Multi-Objective Optimization for Asynchronous Positioning Systems Based on a Complete Characterization of Ranging Errors in 3D Complex Environments

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 Cramér-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.

Hybrid Memetic Algorithm for the Node Location Problem in Local Positioning Systems

“…Therefore, the time required for finding an optimized node distribution increases with the number of TLE analyzed points and it is dependent on the fitness function defined for the sensor distribution quality. This function will contain in this paper the combined uncertainties of noise in LOS and NLOS environments [ 21 ] and clock errors [ 20 ]. These effects are introduced in the covariance matrix of the FIM of the Asynchronous Time Difference of Arrival (A-TDOA) architecture.…”

“…Localization NLP assumes an optimal sensor distribution for reducing the uncertainties in the determination of the TS location. The main system uncertainties in TBP are the noise degradation of the positioning signal in LOS and NLOS environments [ 21 ], the clock errors in the time measurements which are generated by synchronization of the system devices, drift and truncation errors in the CS clocks [ 20 ] and the geometric deployment of the sensors in space which affects the positioning algorithm performance [ 60 ].…”

“…Even completely asynchronous architectures are recently being proposed [ 18 , 19 ] and are attracting high research interest since they collect all the time measurements in a single clock of a Coordinator Sensor (CS). This allows us to avoid synchronism among the system receivers, consequently reducing the architecture clock errors [ 20 ] but increasing the signal paths and noise errors [ 21 ], since they rely on a receive-and-retransmit strategy of the positioning signals in the Target Sensor (TS), producing longer path signals. In addition, a possible CS malfunction may produce temporal system unavailability [ 22 ] due to the particular architecture dependence on the CS.…”

“…In our previous research, we have applied GA to the NLP in LPS [ 7 , 20 , 21 , 22 , 30 ]. In these papers, we have observed that the dimensions of the space of solutions which increases with the number of sensors, the resolution of the pre-defined possible space locations for them and the complexity of the fitness function evaluation, significantly affect the stable performance of the GA.…”

“…These conditions especially occur in NLOS scenarios in which the signal quality may be significantly distorted if a node is located just behind an obstacle. This fact has promoted the usage of pre-defined populations of the GA for obtaining practical results in the GA evolution [ 21 ].…”