Abstract
High-accuracy positioning is fundamental for modern applications of autonomous agent navigation. The accuracy and stability of predicted locations are key factors for evaluating the suitability of positioning architectures that have to be deployed to real-world cases. Asynchronous TDOA (A-TDOA) methodologies in Local Positioning Systems (LPS) are effective solutions that satisfy the given requirements and reduce temporal uncertainties induced during the synchronization process. In this paper, we propose a technique for the combined characterization of ranging errors-noise, and Non-Line-of-Sight (NLOS) propagation- through the Cramer-Rao Bound (CRB). NLOS propagation effects on signal quality are predicted with a new ray-tracing LOS/NLOS algorithm that provides LOS and NLOS travel distances for communication links in 3D irregular environments. In addition, we propose an algorithm for detecting multipath effects of destructive interference and disability of LOS paths. The proposed techniques are applied to sensor placement optimization in 3D real scenarios. A multi-objective optimization (MOP) process is used based on a Genetic Algorithm (GA) that provides the Pareto Fronts (PFs) for the joined minimization of location uncertainties (CRB) and multipath effects for a variable number of A-TDOA architecture sensors. Results show that the designed procedure can determine, before real implementation, the maximum capacities of the positioning system in terms of accuracy. This allows us to evaluate a trade-off between accuracy and cost of the architecture or support the design of the positioning system under accuracy demands.
Highlights
Local Positioning Systems (LPS) received a growing interest from engineering community in recent years as candidates for navigation applications with high-accuracy requirements, such as Automatic Ground Vehicles (AGVs) and Unmanned Aerial Vehicles (UAVs)
We propose a method for multi-objective optimization (MOP) that allows the minimization of the effect of adversarial factors and the adaptability of time-based positioning architectures to 3D environments
In this paper, we propose a new combined model for measuring positioning architectures accuracy, where the effects of noise and NLOS propagation are quantified in the Cramér-Rao Bound (CRB)
Summary
Local Positioning Systems (LPS) received a growing interest from engineering community in recent years as candidates for navigation applications with high-accuracy requirements, such as Automatic Ground Vehicles (AGVs) and Unmanned Aerial Vehicles (UAVs). The justification lies in the small location uncertainties originated during data acquisition and the stability that these systems provide, due to their capability. Of reducing the distances between targets and architecture sensors. All positioning systems require estimating the location of targets. The most popular techniques are based on measuring time delays [1], [2], received power [3], or angles of incidence [4]. Time-based architectures have become predominant due to their trade-off between hardware complexity, accuracy, and adaptability to complex environments of operation [5]. Time-based positioning architectures were designed under the obligation of synchronization between
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
