Joint Transmit Resource Management and Waveform Selection Strategy for Target Tracking in Distributed Phased Array Radar Network

In this paper, 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 Cram\'{e}r-Rao lower bound (BCRLB) 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 non-linear and non-convex optimization problem, where the above four adaptable parameters are all coupled in the objective functions and constraints. Combined with the particle swarm optimization (PSO) 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

Evolutionary Computational Intelligence-Based Multi-Objective Sensor Management for Multi-Target Tracking

In multi-sensor systems (MSSs), sensor selection is a critical technique for obtaining high-quality sensing data. However, when the number of sensors to be selected is unknown in advance, sensor selection is essentially non-deterministic polynomial-hard (NP-hard), and finding the optimal solution is computationally unacceptable. To alleviate these issues, we propose a novel sensor selection approach based on evolutionary computational intelligence for tracking multiple targets in the MSSs. The sensor selection problem is formulated in a partially observed Markov decision process framework by modeling multi-target states as labeled multi-Bernoulli random finite sets. Two conflicting task-driven objectives are considered: minimization of the uncertainty in posterior cardinality estimates and minimization of the number of selected sensors. By modeling sensor selection as a multi-objective optimization problem, we develop a binary constrained evolutionary multi-objective algorithm based on non-dominating sorting and dynamically select a subset of sensors at each time step. Numerical studies are used to evaluate the performance of the proposed approach, where the MSS tracks multiple moving targets with nonlinear/linear dynamic models and nonlinear measurements. The results show that our method not only significantly reduces the number of selected sensors but also provides superior tracking accuracy compared to generic sensor selection methods

Adaptive Region Proposal Selection for SAR Target Detection Using Reinforcement Learning

Compared with optical images, the background clutter has a greater impact on feature extraction in Synthetic Aperture Radar (SAR) images. Due to the traditional redundant region proposals on the entire feature map, these algorithms generate large quantities of false alarms under the influence of clutter in SAR images, thereby lowering the target detection accuracy. To address this issue, this study proposes a Faster R-CNN model-based SAR target detection method, which uses reinforcement learning to realize adaptive region proposal selection. This method can adaptively locate areas that may contain targets on the feature map using the sequential decision-making characteristic of reinforcement learning and simultaneously adjust the scope of the next search area according to previous search results using distance constraints in reinforcement learning. Thus, this method can reduce the impact of complex background clutter and the computation of reinforcement learning. The experimental results based on the measured data indicate that the proposed method improves the detection performance

Optimal Resource Allocation for Asynchronous Multiple Target Tracking in Heterogeneous Radar Network

In this paper, two optimal resource allocation schemes are developed for asynchronous multiple targets tracking (MTT) in heterogeneous radar networks. The key idea of heterogeneous resource allocation (HRA) schemes is to coordinate the heterogeneous transmit resource (transmit power, dwell time, etc.) of different types of radars to achieve a better resource utilization efficiency. We use the Bayesian Cramér-Rao lower bound (BCRLB) as a metric function to quantify the target tracking performance and build the following two HRA schemes: For a given system resource budget: (1) Minimize the total resource consumption for the given BCRLB requirements on multiple targets and (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 a smaller overall MTT BCRLB for given resource budgets or require fewer resources to establish the same tracking performance for multiple target

Flight Path Optimization Method for Dynamic Area Coverage Based on Multi-aircraft Radars

Most traditional multi-aircraft flight path optimization methods are oriented toward area coverage, use static optimization models, and face the challenge of model mismatch under complex dynamic environments. Therefore, this study proposes a flight path optimization method for dynamic area coverage based on multi-aircraft radars. First, we introduce an attenuation factor to this method to characterize the actual coverage effect of airborne radar on a dynamic environment, and we take the area coverage rate under the dynamic area coverage background as the optimization function. After integrating the constraints of multi-dimensional flight path control parameters to be optimized, we built a mathematical model for dynamic area coverage flight path optimization based on multi-aircraft radars. Then, the stochastic optimization method is used to solve the flight path optimization problem of dynamic area coverage. Finally, the simulation results show that the proposed flight path optimization method can significantly improve the dynamic coverage performance in dynamic areas compared with the search mode using preset flight paths based on multi-aircraft radars. Compared with the traditional flight path optimization method oriented to static environments, the dynamic coverage performance of our proposed method is improved by approximately 6% on average

Search task oriented path planning method of airborne radar network

Reasonable planning the trajectory of the airborne radar network can decrease its repeated surveillance region, and improve the search efficiency. To complete a wider range of reconnaissance searches within a certain time, a search task oriented path optimization method of airborne radar network is proposed. Firstly, the single-platform search range by combing the radar equation is characterized, and the multi-radar area coverage function based on the rasterization idea is calculated. A trajectory optimization model with the goal of maximizing the search coverage function is constructed, by combining with the motion constraints of each node and adopting an intelligent method. Simulation results show that the present method can obtain higher search coverage within specified time