Design and Evaluation of a Radar Task Scheduling Algorithm Based on Gaussian Random Perturbation

Task scheduling in multifunction radar systems (MFRs) is a typical combinatorial optimization problem characterized by NP-hard complexity. Traditional exact methods entail high computational complexity, making them unsuitable for real-time applications. This paper presents a heuristic radar task scheduling algorithm based on Gaussian random perturbations. The algorithm introduces priority-dependent stochastic offsets around the expected start times to initially generate a task sequence, and then dynamically updates the priorities and start times of remaining tasks according to their temporal urgency. Simulation results show that compared with traditional methods such as EST, ED, and heuristic algorithms including RSST and DSS, the proposed algorithm significantly reduces the overall scheduling cost by approximately 40% on average and improves the task success rate by about 5%-10%. Hardware validation on an FPGA platform operating at 400 MHz frequency with 350 iterations demonstrated approximately a 10% improvement in task scheduling success rate, confirming the effectiveness and real-time capability of the proposed method.