Design of a Demonstrator of an Adaptive
steerable Antenna system for removal of
Interference, clutter, Jammer based on
AWG &VSA
M Chakraborty1
mithunchakraborty03@gmail.com
R Adhikary2
rabi.kolkata@gmail.com
P Karmakar3
1,2,3
Dept. of ECE, Surendra Institute of Engineering & management, Siliguri, India p.karmakar87@gmail.com
D kandar4
4
Dept of CSE, SKP Engineering College, Tamilnadu, India dkandar@rediffmail.com
B Maji5
5
Dept. of ECE, National Institute of Technology, Durgapur, India. bmajiecenit@yahoo.com
Abstract:
Interfering, clutter and jamming systems are becoming an increasing concern to the military and security industries worldwide. To overcome these problems phase array antennas and adaptive beam forming systems offers a potential solution. STAP is an application of optimum and adaptive array processing algorithms to the radar problem of target detection in ground clutter and interference with pulse-Doppler waveforms.In this paper we have presented our work of designing a MVDR beam former receiver using AWG and VSA. The minimum variance distortion less response (MVDR) approach is very popular technic in array processing that generates some mean square error or values. When these values fed to the phase array antenna system results in electronic steering of antenna beam according to the weight vector generated.
Keywords: STAP, AWG, VSA, MVDR
I. INTRODUCTION
Fig 1: Airbone RADAR
As a representative example of a High Performance Embedded Computing System [8] for detecting targets buried in jamming and clutter interference [12] the above figure illustrates the target and interference scenario [14], multiple channels are input into the real-time signal processor to focus the energy in the direction of the target while simultaneously canceling the intentional jamming interference and the clutter interference generated from the motion of the aircraft. The properties exploited to make the target extraction from these high levels of interference are angle and Doppler. These properties have led to this technique’s common reference as space-time adaptive processing [13]. One important fact we must face in practice is that we do not have the interference covariance matrix,Ri, n which would require infinite number of samples. However, we can estimate it.
A sampledinterference covariance matrix [10] is
(1)
K is the total number of training samples available and, xi,n(k)is the kth training sample. The sampled covariance matrix is the maximum likelihood estimate of the true covariance matrix Ri,n. With a sampled covariance matrix the weight vector equation is given below
(2)
Likewise its most general form is
(3)
II. IMPLEMENTATION
Our group was motivated by the fact that we could have collect the samples of environment containing fixed target priory placed in an open space using 2 x 2 MIMO antenna, by sending two consecutive pulses. Then process the received data in a STAP based receiver to place all the interference and noise in the NULL position and form a pencil like main beam in the direction of Target position. But due to the lack of RF front end hardware we’ve approached the
Fig 2: Ch1 & Ch2 Transmitted DATA (1024 x 1) of AWG
problem in a different way. We’ve simulated the environment to generate the total interference covariance matrix containing Target, Clutter, & Interference and transmitted those data through Arbitrary waveform Generator using 2 transmit channels in a Hardware in the loop set up consisting of Arbitrary Waveform Generator & Vector signal analyzer.
We’ve successfully received those data in the VSA shown in figure 3 above.Upon receiving the data, we’ve proposed a STAP based Receiver model designed in MATLAB/Simulink shown in fig 4,
Fig 5: Beamformer receiver with VSA Sink API connected
The model when ported in VSA calculated the MVDRvalues and minimizes the Mean square error and by applying those weight vectors to thephased array antenna system makes the steering of theantenna main beam and Null.
Fig 6: generated Jammer Covariance matrix
Fig 8: Plot of Generated CLUTTER Covariance Matrix
III. RESULTS & ANALYSIS
Fig 9: The Instantaneous MMSE values generated by the STAP Beamformer observed in Trace B of the VSA sink
Fig 10: The Instantaneous MMSE values generated by the STAP Beamformer and corresponding Spectrum seen at Trace A.
After Doppler filtering & passing through the Equalized channels the Weight Vectors were generated which used for Adaptation, are generating the Mean square Errors. From the generated MMSE values it is seen that as the environment is very random in nature.
References
[1] Haynes, T; “A Primer on Digital Beam forming” Spectrum Signal Processing, http://www.spectrumsignal.com, March 26, 1998. [2] Axelsson S; Saab Bofors Dynamics and Swedish Defence Research Agency “Position correction of moving targets in SAR-imagery” ,
Linköping, Sweden, SAR Image Analysis, Modeling, and Techniques VI, edited by Francesco Posa, Proceedings of SPIE Vol. 5236 (SPIE, Bellingham, WA, 2004) · 0277-786X/04/$15 · doi: 10.1117/12.511213
[3] Walke R, “Adaptive Beamforming using QR in FPGA”, Sixth Annual HPEC Workshop, Lincoln Laboratory, MIT, 24–26 September 2002
[4] Veen B D V, Buckley K M.,” Beamforming: A versatile approach to spatial filtering”, IEEE ASSP Magazine, April’1998.
[5] Vouras P & Freburger B, “Application of Adaptive Beamforming Techniques to HF Radar”, Radar Conference, 2008. RADAR'08. IEEE, Date: 26- 30May’2008, page: 1 – 6.
[6] Fabrizio G, Holdsworth D, Farina A, “Experimental HF Radar Trial of Real- Time STAP,” Proc. International Waveform Diversity and Design Conference, Pisa, Italy, June 4-8 2007.
[7] Bürger W ,” Space-Time Adaptive Processing: Algorithms”, FHR, FGAN, Germany, 2006 Advanced Radar Signal and Data Processing (pp. 7-1 – 7-12). Educational Notes RTO-EN-SET-086, Paper 7.
[8] Martinez D R, Bond R A, Vai M M, “High performance embedded computing hand book – a system perspective”, MIT Lincoln Laboratory.
[9] Torres J A, Davis R M, Kramer J D R, Fante R L, “Efficient Wideband Jammer Nulling When Using Stretch Processing,” IEEE Trans. Aerospace and Electronic Systems, vol. 36, pp. 1167-1178, Oct.2000.
[10] Neyt X, Druyts P, Acheroy M, “Structured covariance matrix estimation for the range-dependent problem in STAP”, Proceedings ARP 07, The Fourth IASTED International Conf., 2007. Haynes, T; “A Primer on Digital Beam forming” Spectrum Signal Processing, http://www.spectrumsignal.com, March 26, 1998.
[11] Laseetha T S J, Sukanesh R. , “Robust Adaptive Beamformers Using Diagonal Loading”, Journal of Selected Areas in Telecommunications (JSAT), March Edition, 2010
[12] Melvin W L, “Space-time adaptive radar performance in heterogeneous clutter,” IEEE Trans. AES, Vol.36, No. 2, April 2000, pp. 621-633.
[13] Walke R, “Adaptive Beamforming using QR in FPGA”, Sixth Annual HPEC Workshop, Lincoln Laboratory, MIT, 24–26 September 2002
[14] Ke Sun, Hao Zhang, Gang Li, Huadong Meng, Xiqin Wang, “Airborne Radar STAP using Sparse Recovery of Clutter Spectrum”, White paper 2010.
[15] Raviraj Adve1, Todd Hale, and Michael Wicks “Knowledge Based Adaptive Processing for Ground Moving Target Indication”, Research Associates for Defense Conversion Inc., Marcy, NY and Air Force Research Laboratory under contract F-30602-97-C-0006. [16] Douglas A. Page, Braham Himed, Mark E. Davis“A STAP Approach for Bistatic Space-Based GMTI Radar”, Adaptive Sensor Array
