Accurate 3D localisation of mobile target using single station with AoA–TDoA measurements

Zhao, Yuexin; Qi, Wangdong; Liu, Peng; Chen, Longliang; Lin, Jie

doi:10.1049/iet-rsn.2019.0600

Cited by 10 publications

(7 citation statements)

References 28 publications

(38 reference statements)

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“…Therefore, a mixture of multiple positioning methods can be considered. In the existing studies, the fusion of localization schemes mainly includes TDOA + AoA and TDOA/AoA + fingerprints [19][20][21][22][23]. The combination of TDOA and AoA can better solve the synchronization problem in a time-based positioning scheme and the problem that the angle-based positioning scheme is sensitive to the rotation angle of the device.…”

Section: Related Workmentioning

confidence: 99%

A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications

et al. 2022

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With the real restriction of packet size and transmission power, a joint UL-AoA (Uplink Angle of Arrival) and UL-TDOA (Uplink Time Difference of Arrival) positioning method is discussed for the Industrial Internet of Things (IIoT), which demands high accuracy of position information. One motivation of the UL positioning method of IIoT is an asymmetric network and uplink traffic is dominant in most identified scenarios. Further, IIoT sensors are power limited for low cost, compared to the transmission power of base stations and other normal user equipment. This paper considers an environment of a 5G-enabled IIoT network with massive Multiple input multiple output (MIMO) antennas, beamforming and millimeter operating bands. After reviewing downlink positioning reference signal (PRS) and uplink sounding reference signal for positioning (SRS-Pos), a general system model and a theoretical localization problem is derived under the joint positioning method. The provided simulation results help to investigate the impact of system configurations on positioning measurements. Further, through the test in this paper, it is proved that joint positioning outperforms that of the time only or angle only method.

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Section: Related Workmentioning

confidence: 99%

A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications

et al. 2022

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“…The hybrid geolocation technique is designed to obtain a more precise estimation of the target's location and reduce the number of sensors needed compared to a particular technique. In the state-of-the-art, various hybrid techniques have been used in geolocation applications, for example, (TDOA/AOA), (TDOA/FDOA), and (TDOA/PDOA) [59][60][61][62][63].…”

Section: Hybrid Technique Of Geolocation Measurementsmentioning

confidence: 99%

A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems

et al. 2021

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A single Radio-Frequency Interference (RFI) is a disturbance source of modern wireless systems depending on Global Navigation Satellite Systems (GNSS) and Satellite Communication (SatCom). In particular, significant applications such as aeronautics and satellite communication can be severely affected by intentional and unintentional interference, which are unmitigated. The matter requires finding a radical and effective solution to overcome this problem. The methods used for overcoming the RFI include interference detection, interference classification, interference geolocation, tracking and interference mitigation. RFI source geolocation and tracking methodology gained universal attention from numerous researchers, specialists, and scientists. In the last decade, various conventional techniques and algorithms have been adopted in geolocation and target tracking in civil and military operations. Previous conventional techniques did not address the challenges and demand for novel algorithms. Hence there is a necessity for focussing on the issues associated with this. This survey introduces a review of various conventional geolocation techniques, current orientations, and state-of-the-art techniques and highlights some approaches and algorithms employed in wireless and satellite systems for geolocation and target tracking that may be extremely beneficial. In addition, a comparison between different conventional geolocation techniques has been revealed, and the comparisons between various approaches and algorithms of geolocation and target tracking have been addressed, including H∞ and Kalman Filtering versions that have been implemented and investigated by authors.

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“…The final estimate for the UAV 3D coordinates is computed adopting extended Kalman filter (EKF) to fuse inertial measurement unit (IMU) data, barometer data and UAV's 2D location estimate. Since the system is configured for a single BS and relatively little research has been conducted regarding UAV localization by employing a single BS [12], we assessed our results against those achieved through IMU based dead‐reckoning [13]. Simulations were performed in simulation‐in‐the‐loop (SITL) software accompanied by Arducopter firmware for a quadcopter.…”

Section: Introductionmentioning

confidence: 99%

Unmanned aerial vehicle localization using angle of departure from a single base station and dead‐reckoning

Rajasekaran,

Meles,

Virrankoski

et al. 2024

Electronics Letters

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Localization in GNSS‐denied environments for unmanned aerial vehicles (UAVs) has recently gained significant interest from the research community. Most of the research is focused primarily on visual localization. This paper examines an algorithm which employs angle of departure and UAVs payload sensor data for UAV localization. First the algorithm uses multiple angle of departures from a single base station and a travel calculated by applying dead‐reckoning on the UAVs inertial measurement unit (IMU), to compute UAV location in 2D coordinates. The 2D location estimate is then fed into a modified extended Kalman filter, which employs the estimate, IMU and barometer data to compute the 3D coordinates for UAV. For the simulation, simulation‐in‐the‐loop accompanied by Arducopter and MAVLink is used to simulate different trajectories and collect the required data for the algorithm. The algorithm is validated by comparing the extended Kalman filter estimates with IMU dead‐reckoned positions. Three simulations were performed, consisting of linear, zigzag, and curved trajectories. A percentile error of 2.5 and 4 m is achieved for the x‐coordinate and y‐coordinate, respectively, on the zigzag and curved trajectories. Interestingly, the linear trajectory showed a larger localization error in its y‐coordinate.

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Accurate 3D localisation of mobile target using single station with AoA–TDoA measurements

Cited by 10 publications

References 28 publications

A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications

A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications

A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems

Unmanned aerial vehicle localization using angle of departure from a single base station and dead‐reckoning

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