplication architecture

Figure 10: Complete multiplier block

Hermitian matrix has been calculated from the conjugate of thetransposed matrix shown in Figure 11 . Since the received signal

13


10973

matrix has been divided into real and imaginary , both the real andimaginary received signal matrices have transposed first and thenonly the transposed imaginary received signal matrix is conjugated

Figure 11. Hermitian process

Eigen values are determined from square matrix of Rxx by usingthe relation in expression (6) det(Rxx - λI) = 0..(6) (3.29) Thisequation results in cubic roots of λ which will be taken as λ1, λ2,λ3. After optimizing the algorithm, an optimized architecture forcube root calculator can be implemented. There are five functionalblocks to establish cube root calculator which consist of main datapath, factor generator, control, sign-in, and sign-out units. Pro-posed main data path architecture has shown in Figure.12 . Inthis architecture, components usage costs 3 buffer registers, 2 mul-tiplexers, 1 concatenation, and 1 subtraction. It shows that maindata path has been efficiently designed by using simple operations.For factor generator unit, proposed architecture has shown in Fig-ure. 13. Its components usage cost 1 buffer register, 2 shifters, 6multiplexers, 3 addition, and 1 multiplication. Multiplication is themost complex and area consuming operation here. Hence, iterativeprocess architecture has been chosen in order to optimize the areaconsumption. Main data path and factor generator units have beenconnected each other through generated factor signal. Integratingall these units will establish a complete cube root calculator archi-tecture as shown in Figure. 14. Actually, proposed architectureis generic architecture for integer cube root calculation. Hence,this architecture can be adapted to comply any number of inputbit-width scalability (e.g 16-bit, 32-bit, 64- bit, 128-bit, etc).

14


10974

Figure 12: Main data path architecture

Figure13: Factor generated architecture

15


10975

Figure 14: Complete architecture for cube root calculator

6 SIMULATION RESULTS

Figure 15 has shown the comparison of peak power for the variousangle to determine the desired angle which has the maximum peakpower among several angle of arrival using MATLAB simulation

Figure 15: Matlab simulation for various angle

The parameter β=(2*3.14)/10-1inter element spacing of 9.9 cm, inwhich source is positioned at three different angles in degree havingthree steering vectors which is a complex numbers and intermediateresults are shown in following Figureure16 and 17.

Figure 16 Real part steering matrix

16


10976

Figure17 Imaginary part steering matrix

Figure 18 Real part of received signal matrix

Figure 19 Imaginary part of received signal matrix

Figure 19 Imaginary part of received signal matrix. Figure.20Transposed real part of received signal matrix

17


10977

Figure.21 Transposed imaginary part of received signal matrix

Figure.22 conjugate imaginary part of received signal matrix

Figure.23 correlation of received signal matrix

Figure24. Intermediate result of Eigen decomposition

18


10978

Figure.25 Intermediate result of Eigen decomposition

Figure 20 and Figure 21 has shown the transposed Real and Imag-inary part of the received signal and Figure 22 shows the outputbeen obtained by taking conjugate for the transposed matrix. Boththe real and imaginary received signal have been transposed andconjugate the transposed imaginary received signal matrix to ob-tain the Hermitian matrix. Figure 23 has shown real and imaginarypart correlation matrix which has been obtained by the multiplica-tion of received signal matrix with the Hermitian of received signalmatrix. Figure 24 and 25 have shown the intermediate results offinding the cubic root equation in order to obtain the eigen valuesfrom the correlation matrix.

7 CONCLUSION

paper, it has been concluded that MUSIC algorithm has good con-sistency and resolution for non-coherent signals and good statisticalaccuracy for finding direction of arrival . It is inherent to noise andto the focus receiver element on the maximum power direction .DOA estimation has played an important role in array signal pro-cessing, and has a wide range of applications. In many areas, suchas communication, radar, sonar, weather forecasting, ocean andgeological exploration, seismic survey and biomedicine, DOA esti-mation problems may occur. The key to DOA estimation has to usean antenna signal array which is located in different spatial regionsto receive signals from signal sources in different directions. Thenthe use of modern signal processing methods may quickly and accu-rately estimate the direction of the signal sources. In recent years, avariety of DOA estimation algorithms has achieved fruitful results,

19


10979

which provides a solid theoretical foundation for practical applica-tion. In this work, it has been concluded that MUSIC algorithmhas good consistency and resolution for non-coherent signals andgood statistical accuracy for finding direction of arrival . It has aninherent capability to noise and to focus receiver element on themaximum power direction. In the future work, the eigen values ob-tained will be sorted form largest to smallest in order to determinethe noise subspace EN. Noise subspace will be used for calculatingthe Pseudo power spectrum of various angles. When the desired di-rection of arrival angles has been determined, beamsteering processwill takes place which assign weights to the antenna data based onthe maximum peak power.

References

[1] Mahmound Mohanna, Mohamed L.Rabeh , Emad M.Zieur,Sherif Hekala , Optimazation of MUSIC algorithm for angel ofarrival estimation in wireless communications, NRIAG Journalof Astronmy and Geophysics,2013.

[2] of Arrival Estimation of Ultrasonic Waves,American Journalof Signal Processing 2014,4(2):49-59

[3] Lalitha Gupta ,R P Singh,Direction of Arrival Estima-tion,International Journal of Advanced Engineering Technol-ogy.

[4] Anwesha Dhar, Anupama Senapati,Jiberdu Sekhar Roy ,Di-rection of Arrival Estimation in Smart Antenna Using-MUSIC AND Improved MUSIC algorithm at Noisy Envi-ronment,International Journal of Microwave Applications,vol5,No.2,March-April 2016

[5] D. G. Manolakis, V. K. Ingle, and S. M. Kogon, Statistical andAdaptive Signal Processing,McGraw-Hill, 2005.

[6] Vida Vakilian,Digital Beamforming implementation ofswitched beam smart antenna system by using integrateddigital signal processor and field programmable gate array.

20


10980

[7] S. R. Saunders, Antennas and Propagation for Wireless Com-munication Systems, 2nEd., John Wiley Sons, 2007.

[8] J. Boccuzzi, Signal Processing for Wireless Communications,McGraw-Hill, 2007.

[9] M. A. Richards, Fundamentals of Radar Signal Processing,McGraw-Hill, 2005.

[10] R. E. Blabut, W. Millerm, and C. H. Wilcox, Radar and Sonar,Part I, Springer-Verlag, New York, 1991.

[11] C. S. Raghavendra, K. M. Sivalingam, and T. Znati, WirelessSensor Networks,Springer Science, 2004.

[12] P. S. Naidu, Sensor Array Signal Processing, 2nd Ed.,CRC,2009.

[13] J. Li and P. Stoica, Robust adaptive beamforming, John Wiley,Inc. Hoboken, New Jersey, 2006.

[14] J. C. Liberti, Jr. and T. S. Rappaport, Smart Antennas forWireless Communications:IS-95 and Third Generation CDMAApplications, Prentice Hall PTR, 1999.

[15] Y. Zou, Z. L. Yu, and Z. Lin, A robust algorithm for linearlyconstrained adaptive beamforming, IEEE Signal ProcessingLetters, vol. 11, pp. 26-29, Jan. 2004.

21


10981

SYNTHESIS OF ANALOG AND STEERINGmance of smart antenna in mobile network depends on the accu-racy of...

Documents

Transcript of SYNTHESIS OF ANALOG AND STEERINGmance of smart antenna in mobile network depends on the accu-racy of...