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In this paper, an antenna array aperture resource management scheme of opportunistic array radar (OAR) based on chance-constraint programming (CCP) is proposed for multiple target tracking (MTT). In the multi-target tracking scenario, multiple beams need to be synthesized to illuminate the targets. According to the predicted distance of targets, the OAR can intelligently segment the antenna array aperture, and select the optimal combination of array elements to synthesize the satisfactory beams with the element-level antenna steering. Owing to the unique array arrangement and work mode of opportunistic array, the element number in working modes is uncertain. To represent the uncertainty, a fuzzy CCP model is introduced. The element number in working modes is viewed as a fuzzy variable. The maximum tracking error, which is denoted by Bayesian Cramér-Rao lower bound(BCRLB), of the targets is regarded as the objective function. The fuzzy simulation algorithm is embedded into genetic algorithm (GA) to compose a hybrid intelligent optimization algorithm to solve the CCP problem. The simulation results verify the validity and practicability of the antenna array aperture resource allocation scheme.

This paper proposes a joint power and beam allocation scheme based on chance-constraint programming (CCP) for multiple target tracking of opportunistic array radar (OAR) in cluttered domains. The objective of the scheme is to cope with complex environments, unknown target information, and insufficient resource of radar system installed in mobile platforms operating over prolonged time periods. In this scheme, the total power of transmitting beams used in estimation process is minimized by effective allocation over the targets so that the desired tracking performance is attained. Allowing for the randomness of target RCS, the CCP used for balancing tracking performance and power resource is introduced conditioned on a specified confidence level. To track the targets more efficiently, the beams are allocated to the targets with large prior Cramer–Rao lower bound (CRLB). Then, in conjunction with an information reduction factor (IRF), the tracking Bayesian CRLB (BCRLB) of the illuminated targets can be considered as a measurement criteria for resource allocation in clutter. Through Conditional Value at Risk (CVaR), the CCP is relaxed to a convex problem solved by Lagrange multipliers method with Kuhn–Tucker (KT). The simulation results verify the validity and practicability of the resource allocation scheme.

In this paper, a joint adaptive sampling interval and power allocation (JASIPA) scheme based on chance-constraint programming (CCP) is proposed for maneuvering target tracking (MTT) in a multiple opportunistic array radar (OAR) system. In order to conveniently predict the maneuvering target state of the next sampling instant, the best-fitting Gaussian (BFG) approximation is introduced and used to replace the multimodal prior target probability density function (PDF) at each time step. Since the mean and covariance of the BFG approximation can be computed by a recursive formula, we can utilize an existing Riccati-like recursion to accomplish effective resource allocation. The prior Cram&eacute, r-Rao lower boundary (prior CRLB-like) is compared with the upper boundary of the desired tracking error range to determine the adaptive sampling interval, and the Bayesian CRLB-like (BCRLB-like) gives a criterion used for measuring power allocation. In addition, considering the randomness of target radar cross section (RCS), we adopt the CCP to package the deterministic resource management model, which minimizes the total transmitted power by effective resource allocation. Lastly, the stochastic simulation is embedded into a genetic algorithm (GA) to produce a hybrid intelligent optimization algorithm (HIOA) to solve the CCP optimization problem. Simulation results show that the global performance of the radar system can be improved effectively by the resource allocation scheme.