In this paper, we address the Line-of-Sight (LOS) localization problem in a monostatic Integrated Sensing and Communication (ISAC) system based on colocated Multiple-Input Multiple-Output (MIMO) technology in the presence of multipath scenarios. Firstly, we derive Generalized Likelihood Ratio Tests (GLRTs) to distinguish LOS paths from Non-Line-of-Sight (NLOS) paths based on full-rank and rank-deficient transmit waveform, providing closed-form expressions for false alarm probability and detection probability. Furthermore, in the case of unknown path parameters, we employ the GLRT philosophy, utilizing meticulously developed estimators to replace the unknown parameters. The angular parameters of both LOS and NLOS paths are estimated using a sparsity-enforced Compressed Sensing (CS) approach, aiming at estimating angular parameters in the continuous domain. Finally, we compare the performance of full-rank and rank-deficient waveforms in different scenarios and demonstrate the effectiveness of the proposed detection-estimation solution through simulations.

Localization with Monostatic ISAC System: LOS Detection and Parameter Estimation / Long, Jiamin; Zheng, Le; Lops, Marco; Liu, Fan; Zhao, Chuanhao. - (2024), pp. 1-6. ( 2024 IEEE Radar Conference, RadarConf 2024 usa 2024) [10.1109/radarconf2458775.2024.10548681].

Localization with Monostatic ISAC System: LOS Detection and Parameter Estimation

Lops, Marco;
2024

Abstract

In this paper, we address the Line-of-Sight (LOS) localization problem in a monostatic Integrated Sensing and Communication (ISAC) system based on colocated Multiple-Input Multiple-Output (MIMO) technology in the presence of multipath scenarios. Firstly, we derive Generalized Likelihood Ratio Tests (GLRTs) to distinguish LOS paths from Non-Line-of-Sight (NLOS) paths based on full-rank and rank-deficient transmit waveform, providing closed-form expressions for false alarm probability and detection probability. Furthermore, in the case of unknown path parameters, we employ the GLRT philosophy, utilizing meticulously developed estimators to replace the unknown parameters. The angular parameters of both LOS and NLOS paths are estimated using a sparsity-enforced Compressed Sensing (CS) approach, aiming at estimating angular parameters in the continuous domain. Finally, we compare the performance of full-rank and rank-deficient waveforms in different scenarios and demonstrate the effectiveness of the proposed detection-estimation solution through simulations.
2024
Localization with Monostatic ISAC System: LOS Detection and Parameter Estimation / Long, Jiamin; Zheng, Le; Lops, Marco; Liu, Fan; Zhao, Chuanhao. - (2024), pp. 1-6. ( 2024 IEEE Radar Conference, RadarConf 2024 usa 2024) [10.1109/radarconf2458775.2024.10548681].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/1022099
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