Document Type : Research Article
Authors
1 Department of Electrical and Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
2 Department of Applied Mathematics, University of Science and Technology of Mazandaran, Behshahr, Iran..
Abstract
This paper addresses the challenges of power control, radar assignment, and signal timing to improve the detection and tracking of multiple targets within a mono-static cognitive radar network. A fusion center is utilized to integrate target velocity data gathered by radars. The primary objective is to minimize the mean square error in target velocity estimation while adhering to constraints related to global detection probability and total radar power consumption for effective target detection and tracking. The optimization problem is formulated and a low-complexity method is proposed using the genetic algorithm (GA). In this approach, the radars and their transmission powers are represented as chromosomes and the network's quality of service (QoS) requirements serve as inputs to the GA. The output of the GA is the mean error square of the target velocity estimation. Once the problem is resolved, the power allocation for each radar assigned to a specific target is determined. Simulation results demonstrate the effectiveness of the proposed algorithm in enhancing detection performance and improving tracking accuracy when compared to other benchmark algorithms.
Highlights
- Introduced a novel algorithm that optimally sets the signal duration, transmission power, and radar-target assignments to enhance target detection and velocity estimation accuracy.
- Formulated the resource allocation problem to minimize target velocity estimation MSE under constraints of detection performance and power budget.
- Employed a genetic algorithm to derive an approximate optimal solution, effectively addressing the non-convex and NP-hard characteristics of the problem.
- Simulation results showcase the algorithm's superior detection and tracking accuracy performance compared to existing benchmark methods.
Keywords
Main Subjects
[2] Boyd, S., and Vandenberghe, L.(2004). “Convex optimization”, Cambridge: Cambridge University Press.
[3] Chavali, Ph., and Nehorai, A. (2012). “Scheduling and power allocation in a cognitive radar network for multiple-target tracking”, IEEE Transactions on Signal Processing. 60. 715-729.
[4] Garcia, N., Haimovich, A.M., Coulon, M. and Lops, M., (2013). “Resource allocation in MIMO radar with multiple targets for non-coherent localization”, IEEE Transactions on Signal Processing, 62(10), 2656-2666.
[5] Geng, Zh., Wang, B., Yan, H., Zhang, J., and Zhu, D. (2022). “Moving target detection and tracking with multiplatform radar network (MRN)”, IET Radar, Sonar & Navigation, 16(5), 815-824.
[6] Godrich, H., Petropulu, A.P., and Poor, H.V. (2011). “Power allocation strategies for target localization in distributed multiple-radar architectures”, IEEE Transactions on Signal Processing, 59(7), 3226-3240.
[7] Goldberg, D.E. (1989). “Genetic algorithm in search optimization and learning machine”, Addison Wesley.
[8] He, Q., Blum, R., and Godrich, H. (2010). “Target velocity estimation and antenna placement for MIMO radar with widely separated antennas”, IEEE Journal of Selected Topics in Signal Processing, 4, 79-100.
[9] He, Q., Blum, R.S., Godrich, H., and Haimovich, A.M. (2010). “Target velocity estimation and antenna placement for MIMO radar with widely separated antennas”, IEEE Journal of Selected Topics in Signal Processing, 4(1), 79-100.
[10] Hosseini Andargoli, S.M., and Malekzadeh, J. (2019). “LPI radar network optimization based on geometrical measurement fusion”, Optimization and Engineering Journal, 20(1), 119-150.
[11] Jin, B., Kuang, X., Liu, Sh., Zhang, Zh., and Lian, Zh. (2023). “Joint allocation of transmit power and signal bandwidth for distributed cognitive tracking radar network using cooperative game”, Digital Signal Processing, 135.
[12] Jin, B., kuang, X., Peng, Y., Zhang, Zh., Wang, B., Li, S., and Lian, Zh. (2022). “Distributed power allocation for cognitive tracking based on non-cooperative game in decentralized netted radar”, Digital Signal Processing, 126.
[13] Kurdzo, J.M., and Palmer, R.D. (2011). “On the use of genetic algorithms for optimization of a multi-band, multi-mission radar network”, IEEE Radar Conference, 231-236.
[14] Li, F., Gao, X., and Li, B. (2014). “A study on search strategies of netted surveillance radar”, In: International Conference on Logistics Engineering, Management and Computer Science, LEMCS 2014, Atlantis Press.
[15] Niu, R.X., Blum, R.S., Varshney, P.K., and Drozd, A.L. (2012). “Target localization and tracking in noncoherent multiple-input multiple-output radar systems”, IEEE Transactions on Aerospace and Electronic Systems, 48(2), 1466-1489.
[16] Richards, M.A., and Holm, W.A. (2010). “Principles of modern radar: Basic principles”, Science and Technology Publishing.
[17] Shi, C., Wang, F., Sellathurai, M., and Zhou, J. (2014). “LPI optimization framework for target tracking in radar network architectures using information-theoretic criteria”, International Journal of Antennas and Propagation, 21, 1-10.
[18] Skolnik, M.I. (1962). “Introduction to radar, in: Radar Handbook”, 2nd Edition, McGraw-Hill Publishing Company, New York, 1990.
[19] Song, X., Zheng, N., Yan, Sh., and Li, H. (2018). “A joint resource allocation method for multiple targets tracking in distributed MIMO radar systems”, EURASIP Journal on Advances in Signal Processing, 65, 1-12.
[20] Tichavsky, P., Muravchik, C.H., and Nehorai, A. (1998). “Posterior Cramer-Rao bounds for discrete time nonlinear filtering”, IEEE Transactions on Signal Processing, 46, 1386-1396.
[21] Xie, M., Yi, W., and Kong, L. (2016). “Joint node selection and power allocation for multitarget tracking in decentralized radar networks”, 19th International Conference on Information Fusion, Heidelberg, Germany.
[22] Xie, M., Yi, W., Kirubarajan, Th., and Kong, L. (2018). “Joint node selection and power allocation strategy for multitarget tracking in decentralized radar networks”, IEEE Transactions on Signal Processing, 66(3), 729-743.
[23] Yan, J., Liu, H., and Bao, Zh. (2018). “Power allocation scheme for target tracking in clutter with multiple radar system”, Elsevier Signal Processing Journal, 144, 453-458.
[24] Yan, J.K., Liu, H.W., Jiu, B., Chen, B., Liu, Z., and Bao, Z. (2015). “Simultaneous multi beam resource allocation scheme for multiple target tracking”, IEEE Transactions on Signal Processing, 63(12), 3110-3122.
[25] Zhu, P., Liang, J., Luo, Z., and Shen, X. (2023). “Cognitive radar target tracking using intelligent waveforms based on reinforcement learning”, IEEE Transactions on Geoscience and Remote Sensing, 61.
)