Haowei Zhang

A hybrid DPSO with Levy flight for scheduling MIMO radar tasks

Abstract In this paper, an optimization model for the multiple-input and multiple-output (MIMO) radar task scheduling is established, and a hybrid discrete particle swarm optimization (DPSO) algorithm with Levy flight is proposed for a solution to the model. The optimization model takes the task internal structure, the characteristics of task scheduling in the MIMO radar and the three task scheduling principles into consideration. The hybrid DPSO integrates a heuristic task interleaving algorithm for the task schedulability analysis of candidate scheduling schemes (particles) with a DPSO with Levy flight for exploring the best solution. The heuristic task interleaving algorithm not only exploits the wait interval to interleave subtasks, but also incorporates transmit intervals and overlaps receive intervals in order to make a maximum utilization of the radar timeline. The DPSO is combined with Levy flight to escape from local optima by utilizing the long jump property. In addition, the chaos initialization and the linearly decreasing inertia weight are designed to enhance the exploration ability and the exploitation ability. The simulation results verify the outperformance of the proposed algorithm compared with the existing ones.

A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar

A hybrid optimization approach combining a particle swarm algorithm, a genetic algorithm, and a heuristic inter-leaving algorithm is proposed for scheduling tasks in the multifunction phased array radar. By optimizing parameters using chaos theory, designing the dynamic inertia weight for the particle swarm algorithm as well as introducing crossover operation and mutation operation of the genetic algorithm, both the efficiency and exploration ability of the hybrid algorithm are improved. Under the frame of the intelligence algorithm, the heuristic interleaving scheduling algorithm is presented to further use the time resource of the task waiting duration. A large-scale simulation demonstrates that the proposed algorithm is more robust and efficient than existing algorithms.

Joint beam and waveform selection for the MIMO radar target tracking

Abstract The simultaneous orthogonal multi-beam transmitted by a collocated multiple-input multiple-output (MIMO) radar is very effective in multi-target tracking. Since this working mode can meet the requirement of low possibility of interception; meanwhile, gain higher sensitivity and resolution. Aiming at the resource management in such a scenario, a joint beam, power and waveform selection strategy is put forward. The optimization model is established subject to a certain number of beams, power budget and waveform parameters. The criterion, which is predicted by the Kalman recursive equation, is minimizing the posterior estimate errors of multiple targets in worst cases simultaneously. Thereby, the suitable number of utilized beams, the allocated power as well as the waveform parameter in each beam for the current time epoch can be adaptively turned according to the estimate errors from the previous time epoch. We then fully demonstrate such a non-convex problem can be transformed into several convex problems. As such, the solution can be provided efficiently to meet the real-time demand. Furthermore, through obtaining the posterior estimate error by the square-root cubature Kalman filter, prediction and then adapts the three resources for next time epoch, a closed loop feedback allocation scheme is established. The simulation results show that the proposed algorithm can significantly improve the tracking performance compared with the uniform and random resource allocation strategies.

An Entropy-based PSO for DAR task scheduling problem

Abstract This paper addresses the task scheduling problem in the digital array radar (DAR), which determines the optimal execution order of all tasks subject to precedence and resource constraints. The aim is to achieve good performance in multiple aspects. To our best knowledge, the existing scheduling algorithms, neglecting the task internal structure, not posed as an optimization model, and only utilizing the heuristic method or the meta-heuristic method to solve the problem, cannot fully give free rein to the DAR capability of handling various tasks. Therefore, for such an N-P hard problem, an integer programming optimization model and a hybrid particle swarm optimization (PSO) algorithm are proposed. In the optimization model, a full radar task structure is established, and a comprehensive objective function is formed to guarantee the performance in multiple aspects. In the hybrid PSO, a modified PSO is incorporated to explore good scheduling schemes, and a heuristic task interleaving algorithm, embedded in the PSO framework, for the efficient task schedulability analysis. Moreover, the chaotic sequences are adopted to improve the quality of initialized solution. The Shannon’s entropy is introduced to indicate the diversity of the population and adaptively tunes the parameters. Simulation results show that the proposed algorithm outperforms the three state-of-the-art scheduling algorithms while maintaining a reasonable runtime.