Optimizing distributed detection thresholds for multistatic radar systems
DOI:
https://doi.org/10.54939/1859-1043.j.mst.99.2024.12-23Keywords:
Multistatic radar; Distributed Detection; Particle Swarm Optimization (PSO).Abstract
In this paper, we propose an approach for optimizing distributed detection thresholds for multi-static radar systems. Under the Neyman-Pearson criterion, local detection thresholds are optimized using the Particle Swarm Optimization (PSO) algorithm. The local thresholds are optimized to maximize the overall detection probability under the constraint of a given overall false alarm probability. The advantages of PSO include its simplicity, few parameters, and efficient global search. Numerical simulation examples are provided for a radar system comprising two local stations for the distributed detection of targets in Gaussian clutter. The results indicate that the OR rule consistently serves as the optimal fusion rule, and thresholds are optimized flexibly to maintain the overall detection performance despite heterogeneous changes in the signal-to-clutter power ratio (SCR) at local stations. In some cases, local threshold optimization can be omitted without significant reduction in the overall system detection performance.
References
[1]. Chernyak Victor S, “Fundamentals of multisite radar systems: multistatic radars and multiradar systems,” Routledge (2018).
[2]. E. Conte et al, “Multistatic radar detection: synthesis and comparison of optimum and suboptimum receivers,” IEE Proc. F - Commun., Radar and Signal Process., vol. 130, no. 6, pp. 484–494, (1983).
[3]. E. D’Addio et al: “Optimum and sub-optimum processors for multistatic radar systems,” in Riv. Tec. Selenia, vol. 8, no. 2, pp. 21–28,, (1982).
[4]. R. Srinivasan, “Distributed radar detection theory,” IEE Proc. F - Commun., Radar and Signal Process., vol. 133, no. 1, pp. 55–60, (1986).
[5]. R. R. Tenney and N. R. Sandell, “Detection with distributed sensors,” IEEE Trans. Aerosp. Electron. Syst., vol. AES-17, no. 4, pp. 501–510, (1981).
[6]. Z. Chair and P. K. Varshney, “Optimal data fusion in multiple sensor detection systems,” IEEE Trans. Aerosp. Electron. Syst., vol. AES-22, no. 1, pp. 98–101, (1986).
[7]. I. Y. Hoballah and P. K. Varshney, “Neyman-Pearson detection with distributed sensors,” in Proc. 25th IEEE Conf. Decis. Control, vol. 25, pp. 237–241, (1986).
[8]. P. K. Varshney, “Distributed Detection and Data Fusion”. New York, NY, USA: Springer, (2012).
[9]. R. Viswanathan et al, “On counting rules in distributed detection,” IEEE Trans. Acoust., Speech, Signal Process., vol. 37, no. 5, pp. 772–775, (1989).
[10]. R. Niu and P. K. Varshney, “Distributed detection and fusion in a large wireless sensor network of random size,” EURASIP J. Wireless Commun. Netw., vol. 2005, no. 4, pp. 462–472, (2005).
[11]. R. Niu, P. K. Varshney, and Q. Cheng, “Distributed detection in a large wireless sensor network,” Inf. Fusion, vol. 7, no. 4, pp. 380–394, (2006).
[12]. V. Aalo et al, “On distributed detection with correlated sensors: two examples,” IEEE Trans. Aerosp. Electron. Syst., vol. 25, no. 3, pp. 414–421, (1989).
[13]. Nguyen Duc Minh, Bui Thi Dan, and Pham Van Hung, "The effect of correlated noise with student-t distribution to detection quality of distributed multi-location radar network", Journal of Military Science and Technology, vol. 56, pp. 68–75, (2018).
[14]. Nguyen Duc Minh, Bui Thi Dan, and Pham Van Hung, "To solve the problem of detection in multi-location radar network which affected by correlated noise with log-normal distribution model", Journal of Military Science and Technology, vol. 57, pp. 110–122, (2018).
[15]. Pham Van Hung and Nguyen Duc Minh, “The detection performance of the multistatic radar system with the copula-based dependence structure”, Journal of Science and Technology on Information and Communications, vol. 03, CS.01, pp. 124–130, (2020).
[16]. Pham Van Hung, Nguyen Duc Minh, and Nguyen Tuan Hung, “A new method solving the detection problem in distributed-processing multistatic radar system with statistically dependent local decisions”, Journal of Science and Technology on Information and Communications, vol. 01, CS.01, pp. 111–118, (2022).
[17]. Van Hung PHAM, Tuan Hung NGUYEN, Duc Minh NGUYEN, and Hisashi MORISHITA, “A New Method Based on Copula Theory for Evaluating Detection Performance of Distributed-Processing Multistatic Radar System”, IEICE Transactions on Communications, vol. E105-B, no. 1, pp. 67–75, (2022).
[18]. Van Hung PHAM, Tuan Hung NGUYEN, and Hisashi MORISHITA, “Detection Performance Analysis of Distributed-Processing Multistatic Radar System with Different Multivariate Dependence Models in Local Decisions”, IEICE Transactions on Communications, vol. E105-B, no. 9, pp. 1097–1104, (2022).
[19]. J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Netw., Perth, Australia, vol. 4, pp. 1942–1948, (1995).
[20]. R. C. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proc. 6th Int. Symp. Micromachine Human Sci., Nagoya, Japan, pp. 39–43, (1995).
[21]. S.-Y. Ho, H.-S. Lin, W.-H. Liauh, and S.-J. Ho, “OPSO: Orthogonal particle swarm optimization and its application to task assignment problems,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 38, no. 2, pp. 288–298, (2008).
[22]. B. Liu, L. Wang, and Y. H. Jin, “An effective PSO-based memetic algorithm for flow shop scheduling,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 37, no. 1 (2007), pp. 18–27.
[23]. G. Ciuprina, D. Ioan, and I. Munteanu, “Use of intelligent-particle swarm optimization in electromagnetics,” IEEE Trans. Magn., vol. 38, no. 2, pp. 1037–1040, (2002).
[24]. J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Trans. Evol. Comput., vol. 10, no. 3, pp. 281–295, (2006).
