A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives

Kabiri, Meisam; Cimarelli, Claudio; Bavle, Hriday; Sánchez-López, José Luis; Voos, Holger

doi:10.3390/s23010188

Cited by 10 publications

(6 citation statements)

References 146 publications

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“…From the many different position estimation methods that can be used, e.g., in cellular networks [ 34 , 35 ], the time difference of arrival (TDoA) method was chosen for investigation. This is a time-based method in which the measurement results provide information about the difference in distance of the mobile object (user terminal) to two reference nodes (base stations).…”

Section: System Modelmentioning

confidence: 99%

A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks

Sadowski,

Stefanski

2024

Sensors

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This article proposes the use of a feedforward neural network (FNN) to select the starting point for the first iteration in well-known iterative location estimation algorithms, with the research objective of finding the minimum size of a neural network that allows iterative position estimation algorithms to converge in an example positioning network. The selected algorithms for iterative position estimation, the structure of the neural network and how the FNN is used in 2D and 3D position estimation process are presented. The most important results of the work are the parameters of various FNN network structures that resulted in a 100% probability of convergence of iterative position estimation algorithms in the exemplary TDoA positioning network, as well as the average and maximum number of iterations, which can give a general idea about the effectiveness of using neural networks to support the position estimation process. In all simulated scenarios, simple networks with a single hidden layer containing a dozen non-linear neurons turned out to be sufficient to solve the convergence problem.

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Section: System Modelmentioning

confidence: 99%

A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks

Sadowski,

Stefanski

2024

Sensors

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“…Other RF measurements, such as the time difference of arrival (TDOA), allow the release of the synchronization requirement or the computation of the position in a non-line-of-sight (NLOS) scenario by extracting information from matrices of channel state information (CSI). Kabiri et al [ 24 ] provide an exhaustive review of RF-based localization methods and give an outlook on current challenges and future research directions.…”

Section: Situational Perceptionmentioning

confidence: 99%

From SLAM to Situational Awareness: Challenges and Survey

Bavle¹,

Sánchez-López²,

Schmidt³

et al. 2023

Sensors

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The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the present research aims to connect the broad multidisciplinary existing knowledge to pave the way for a complete SA system for mobile robotics that we deem paramount for autonomy. To this aim, we define the principal components to structure a robotic SA and their area of competence. Accordingly, this paper investigates each aspect of SA, surveying the state-of-the-art robotics algorithms that cover them, and discusses their current limitations. Remarkably, essential aspects of SA are still immature since the current algorithmic development restricts their performance to only specific environments. Nevertheless, Artificial Intelligence (AI), particularly Deep Learning (DL), has brought new methods to bridge the gap that maintains these fields apart from the deployment to real-world scenarios. Furthermore, an opportunity has been discovered to interconnect the vastly fragmented space of robotic comprehension algorithms through the mechanism of Situational Graph (S-Graph), a generalization of the well-known scene graph. Therefore, we finally shape our vision for the future of robotic situational awareness by discussing interesting recent research directions.

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“…The UAV navigation system should be well balanced and redundant. Autonomous operation in dynamic and unknown environments require precise knowledge of UAV's state, which is characterized by position, velocity and attitude [4]. Currently, almost all commercial and defence UAVs need global navigation satellite system (GNSS) with the help of inertial navigation system (INS) to navigate in unknown environments [5].…”

Section: Introductionmentioning

confidence: 99%

“…Figure 2 presents the parameters used to calculate the 2D location of the UAV. The main concept of the algorithm is to use multiple azimuth angles (θ), yaw angle (ψ), and distance travelled (D) to solve a linear equation using a matrix (4). The main parameter to determine is the projected range d. From which we can determine the X and Y coordinates in the NED (North, East, and Down) frame by converting the polar coordinates d and θ .…”

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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A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives

Cited by 10 publications

References 146 publications

A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks

A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks

From SLAM to Situational Awareness: Challenges and Survey

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

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