An Accurate UAV Ground Landing Station System Based on BLE-RSSI and Maximum Likelihood Target Position Estimation

Abstract

Earth observation with unmanned aerial vehicles (UAVs) offers an extraordinary opportunity to bridge the gap between field observations and traditional air and space-borne remote sensing. In this regard, ground landing stations (GLS) systems play a central role to increase the time and area coverage of UAV missions. Bluetooth low energy (BLE) technology and the received signal strength indicator (RSSI) techniques have been proposed for target location during UAV landing. However, these RSSI-based techniques present a lack of precision due to the propagation medium characteristics, which leads to UAV position vagueness. In this sense, the development of a novel low-cost GLS system for UAV tracking and landing is proposed. The GLS system has been embodied for the purpose of testing the UAV landing navigation capability. The maximum likelihood estimator (MLE) algorithm is addressed on an embedded microcontroller for the position estimation based on the RSSI acquired from an array of BLE devices. Experimental results demonstrate the feasibility and accuracy of the ground landing station system, achieving average errors of less than 0.04 m with the UAV-MLE target position estimation approach. This 0.04 m distance represents an order of magnitude increase in location precision over other currently available solutions. In many cases, this increased precision can enable more innovative docking mechanisms, less likelihood of mishaps in docking, and also quicker docking. It may also facilitate docking procedures where the docking station is itself moving, which may be the case if the docking unit is a mobile ground rover.

Research Progress of Wireless Positioning Methods Based on RSSI

“…In [47], wireless positioning technology was applied to UAV (unmanned aerial vehicle) landing, and a prototype UAV ground landing station based on BLE-RSSI was proposed in combination with maximum likelihood estimation and the trilingual measurement algorithm. The results showed that BLU-RSSI could determine the location target more accurately when the UAV was within 5 m of the ground landing station platform.…”

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

“…Representing an enhanced version of Bluetooth, BLE is designed for applications that require short range demands with low power, having the same bit rate, frequency range (i.e., from 2.4 GHz to 2.48 GHz), and range of traditional Bluetooth [241]. In combination with the Received Signal Strength Indicator (RSSI) technique, the BLE-RSSI combined method presented a lack of precision in UAV positioning, which was overcome in [242]. With regard to UAV navigation in GNSS-denied environments, an indoor positioning system for UAVs is set up through BLE beacons by analyzing the RSSI, where the final position estimation is acquired by trilateration [243].…”

Internet of Things (IoT): Wireless Communications for Unmanned Aircraft System

“…The most significant data rate that BLE can accomplish is 1Mbps, which may not always be sufficient for devices like wireless headphones that demand continuous data streaming (Avilés-Viñas et al, 2022;Barsocchi, Girolami, & La Rosa, 2021;Belwafi, Alkadi, Alameri, Al Hamadi, & Shoufan, 2022;Cantizani-Estepa et al, 2022). On the other hand, another internet of thing application requires periodic small data transmissions.…”