In this paper, two optimal resource allocation schemes are developed for asynchronous multiple targets tracking (MTT) in heterogeneous radar network. The key idea of the heterogeneous resource allocation (HRA) schemes is to coordinate the heterogeneous transmit resource (transmit power, dwell time, etc.) of different types of radars for better resource utilization efficiency. We use the Bayesian Cramr-Rao lower bound (BCRLB) as a metric function to quantify the target tracking performance, and build the following two HRA schemes: For given system resource budget, (1) Minimize the total resource consumption for given BCRLB requirements on multiple targets; (2) Maximize the overall MTT accuracy. Instead of updating the state of each target recursively at different measurement arrival times, we combine multiple asynchronous measurements into a single composite measurement, and use it as an input of the tracking filter for state estimation. In such a case, target tracking BCRLB no longer needs to be recursively calculated, and thus we can formulate the HRA schemes as two convex optimization problems. We subsequently design two efficient methods to solve these problems by exploring their unique structures. Simulation results demonstrate that the HRA processes can either provide smaller overall MTT BCRLB for given resource budgets, or require less resource to establish the same tracking performance for multiple targets.

In this article, a joint transmit resource management and waveform selection (JTRMWS) strategy is put forward for target tracking in distributed phased array radar network. We establish the problem of joint transmit resource and waveform optimization as a dual-objective optimization model. The key idea of the proposed JTRMWS scheme is to utilize the optimization technique to collaboratively coordinate the transmit power, dwell time, waveform bandwidth, and pulse length of each radar node in order to improve the target tracking accuracy and low probability of intercept (LPI) performance of distributed phased array radar network, subject to the illumination resource budgets and waveform library limitation. The analytical expressions for the predicted Bayesian Cramér–Rao lower bound and the probability of intercept are calculated and subsequently adopted as the metric functions to evaluate the target tracking accuracy and LPI performance, respectively. It is shown that the JTRMWS problem is a nonlinear and nonconvex optimization problem, where the above four adaptable parameters are all coupled in the objective functions and constraints. Combined with the particle swarm optimization algorithm, an efficient and fast three-stage-based solution technique is developed to deal with the resulting problem. Simulation results are provided to verify the effectiveness and superiority of the proposed JTRMWS algorithm compared with other state-of-the-art benchmarks.

In this paper, a target capacity based resource optimization (TC-RO) scheme is developed for multiple target tracking (MTT) application in radar networks. The key idea of this scheme is to coordinate the transmit power and dell time resource usage of multiple radars in order to increase the number of the targets that can be tracked with predetermined accuracy requirements. We adopt the Bayesian Cramér-Rao lower bound as a metric function to quantify the MTT accuracies, and build the TC-RO scheme as a non-smooth and non-convex optimization problem. To deal with this problem, we design an efficient three-step solution technique which incorporates relaxation and fine-tuning process. Specifically, we first relax the resulting optimization problem as a smooth one by applying sigmoid-type transformation to its objective, and then develop an appropriate method to find a local minimum to the relaxed non-convex problem with guaranteed convergence. After that, the local minimum of the relaxed problem is used as an initial point and a fine-tuning process is performed to search for a reasonable feasible solution to the original non-smooth optimization problem. Simulation results demonstrate that the proposed TC-RO scheme can greatly increase the target capacity of the radar network when compared with the traditional uniform allocation scheme.

Taking into account the probabilistic uncertainty on the target radar cross section (RCS) parameter, a robust chance constrained power allocation (RCC-PA) scheme is presented for multiple target localization in colocated multiple-input multiple-output radar system. Such a system adopts a multibeam working mode, in which multiple simultaneous transmit beams are synthesized to illuminate multiple targets independently. We formulate the RCC-PA problem into a chance constrained programming model, where the total power consumption of the multiple beams is minimized to meet a specified multi-target localization accuracy requirement with high probability. Various target RCS fluctuation models have been discussed with different forms of probability distributions. We analytically show that the chance constrained programming problem for each RCS fluctuation model can equivalently be formulated as a deterministic convex optimization problem. Then, by formulating the Karush-Kuhn-Tuckers conditions, we transform the convex optimization problem into a nonlinear equation solving problem, and then solve it by using the bisection method. Simulation results show that our RCC-PA scheme can enhance the power utilization efficiency, and is more robust than the existing deterministic PA schemes.

In this paper, a joint threshold adjustment and power allocation (JTAPA) algorithm is developed for target tracking in asynchronous radar network (ARN). The basis of the JTAPA strategy is to feed back the target track information from the fusion center to local radar sites to enhance both the target detection capability and the resource utilization efficiency of the ARN. For the detector, we develop a threshold adjustment (TA) algorithm for better detection performance, based on the predicted target location information fed back from the fusion center. For the transmitter, we build an asynchronous power allocation (APA) model based on the perceptual information, and use an optimization technique to control the limited power resource for the next period of time. The goal of the APA scheme is to achieve better target tracking accuracy with a given power budget. The Bayesian Cramér-Rao lower bound is derived, normalized, and subsequently utilized, as the optimization criterion for the APA strategy. The resulting nonconvex optimization problem is solved through relaxation incorporating the spectral projected gradient technique. Simulation results demonstrate that the integration of the TA and APA processes can evidently improve the tracking performance.