A Novel Hybrid NN-ABPE-Based Calibration Method for Improving Accuracy of Lateration Positioning System

Positioning systems based on the lateration method utilize distance measurements and the knowledge of the location of the beacons to estimate the position of the target object. Although most of the global positioning techniques rely on beacons whose locations are known a priori, miscellaneous factors and disturbances such as obstacles, reflections, signal propagation speed, the orientation of antennas, measurement offsets of the beacons hardware, electromagnetic noise, or delays can affect the measurement accuracy. In this paper, we propose a novel hybrid calibration method based on Neural Networks (NN) and Apparent Beacon Position Estimation (ABPE) to improve the accuracy of a lateration positioning system. The main idea of the proposed method is to use a two-step position correction pipeline that first performs the ABPE step to estimate the perceived positions of the beacons that are used in the standard position estimation algorithm and then corrects these initial estimates by filtering them with a multi-layer feed-forward neural network in the second step. In order to find an optimal neural network, 16 NN architectures with 10 learning algorithms and 12 different activation functions for hidden layers were implemented and tested in the MATLAB environment. The best training outcomes for NNs were then employed in two real-world indoor scenarios: without and with obstacles. With the aim to validate the proposed methodology in a scenario where a fast set-up of the system is desired, we tested eight different uniform sampling patterns to establish the influence of the number of the training samples on the accuracy of the system. The experimental results show that the proposed hybrid NN-ABPE method can achieve a high level of accuracy even in scenarios when a small number of calibration reference points are measured

Seasonal changes of radon concentration in Niedzwiedzia cave (SW Poland)

The paper presents the results of measurements of average monthly radon concentrations obtained in the most beautiful Polish cave, the Niedz´wiedzia Cave, between July 1995 and December 1996. 222 measurements were taken at 7 measurement points with the use of trace detectors LR-115 type II. Distinct seasonal fluctuation of the concentration was observed: the highest values (up to 3.60 kBq/m3) were noted in summer (from April to September), while the lowest ones in winter (0.10 kBq/m3, in January 1996). A sharp increase in the concentration in spring and a decrease in autumn are typical. The main factor controlling radon concentration changes in the air of the Niedz´wiedzia Cave is the process of ventilation

A Novel Hybrid NN-ABPE-Based Calibration Method for Improving Accuracy of Lateration Positioning System

Positioning systems based on the lateration method utilize distance measurements and the knowledge of the location of the beacons to estimate the position of the target object. Although most of the global positioning techniques rely on beacons whose locations are known a priori, miscellaneous factors and disturbances such as obstacles, reflections, signal propagation speed, the orientation of antennas, measurement offsets of the beacons hardware, electromagnetic noise, or delays can affect the measurement accuracy. In this paper, we propose a novel hybrid calibration method based on Neural Networks (NN) and Apparent Beacon Position Estimation (ABPE) to improve the accuracy of a lateration positioning system. The main idea of the proposed method is to use a two-step position correction pipeline that first performs the ABPE step to estimate the perceived positions of the beacons that are used in the standard position estimation algorithm and then corrects these initial estimates by filtering them with a multi-layer feed-forward neural network in the second step. In order to find an optimal neural network, 16 NN architectures with 10 learning algorithms and 12 different activation functions for hidden layers were implemented and tested in the MATLAB environment. The best training outcomes for NNs were then employed in two real-world indoor scenarios: without and with obstacles. With the aim to validate the proposed methodology in a scenario where a fast set-up of the system is desired, we tested eight different uniform sampling patterns to establish the influence of the number of the training samples on the accuracy of the system. The experimental results show that the proposed hybrid NN-ABPE method can achieve a high level of accuracy even in scenarios when a small number of calibration reference points are measured

Neural network-based calibration for accuracy improvement in lateration positioning system

Mobile robot positioning is a crucial problem in modern industrial autonomous solutions. Lateration Positioning Systems base on the distance measurements to estimate the object's position. These measurements are however often affected by numerous sources of noise: obstacles, multi-pathing, signal propagation speed etc. Effective calibration methods are therefore required to eliminate these errors to achieve precise positioning. In this paper, we present the application of neural networks to improve the accuracy of a UWB lateration system. We present the network architecture and demonstrate how it can be used to alleviate the effects of multi-pathing and environment anisotropy in a real positioning setup. We furthermore compare the efficiency of the neural network with the state-of-the-art calibration methods

Neural network-based calibration for accuracy improvement in lateration positioning system

Mobile robot positioning is a crucial problem in modern industrial autonomous solutions. Lateration Positioning Systems base on the distance measurements to estimate the object's position. These measurements are however often affected by numerous sources of noise: obstacles, multi-pathing, signal propagation speed etc. Effective calibration methods are therefore required to eliminate these errors to achieve precise positioning. In this paper, we present the application of neural networks to improve the accuracy of a UWB lateration system. We present the network architecture and demonstrate how it can be used to alleviate the effects of multi-pathing and environment anisotropy in a real positioning setup. We furthermore compare the efficiency of the neural network with the state-of-the-art calibration methods