Cluster-Based Multi-Target Localization under Partial Observations

Compressive sensing (CS) has been applied to reduce the acquisition costs in the task of target localization by deploying a small number of sensors to collect measurements. It is based on a fact that the number of targets is much smaller than all possible positions where the targets could be located. To improve the localization accuracy with noisy measurements, our previous work has proposed a localization approach by solving a row-based least absolute shrinkage and selection operator (RLASSO) formulation, which utilizes the joint sparsity property. However, some practical concerns in localization implementation, e.g., a decrement in power radiated by targets and an enlargement of overall localization region, degrade the localization accuracy seriously. The performance impairment is caused by the common but undesirable limitations that the reduced radiation power and the enlarged field indeed make targets partially unobservable from individual sensor alone. To combat the impacts of such constrains on localization performance, this paper develops a cluster-based multi-target localization approach based on matrix rank minimization (MRM). The solutions representing all the collected measurements from distributed clusters are modeled to possess a low rank property. Capitalizing on this low rank property, a nuclear norm minimization problem is formulated to localize the positions of multiple targets. Simulation results reveal that the proposed localization approach based on MRM outperforms those via RLASSO and direct CS, since the low rank property enables efficient utilization of user diversity in localization even under partial observations.