Low-Complexity Joint Range and Velocity Estimation for OFDM-Based Integrated Sensing and Communication

Integrated sensing and communication (ISAC) can realize communication and sensing functionalities simultaneously by sharing spectrum and hardware resources, where the sensing performance can be guaranteed by accurate range and velocity estimation. However joint range and velocity estimation inherently confronts the accuracy-complexity tradeoff. Therefore, a low-complexity joint range and velocity estimation algorithm is developed in this work, referred to as the particle swarm optimization reconstructed subspace multiple signal classification (PSO-RS-MUSIC). The proposed algorithm leverages optimized subspace reuse mechanisms to enhance estimation accuracy. To address the high complexity problem, the PSO-RS-MUSIC algorithm employs the particle swarm optimization (PSO) technique to replace the traditional spectral peak search, thereby reducing computational complexity significantly. Simulation results illustrate that the proposed algorithm outperforms the conventional RS-MUSIC algorithm, while the computational complexity is reduced by more than 90%.