Fast Power Allocation Algorithm for Multiple Target Localization in MIMO Radar System

FENG Hanzhe; YAN Junkun; LIU Hongwei

doi:10.11999/JEIT160981

Volume 38 Issue 12

Jan. 2017

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(12): 3219-3223

FENG Hanzhe, YAN Junkun, LIU Hongwei. Fast Power Allocation Algorithm for Multiple Target Localization in MIMO Radar System[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3219-3223. doi: 10.11999/JEIT160981

Citation:

FENG Hanzhe, YAN Junkun, LIU Hongwei. Fast Power Allocation Algorithm for Multiple Target Localization in MIMO Radar System[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3219-3223. doi: 10.11999/JEIT160981

Citation:

PDF( 370 KB)

Fast Power Allocation Algorithm for Multiple Target Localization in MIMO Radar System

doi: 10.11999/JEIT160981

Funds:

The National Natural Science Foundation of China (61601340), The National Science Fund for Distinguished Young Scholars (61525105), The Postdoctoral Science Foundation of China (2015M580817, 2016T90890)

Received Date: 2016-09-29
Rev Recd Date: 2016-11-23
Publish Date: 2016-12-19

Abstract

Abstract

To meet the need of the real application, this paper proposes a power allocation algorithm for multiple target localization, which tries to get the quick optimal allocation of the limited power resources in the MIMO radar. Firstly, Cramr-Rao Lower Bound (CRLB) of the Mean Square Error (MSE) of the multi-target localization is given, and CRLB is used as a cost function to allocate the power resource. Then, an Alternating Global Optimal Algorithm (AGOA) is designed which can be used in power allocation of multi-target localization, the related Pareto sets to achieve the fast allocation of the power resources. Finally, the simulation results show that the AGOA can quickly achieve the optimal allocation of the limited power allocation in MIMO radar, and can significantly enhance the precision of the multiple target localization.
- MIMO radar,
- Cramr-Rao Lower Bound (CRLB),
- Pareto optimal set,
- Alternating Global Optimal Algorithm (AGOA)

FullText(HTML)

References(19)

References

GODRICH H, HAIMOVICH A M, and BLUM R S. Target localization accuracy gain in MIMO radar based system [J], IEEE Transactions on Information Theory, 2010, 56(6): 2783-2803.

VAN TREES H L and BELL K L. Bayesian Cramer-Rao bounds for multistatic radar[C]. Proceedings of Waveform Diversity Design, Orlando, FL, USA, 2007: 856-859.

GODRICH H, PETROPULU A P, and POOR H V. Cluster allocation schemes for target tracking in multiple radar architectures[C]. Proceeding of Signals, Systems and Computers, Princeton, NJ, USA, 2011, 863-867.

GODRICH H, PETROPULU A P, and POOR H V. Resource allocation schemes for target localization in distributed multiple radar architectures[C]. Proceedings of Signal Processing, Aalborg, Denmark, 2010: 23-27.

GODRICH H, PETROPULU A, and POOR H V. Power allocation strategies for target localization in distributed multiple-radar architecture[J]. IEEE Transactions on Signal Processing, 2011, 59(7): 3226-3240.

严俊坤, 刘宏伟, 戴奉周, 等. 基于非线性机会约束规划的多基雷达系统稳健功率分配算法[J]. 电子与信息学报, 2014, 36(3): 509-515. doi: 10.3724/SP.J.1146.2013.01189.

YAN Junkun, LIU Hongwei, and DAI Fengzhou, et al. Nonlinear chance constrained programming based robust power allocation algorithm for multistatic radar systems[J]. Journal of Electronics Information Technology, 2014, 36(3): 509-515. doi: 10.3724/SP.J.1146.2013.01189.

时晨光, 汪飞, 周建江, 等. 基于低截获概率优化的组网雷达系统最优功率分配算法[J]. 雷达学报, 2014, 3(4): 465-473.

SHI Chenguang, WANG Fei, and ZHOU Jianjiang, et al. Optimal power allocation algorithm for radar network systems based on low probability of intercept optimization[J]. Journal of Radars, 2014, 3(4): 465-473.

GARCIA N, HAIMOVICH A M, COULON M, et al. Resource allocation in MIMO radar with multiple targets for non-coherent localization[J]. IEEE Transactions on Signal Processing, 2014, 62(10): 2656-2666.

HERO A O and COCHRAN D. Sensor management: Past, present, and future[J]. IEEE Sensors Journal, 2011, 11(12): 3064-3075.

VAN TREESH L. Detection, Estimation, and Modulation Theory, Part III[M]. New York, NY: John Wiley and Sons, 1971: 275-352.

STOICA P and SELN Y. Cyclic minimizers, majorization techniques, and expectation-maximization algorithm: A refresher[J] IEEE Signal Processing Magazine, 2004, 21(1): 112-114.

GODRICH H, PETROPULU A, and POOR H V. A combinatorial optimization framework for subset selection in distributed multiple-radar architecture[C]. Proceedings of Acoustics, Speech and Signal Processing, Piscataway, NJ, USA, 2011: 2796-2799.

张娟, 赵永红, 张林让, 等. 网络化雷达协同抗干扰发射功率分配方法[P]. CN103941238A. 2014.

LIN Jiguan. Multiple-objective problems: Pareto-optimal solutions by method of proper equality constraints[J]. IEEE Transactions on Automatic Control, 1976, 21(5): 641-650.

KIM I Y and DE WECK O L. Adaptive weighted-sum method for bi-objective optimization: Pareto front generation [J]. Structural and Multidisciplinary Optimization, 2005, 29(2): 149-158.

KAO H Y, CHAN C Y, and WU D J. A multi-objective programming method for solving network DEA[J]. Applied Soft Computing, 2014, 24: 406-413.

EHRGOTT M and WIECEK M M. Multiple Criteria Decision Analysis: State of the Art Surveys[M]. New York, NY: Springer 2005: 667-708.

Relative Articles

Supplements(0)

Cited By

Proportional views