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Karbasi, S., Naghsh, M., Masjedi, M., bastani, M. (2019). Transmit Code Design for Detection of an Extended Target Embedded in Signal-Dependent Interference. Electronic and Cyber Defense, 6(2), 59-71.
seyed mohammad Karbasi; Mohammad Mahdi Naghsh; Maryam Masjedi; mohammad hasan bastani. "Transmit Code Design for Detection of an Extended Target Embedded in Signal-Dependent Interference". Electronic and Cyber Defense, 6, 2, 2019, 59-71.
Karbasi, S., Naghsh, M., Masjedi, M., bastani, M. (2019). 'Transmit Code Design for Detection of an Extended Target Embedded in Signal-Dependent Interference', Electronic and Cyber Defense, 6(2), pp. 59-71.
Karbasi, S., Naghsh, M., Masjedi, M., bastani, M. Transmit Code Design for Detection of an Extended Target Embedded in Signal-Dependent Interference. Electronic and Cyber Defense, 2019; 6(2): 59-71.

Transmit Code Design for Detection of an Extended Target Embedded in Signal-Dependent Interference

رادار
Article 6, Volume 6, Issue 2 - Serial Number 20, October 2019, Pages 59-71    PDF (1.62 M)
Document Type: Original Article
Authors
seyed mohammad Karbasi1; Mohammad Mahdi Naghsh* 2; Maryam Masjedi2; mohammad hasan bastani1
1Sharif University of Technology
2Isfahan University of Technology
Receive Date: 30 September 2018,  Revise Date: 01 March 2019,  Accept Date: 05 May 2019 
Abstract
We consider the problem of transmit code design to enhance the detection performance of an extended target embedded in clutter. We model the target impulse response (TIR) in two frameworks, either via the product of a deterministic TIR with an unknown reflection factor or as a Gaussian random vector (with known covariance). For both frameworks, we impose either the peak-to-average-power ratio or the similarity constraints on the sought code, separately. In the former framework, the performance of the generalized likelihood-ratio test depends monotonically on the signal-to-interference-plus-noise ratio (SINR) of the detector. Hence, we cope with the code design, maximizing the SINR. The resulting optimization problem is non-convex, and we propose a novel approach to tackle it. In the latter, dependence of the optimal detector’s performance on the metrics is too complex for code design. Consequently, we employ the mutual information between the TIR ensemble and the received echo as the design metric. We devise an iterative method based on majorization-minimization technique to deal with the resulting non-convex constrained problem. We make the proposed method robust to deal with uncertainties about prior knowledge of clutter and interference. Numerical analyses highlight the effectiveness of the proposed methods comparing to their counterparts.
Keywords
Code Design; Detection Performance; Extended Target; Mutual Information; Robust Design
References

   [1]      F. Gini, A. De Maio, and L. Patton, “Waveform Design and Diversity for Advanced Radar Systems,” London, UK: Institution of Engineering and Technology, 2012.

   [2]      M. R. Bell, “Information Theory and Radar Waveform Design,” vol. 39, no. 5, pp. 1578–1597, 1993.

   [3]      A. Aubry, A. De Maio, A. Farina, and M. Wicks, “Knowledge-Aided (Potentially Cognitive) Transmit Signal and Receive Filter Design in                   Signal-Dependent Clutter,” vol. 49, no. 1, pp. 93–117, 2013.

   [4]      A. De Maio, “Polarimetric Adaptive Detection of Range Distributed Targets,” vol. 50, no. 9, pp.    2152–2159, 2002.

   [5]      A. Aubry, A. De Maio, L. Pallotta, and A. Farina, “Radar Detection of Distributed Targets in Homogeneous Interference Whose Inverse Covariance Structure is Defined via
Unitary Invariant Functions,” vol. 61, no. 20, pp. 4949–4961, 2013.

   [6]      S. M. Karbasi, A. Aubry, A. De Maio, and M. H. Bastani, “Robust Transmit Code and Receive Filter Design for Extended Targets in Clutter,” vol. 63, pp. 1965–1976, April 2015.

   [7]      S. Pillai, H. Oh, D. Youla, and J. Guerci, “Optimal
Transmit-Receiver Design in the Presence of Signal Dependent Interference and Channel Noise,” vol. 46, no. 2, pp. 577–584, 2000.

   [8]      R. Romero and N. Goodman, “Waveform Design in
Signal-Dependent Interference and Application to Target Recognition with Multiple Transmissions,” vol. 3, no. 4, pp. 328–340, 2009.

   [9]      F. Yin, C. Debes, and A. M. Zoubir, “Parametric Waveform Design using Discrete Prolate Spheroidal Sequences for Enhanced Detection of Extended Targets,” vol. 60, no. 9, pp. 4525–4536, 2012.

[10]      S. M. Karbasi, M. M. Naghsh, and M. H. Bastani, “Transmit Code Design for Extended Target Detection in the Presence of Clutter,” ICASSP, (Australia), 2015.

[11]      C. Y. Chen and P. P. Vaidyanathan, “MIMO Radar Waveform Optimization with prior Information of the Extended Target and Clutter,” vol. 57, no. 9, pp. 3533–3544, 2009.

[12]      B. Tang, J. Tang, and Y. Peng, “Waveform Optimization for MIMO Radar in Colored Noise: Further Results for Estimation-Oriented Criteria,” vol. 60, pp. 1517–1522, March 2012.

[13]      B. Tang, M. M. Naghsh, and J. Tang, “Relative EntropyBased Waveform Design for MIMO Radar Detection in the Presence of Clutter and Interference,” vol. 63, pp. 3783–3796, July 2015.

[14]      J. R. Guerci, “Cognitive Radar: a Knowledge-aided Fully Adaptive Approach,” in IEEE Radar Conference, pp. 1365–1370, 2010.

[15]      S. M. Kay, “Fundamentals of Statistical Signal Processing,” vol. II: Detection Theory, Upper Saddle River, NJ: Prentice Hall, 1998.

[16]      A. De Maio, S. De Nicola, Y. Huang, S. Zhang, and
A. Farina, “Code Design to Optimize Radar Detection Performance Under Accuracy and Similarity Constraints,” vol. 56, no. 11, pp.       5618–5629, 2008.

[17]      Y. Yang and R. S. Blum, “MIMO Radar Waveform Design Based on Mutual Information and Minimum Mean-Square Error Estimation,” vol. 43, no. 1, pp. 330–343, 2007.

[18]      M. M. Naghsh and M. Modarres-Hashemi, “Exact Theoretical Performance Analysis of Optimum Detector for Statistical MIMO Radars,” vol. 6, pp. 99–111, 2012.

[19]      M. M. Naghsh, M. Soltanalian, P. Stoica, and
M. Modarres-Hashemi, “Radar Code Design for Detection of Moving Targets,” vol. 50, pp.        2762–2778, October 2014.

[20]      J. A. Tropp, I. S. Dhillon, R. W. Heath, and T. Strohmer, “Designing Structured Tight Frames via an Alternating Projection Method,” vol. 51, no. 1, pp. 188–209, 2005.

[21]      W. Ai, Y. Huang, and S. Zhang, “Further Results on RankOne Matrix Decomposition and its Application,” Math. Program, 2008.

[22]      S. M. Karbasi, A. Aubry, V. Carotenuto, M. M. Naghsh, and M. H. Bastani, “Knowledge-based Design of SpaceTime Transmit Code and Receive Filter for a MultipleInput Multiple-Output Radar in Signal-dependent Interference,” vol. 9, pp.        1124–1135, September 2015.

[23]      R. Horn and C. Johnson.Matrix Analysis, “Matrix Analysis,” Cambridge University Press, 2012.

[24]      P. Stoica and Y. Selen, “Cyclic Minimizers, Majorization Techniques, and the Expectation-Maximization Algorithm: a Refresher,” vol. 21, pp. 112 – 114, Jan 2004.

[25]      S. P. Boyd and L. Vandenberghe, “Convex Optimization,” Cambridge University Press, 2004.

[26]      M. Skolnik, “Radar Handbook,” New York: McGraw-Hill, Third ed., 2008.

[27]      M. Grant, S. Boyd, and Y. Ye, “CVX: MATLAB Software for Disciplined Convex Programming,” 2008.

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