High-performance low-complexity multi-sensing-parameter association in perceptive mobile networks

The integrated sensing and communication (ISAC) technology has emerged as an enabler that promises to transform the traditional mobile communication networks into the multifunctional perceptive mobile networks (PMNs), where precise positioning and motion state estimation of network nodes can be achieved relying on wireless communications within the network itself. However, in a practical PMN, multiple types of individually estimated parameters corresponding to multiple sensing targets are not naturally associated with each specific target, which may cause severe obstacles to subsequent signal processing tasks, such as positioning and motion state estimation. To address this challenge, a high-performance low-complexity sensing parameter association algorithm is proposed in this paper. Different from previous works, we first develop a novel spatial filter by exploiting the convolutional beamspace based beamformer to separate paths with different directions of arrival (DOA), and then leverage a low-complexity correlation-based algorithm to associate the DOA estimates with the corresponding paired range-velocity estimates. Extensive simulation results are provided to validate the superior performance of the proposed parameter association algorithm over state-of-the-art schemes.