An Improved Compressive Sensing and Received Signal Strength-Based Target Localization Algorithm with Unknown Target Population for Wireless Local Area Networks

Abstract

In this paper a two-phase compressive sensing (CS) and received signal strength (RSS)-based target localization approach is proposed to improve position accuracy by dealing with the unknown target population and the effect of grid dimensions on position error. In the coarse localization phase, by formulating target localization as a sparse signal recovery problem, grids with recovery vector components greater than a threshold are chosen as the candidate target grids. In the fine localization phase, by partitioning each candidate grid, the target position in a grid is iteratively refined by using the minimum residual error rule and the least-squares technique. When all the candidate target grids are iteratively partitioned and the measurement matrix is updated, the recovery vector is re-estimated. Threshold-based detection is employed again to determine the target grids and hence the target population. As a consequence, both the target population and the position estimation accuracy can be significantly improved. Simulation results demonstrate that the proposed approach achieves the best accuracy among all the algorithms compared.

Energy-Level Jumping Algorithm for Global Optimization in Compressive Sensing-Based Target Localization

“…A basis pursuit algorithm SPGL1 [42] was used to simultaneously estimate the locations and transmitting powers of targets. However, the authors of [43] stated that the above localization algorithms needed to previously know the number of targets, which was usually unknown in practice. In addition, the relevant recovery algorithms easily converge to suboptimal solutions.…”

Signal acquisition of Luojia-1A low earth orbit navigation augmentation system with software defined receiver

“…Many studies consider using Signals of Opportunity (SOP) for positioning when GNSS is unavailable or unreliable. These SOP include digital television (Chen et al 2017b;Chen et al 2014), Bluetooth (Cao et al 2019), LEO (Chen, Wang, and Zhang 2016;Ardito et al 2019), Wi-Fi (Yan et al 2021(Yan et al , 2018(Yan et al , 2017, vision (Wang et al 2020;Chen et al 2017), and 5 G (Dammann, Raulefs, and Zhang 2015;Wymeersch et al 2017;Zhou et al 2020), and so on. Among them, the LEO satellite has been paid more and more attention and has become a research hotspot.…”

“…Many studies consider using signals of opportunity (SOP) for positioning when GNSS is unavailable or unreliable. These signals of opportunity include digital television [9]- [11], Bluetooth [12], low earth orbit [13], [14], WIFI [15]- [17], vision [18], [19], and 5G [20]- [22], etc. Among them, the LEO satellite has been paid more and more attention and has become a research hotspot.…”