A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI

Zhang, Tingwei; Zhang, Peng; Kalathas, Paris; Wang, Guangxin; Liu, Huaping

doi:10.3390/s22176404

Cited by 5 publications

(1 citation statement)

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“…The RSSI method depends on the wave propagation channel attenuation model of the practical scenario. Hence, the distances between the sources and the receivers are estimated by measuring the RSSI, and the localization is carried out by multilateration with large errors [ 19 , 20 , 21 , 22 ]. The TDOA method provides reasonably high accuracy of localization and are independent of the propagation channel model.…”

Section: Related Workmentioning

confidence: 99%

Precision Enhancement of Wireless Localization System Using Passive DOA Multiple Sensor Network for Moving Target

Chen

Chang

et al. 2022

Sensors

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Determining the direction-of-arrival (DOA) of any signal of interest has long been of great interest to the wireless localization research community for military and civilian applications. To efficiently facilitate the deployment of DOA systems, the accuracy of wireless localization is critical. Hence, this paper proposes a novel method to improve the prediction result of a wireless DOA localization system. By considering the signal variation existing in the complex environment, the actual location of the target can be determined including the maximum prediction error. Moreover, the scenario of the moving target is further investigated by incorporating the adaptive Kalman Filter algorithm to obtain the prediction route of the flying drone based on the accuracy assessment method. This proposed adaptive Kalman Filter is a high-efficiency algorithm that can filter out the noise in the multipath area and optimize the predicted data in real-time. The simulation result agrees well with the measured data and thus validates the proposed DOA system with the adaptive Kalman Filter algorithm. The measured DOA of the fixed radiation source obtained by a single base station and the moving route of a flying drone from a two-base station localization system are presented and compared with the calculated results. Results show that the prediction error in an outdoor region of 500 × 500 m2 is about 10–20 m, which demonstrate the usefulness of the proposed wireless DOA system deployment in practical applications.

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