Measurement matrix design for compressive sensing-based MIMO radar

IEEE Transactions on Signal Processing

Volumn 59, Issue 11, 2011, Pages 5338-5352

  Yu, Yao ^a   Petropulu, Athina P ^a   Poor, H Vincent ^b  

^a RUTGERS UNIVERSITY   (United States)

^b Princeton University   (United States)

Author keywords

Compressive sensing; direction of arrival (DOA) estimation; measurement matrix; multiple input multiple output (MIMO) radar

Indexed keywords

BASIS MATRIX; CO-LOCATED; COMPRESSIVE SENSING; DETECTION ACCURACY; DETECTION PERFORMANCE; DIRECTION OF ARRIVAL (DOA) ESTIMATION; FUSION CENTER; GAUSSIAN MEASUREMENTS; GAUSSIANS; LINEAR COMBINATIONS; LOCALIZATION PERFORMANCE; MATRIX; MEASUREMENT MATRIX; MULTIPLE-INPUT MULTIPLE-OUTPUT (MIMO) RADAR; MULTIPLE-INPUT MULTIPLE-OUTPUT RADARS; OPTIMAL MEASUREMENTS; OPTIMALITY CRITERIA; OPTIMIZATION PROBLEMS; RANDOM MEASUREMENT; RECEIVED SIGNALS; RECOVERY PERFORMANCE; SIGNAL-TO-INTERFERENCE RATIO; TARGET INFORMATION; WAVE FORMS;

DIRECTION OF ARRIVAL; LINEAR TRANSFORMATIONS; MEASUREMENTS; MIMO RADAR; MIMO SYSTEMS; OPTIMIZATION; SIGNAL DETECTION; SIGNAL RECONSTRUCTION; TRANSFER MATRIX METHOD;

RADAR MEASUREMENT;

EID: 80054065922     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2011.2162328     Document Type: Article

References (36)

1. E. Fishler, A. Haimovich, R. Blum, D. Chizhik, L. Cimini, and R. Valenzuela, "MIMO radar: An idea whose time has come," in Proc. IEEE Radar Conf., Philadelphia, PA, Apr. 2004, pp. 71-78.
2. L. Xu, J. Li, and P. Stoica, "Radar imaging via adaptive MIMO techniques," in Proc. Eur. Signal Process. Conf., Florence, Italy, Sep. 2006.
3. J. Li, P. Stoica, L. Xu, andW. Roberts, "On parameter identifiability of MIMO radar," IEEE Signal Process. Lett., vol. 14, no. 12, pp. 968-971, Dec. 2007.
4. A. M. Haimovich, R. S. Blum, and L. J. Cimini, "MIMO radar with widely separated antennas," IEEE Signal Process. Mag., vol. 25, no. 1, pp. 116-129, Jan. 2008.
5. H. Godrich, A. M. Haimovich, and R. S. Blum, "Target localization accuracy gain in MIMO radar based system," IEEE Trans. Inf. Theory, vol. 56, no. 6, pp. 1-21, Jun. 2010.
6. Q. He, R. S. Blum, H. Godrich, and A.M. Haimovich, "Target velocity estimation and antenna placement for MIMO radar with widely separated antennas," IEEE J. Sel. Topics Signal Process., vol. 4, no. 1, pp. 79-100, Feb. 2010.
7. P. Stoica and J. Li, "MIMO radar with colocated antennas," IEEE Signal Process. Mag., vol. 24, no. 5, pp. 106-114, Sep. 2007.
8. C. Chen and P. P. Vaidyanathan, "MIMO radar space-time adaptive processing using prolate spheroidal wave functions," IEEE Trans. Signal Process., vol. 56, no. 2, pp. 623-635, Feb. 2008.
9. D. V. Donoho, "Compressed sensing," IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.
10. E. J. Candes, "Compressive sampling," in Proc. Int. Congress of Math., Madrid, Spain, Aug. 2006, pp. 1433-1452.
11. E. J. Candes and M. B. Wakin, "An introduction to compressive sampling (A sensing/sampling paradigm that goes against the common knowledge in data acquisition)," IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21-30, Mar. 2008.
12. J. Romberg, "Imaging via compressive sampling (Introduction to compressive sampling and recovery via convex programming)," IEEE Signal Process. Mag., vol. 25, no. 2, pp. 14-20, Mar. 2008.
13. E. J. Candes, J. K. Romberg, and T. Tao, "Stable signal recovery from incomplete and inaccurate measurements," Commun. Pure and Appl. Math., vol. 59, no. 8, pp. 1207-1223, Aug. 2006.
14. M. Elad, "Optimized projections for compressed sensing," IEEE Trans. Signal Process., vol. 55, no. 12, pp. 5695-5702, Dec. 2007.
15. J. M. Duarte-Carvajalino and G. Sapiro, "Learning to sense sparse signals: Simultaneous sensingmatrix and sparsifying dictionary optimization," IEEE Trans. Image Process., vol. 18, no. 7, pp. 1395-1408, Jun. 2009.
16. G. Tang and A. Nehorai, The -Constrained Minimal Singular Value: A Computable Quantification of the Stability of Sparse Signal Reconstruction [Online]. Available: http://arxiv.org/abs/1004.4222v1
17. G. Tang andA.Nehorai, Verifiable and Computable Performance Evaluation of Sparse Signal Recovery, unpublished.
18. S. Sen, G. Tang, and A. Nehorai, "Multi-objective optimized OFDM radar waveform for target detection in multipath scenarios," IEEE Trans. Signal Process., vol. 59, no. 2, pp. 639-652, Feb. 2011.
19. R. Baraniuk and P. Steeghs, "Compressive radar imaging," in Proc. IEEE Radar Conf., Boston, MA, Apr. 2007, pp. 128-133.
20. A. C. Gurbuz, J. H.McClellan, andW. R. Scott, "Compressive sensing for gpr imaging," in Proc. 41th Asilomar Conf. Signals, Syst. Comput., Pacific Grove, CA, Nov. 2007, pp. 2223-2227.
21. M. A. Herman and T. Strohmer, "High-resolution radar via compressed sensing," IEEE Trans. Signal Process., vol. 57, no. 6, pp. 2275-2284, Jun. 2009.
22. M. Herman and T. Strohmer, "Compressed rensing radar," in Proc. IEEE Int. Conf. Acoust. Speech Signal Process, Las Vegas, NV, Mar.-Apr. 2008, pp. 2617-2620.
23. A. P. Petropulu, Y. Yu, and H. V. Poor, "Distributed MIMOradar using compressive sampling," in Proc. 42nd Asilomar Conf. Signals, Syst. Comput., Pacific Grove, CA, Nov. 2008, pp. 203-207.
24. C. Y. Chen and P. P. Vaidyanathan, "Compressed sensing in MIMO radar," in Proc. 42nd Asilomar Conf. Signals, Syst. Comput., Pacific Grove, CA, Nov. 2008, pp. 41-44.
25. T. Strohmer and B. Friedlander, "Compressed sensing for MIMO radar-algorithms and performance," in Proc. 43rd Asilomar Conf. Signals, Syst. Comput., Pacific Grove, CA, Nov. 2009, pp. 464-468.
26. Y. Yu, A. P. Petropulu, and H. V. Poor, "MIMO radar using compressive sampling," IEEE J. Sel. Topics in Signal Process., vol. 4, no. 1, pp. 146-163, Feb. 2010.
27. Y. Yu, A. P. Petropulu, and H. V. Poor, "Compressive sensing for MIMO radar," in Proc. IEEE Int. Conf. Acoust. Speech Signal Process., Taipei, Taiwan, Apr. 2009, pp. 3017-3020.
28. Y. Yu, A. P. Petropulu, and H. V. Poor, "CSSF MIMO radar: Low-complexity compressive sensing based MIMO radar that uses step frequency," IEEE Trans. Aerosp. Electron. Syst., 2011, to be published.
29. E. J. Candes and T. Tao, "The Dantzig selector: Statistical estimation when is much larger than ," Ann. Statist., vol. 35, no. 6, pp. 2313-2351, Dec. 2007.
30. W. Dai and O. Milenkovic, "Subspace pursuit for compressive sensing signal reconstruction," IEEE Trans. Inf. Theory, vol. 55, no. 5, pp. 2230-2249, May 2009.
31. D. Needell and J. Tropp, "CoSaMP: Iterative signal recovery from incomplete and inaccurate samples," Appl. Comput. Harmon. Anal., vol. 26, no. 3, pp. 301-321, May 2009.
32. G. Tang and A. Nehorai, "Performance analysis for sparse support recovery," IEEE Trans. Inf. Theory, vol. 56, no. 3, pp. 1383-1399, Mar. 2009.
33. J. A. Tropp, "Greed is good: Algorithmic results for sparse approximation," IEEE Trans. Inf. Theory, vol. 50, no. 10, pp. 2231-2242, Oct. 2004.
34. H.Ochiai, P. Mitran, H. V. Poor, andV. Tarokh, "Collaborative beamforming for distributed wireless ad hoc sensor networks," IEEE Trans. Signal Process., vol. 53, no. 11, pp. 4110-4124, Nov. 2005.
35. N. Levanon and E. Mozeson, Radar Signals. Hoboken, NJ: Wiley, 2004.
36. A. Hassanien and S. A. Vorobyov, "Why the phased MIMO radar outperforms the phased-array and MIMO radars," in Proc. 18th Eur. Signal Process. Conf., Aalborg, North Denmark, Aug. 2010, pp. 1234-1238.