Skip to main content
Log in

Transmit and Receive Gain Optimization for Distributed MIMO Radar

Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Distributed compressive sensing (DCS) has been used in multiple-input multiple-output (MIMO) radar system. This application has led to substantial improvements over existing methods in MIMO radar. But there are also some challenges that should be resolved in order to benefit the most from DCS-based MIMO radar, such as radar signal with low signal to noise ratio and optimizing measurement matrix design. In distributed DCS-based MIMO radar context, this paper presents a cognitive mechanism for optimizing transmit and receive gain by applying the optimization guideline which based on the coherence of the sensing matrix (CSM) and signal-to-noise ratio. This paper proposed two kinds of method: the first one is to optimize transmit gain with the aim to maximize SNR, and the second one is to minimize CSM by adjusting receive gain. Simulations show that the proposed methods obtain significant better recovery performance than traditional DCS-based MIMO radar systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Haimovich, A. M., Blum, R. S., & Cimini, L. J. (2008). MIMO radar with widely separated antennas. IEEE on Signal Processing Magazine, 25(1), 116–129.

    Article  Google Scholar 

  2. Li, J., & Stoica, P. (2007). MIMO radar with colocated antennas. IEEE on Signal Processing Magazine, 24(5), 106–114.

    Article  Google Scholar 

  3. Fishler, E., Haimovich, A., Blum, R. S., Cimini, L. J., Chizhik, D., & Valenzuela, R. A. (2006). Spatial diversity in radars-models and detection performance. IEEE Transactions on Signal Processing, 54(3), 823–838.

    Article  Google Scholar 

  4. Baraniuk, R., Steeghs, P., Baraniuk, R., & Steeghs, P. (2007). Compressive radar imaging. In IEEE on Radar conference, 2007 (pp. 128–133). IEEE.

  5. Herman, M., & Strohmer, T. (2008). Compressed sensing radar. In IEEE on radar conference, 2008. RADAR’08 (pp. 1–6). IEEE.

  6. Duarte, M. F., Sarvotham, S., Baron, D., Wakin, M. B., & Baraniuk, R. G. (2005). Distributed compressed sensing of jointly sparse signals. In Asilomar conference on signals, systems, and computers (pp. 1537-1541).

  7. Chen, C. Y., & Vaidyanathan, P. P. (2008). Compressed sensing in MIMO radar. In 42nd Asilomar conference on signals, systems and computers, 2008 (pp. 41–44). IEEE.

  8. Subotic, N. S., Thelen, B., Cooper, K., Buller, W., Parker, J., Browning, J., & Beyer, H. (2008). Distributed RADAR waveform design based on compressive sensing considerations. In IEEE on radar conference, 2008. RADAR’08. (pp. 1–6). IEEE.

  9. Yu, Y., Petropulu, A. P., & Poor, H. V. (2012). Power allocation for CS-based colocated MIMO radar systems. In IEEE 7th Sensor array and multichannel signal processing workshop (SAM), 2012 (pp. 217–220). IEEE.

  10. Yu, Y., Petropulu, A. P., & Poor, H. V. (2011). Measurement matrix design for compressive sensing Cbased mimo radar. IEEE Transactions on Signal Processing, 59(11), 5338–5352.

    Article  MathSciNet  Google Scholar 

  11. Gogineni, S., & Nehorai, A. (2011). Target estimation using sparse modeling for distributed MIMO radar. IEEE Transactions on Signal Processing, 59(11), 5315–5325.

    Article  MathSciNet  Google Scholar 

  12. Zhang, J., Zhu, D., & Zhang, G. (2012). Adaptive compressed sensing radar oriented toward cognitive detection in dynamic sparse target scene. IEEE Transactions on Signal Processing, 60(4), 1718–1729.

    Article  MathSciNet  Google Scholar 

  13. Grant, M., Boyd, S., & Ye, Y. (2008). CVX: Matlab software for disciplined convex programming.

  14. Grant, M. C., & Boyd, S. P. (2008). Graph implementations for nonsmooth convex programs. Recent advances in learning and control (pp. 95–110). London: Springer.

    Chapter  Google Scholar 

  15. Duarte-Carvajalino, J. M., & Sapiro, G. (2009). Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization. IEEE Transactions on Image Processing, 18(7), 1395–1408.

    Article  MathSciNet  Google Scholar 

  16. Stoica, P., He, H., & Li, J. (2009). New algorithms for designing unimodular sequences with good correlation properties. IEEE Transactions on Signal Processing, 57(4), 1415–1425.

    Article  MathSciNet  Google Scholar 

  17. Stoica, P., Li, J., & Zhu, X. (2008). Waveform synthesis for diversity-based transmit beampattern design. IEEE Transactions on Signal Processing, 56(6), 2593–2598.

    Article  MathSciNet  Google Scholar 

  18. Stoica, P., He, H., & Li, J. (2009). On designing sequences with impulse-like periodic correlation. IEEE on Signal Processing Letters, 16(8), 703–706.

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by China NSF Grants (61071163, 61271327,61071164, 61201367 and 61471191), the Fundamental Research Funds for the Central Universities (3082015NP2015504), and partly funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PADA) and the Natural Science Foundation of Jiangsu Province under Grant BK2012382 and Funding of Jiangsu Innovation Program for Graduate Education and the Fundamental Research Funds for the Central Universities under Grant CXZZ12-0155.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gong Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, S., Zhang, G., Zhang, JD. et al. Transmit and Receive Gain Optimization for Distributed MIMO Radar. Wireless Pers Commun 85, 1969–1986 (2015). https://doi.org/10.1007/s11277-015-2885-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-015-2885-1

Keywords

Navigation