InPhase: Phase-based Ranging and Localization

Ranging and subsequent localization have become more and more critical in today’s factories and logistics. Tracking goods precisely enables just-in-time manufacturing processes. We present the InPhase system for ranging and localization applications. It employs narrowband 2.4 GHz IEEE 802.15.4 radio transceivers to acquire the radio channel’s phase response. In comparison, most other systems employ time-of-flight schemes with Ultra Wideband transceivers. Our software can be used with existing wireless sensor network hardware, providing ranging and localization for existing devices at no extra cost. The introduced Complex-valued Distance Estimation algorithm evaluates the phase response to compute the distance between two radio devices. We achieve high ranging accuracy and precision with a mean absolute error of 0.149 m and a standard deviation of 0.104 m. We show that our algorithm is resilient against noise and burst errors from the phase-data acquisition. Further, we present a localization algorithm based on a particle filter implementation. It achieves a mean absolute error of 0.95 m in a realistic 3D live tracking scenario.

Localization of Thermal Wellbore Defects Using Machine Learning

Defect detection and localization are key to preventing environmentally damaging wellbore leakages in both geothermal and oil/gas applications. In this work, a multi-step, machine learning approach is used to localize two types of thermal defects within a wellbore model. This approach includes a COMSOL heat transfer simulation to generate base data, a neural network to classify defect orientations, and a localization algorithm to synthesize sensor estimations into a predicted location. A small-scale physical wellbore test bed was created to verify the approach using experimental data. The classification and localization results were quantified using this experimental data. The classification predicted all experimental defect orientations correctly. The localization algorithm predicted the defect location with an average root mean square error of 1.49 in. The core contributions of this work are 1) the overall localization architecture, 2) the use of centroid-guided mean-shift clustering for localization, and 3) the experimental validation and quantification of performance.

MLSS-VO: A Multi-Level Scale Stabilizer with Self-Supervised Features for Monocular Visual Odometry in Target Tracking

In this study, a multi-level scale stabilizer intended for visual odometry (MLSS-VO) combined with a self-supervised feature matching method is proposed to address the scale uncertainty and scale drift encountered in the field of monocular visual odometry. Firstly, the architecture of an instance-level recognition model is adopted to propose a feature matching model based on a Siamese neural network. Combined with the traditional approach to feature point extraction, the feature baselines on different levels are extracted, and then treated as a reference for estimating the motion scale of the camera. On this basis, the size of the target in the tracking task is taken as the top-level feature baseline, while the motion matrix parameters as obtained by the original visual odometry of the feature point method are used to solve the real motion scale of the current frame. The multi-level feature baselines are solved to update the motion scale while reducing the scale drift. Finally, the spatial target localization algorithm and the MLSS-VO are applied to propose a framework intended for the tracking of target on the mobile platform. According to the experimental results, the root mean square error (RMSE) of localization is less than 3.87 cm, and the RMSE of target tracking is less than 4.97 cm, which demonstrates that the MLSS-VO method based on the target tracking scene is effective in resolving scale uncertainty and restricting scale drift, so as to ensure the spatial positioning and tracking of the target.

A simple faulted phase-based fault distance estimation algorithm for a loop distribution system

This paper presents a single ended faulted phase-based traveling wave fault localization algorithm for loop distribution grids which is that the sensor can get many reflected signals from the fault point to face the complexity of localization. This localization algorithm uses a band pass filter to remove noise from the corrupted signal. The arriving times of the faulted phase-based filtered signals can be obtained by using phase-modal and discrete wavelet transformations. The estimated fault distance can be calculated using the traveling wave method. The proposed algorithm presents detail level analysis using three detail levels coefficients. The proposed algorithm is tested with MATLAB simulation single line to ground fault in a 10 kV grounded loop distribution system. The simulation result shows that the faulted phase time delay can give better accuracy than using conventional time delays. The proposed algorithm can give fault distance estimation accuracy up to 99.7% with 30 dB contaminated signal-to-noise ratio (SNR) for the nearest lines from the measured terminal.

Research on 3D Fingerprint Positioning Based on MIMO Base Station

The aim of this article is to solve the problem that the accuracy of traditional positioning algorithm decreases in complex environment and to provide some ideas for the few researches of fingerprint localization algorithm in three-dimensional space. This paper builds a system model in a three-dimensional space, provides three reference point distribution methods, and discusses the positioning performance under these distribution methods. After that, based on the high base station deployment density, multi-point fusion positioning method is used to locate the target, which further improves the positioning accuracy and makes more effective use of reference point resources. Finally, a backward-assisted positioning method is proposed, which uses the position information of the positioned points to assist the positioning of the current point. Research shows that this method can improve the positioning accuracy and has good versatility. (Foundation items: Social Development Projects of Jiangsu Science and Technology Department (No.BE2018704).)

Mobile Industrial Robots Localization Algorithm Based on Improved Multidimensional Scale and Received Signal Strength Indication

Aiming at the problem of large error in the location algorithm based on MDS-MAP when the distance between mobile industrial robots is not measurable, a mobile industrial robot location algorithm based on improved MDS-MAP is proposed. Experimental simulation shows that the algorithm can achieve good positioning effect. When the distance between mobile industrial robots is measurable, the positioning algorithm based on RSSI achieves good positioning effect. Therefore, this paper discusses the influence of different anchor robot selection methods on the positioning accuracy of RSSI positioning algorithm. The experimental simulation shows that when the selection method of anchoring robot is that the unknown robot with adjacent anchoring robot uses the original anchoring robot for positioning and the unknown robot without anchoring robot uses the adjacent positioning robot as the anchoring robot for positioning, its positioning effect is the best, and it can still achieve good positioning effect when there are few anchoring robots.

UWB Indoor Localization Algorithm Using Firefly of Multistage Optimization on Particle Filter

With the increasing application potential of indoor personnel positioning, ultra-wideband (UWB) positioning technology has attracted more and more attentions of scholars. In practice, an indoor positioning process often involves multipath and Non-Line-Of-Sight (NLOS) problems, and a particle filtering (PF) algorithm has been widely used in the indoor positioning research field because of its outstanding performance in nonlinear and non-Gaussian estimations. Aiming at mitigating the accuracy decreasing caused by the particle degradation and impoverishment in traditional Sequential Monte Carlo (SMC) positioning, we propose a method to integrate the firefly and particle algorithm for multistage optimization. The proposed algorithm not only enhances the searching ability of particles of initialization but also makes the particles propagate out of the local optimal condition in the sequential estimations. In addition, to prevent particles from falling into the oscillatory situation and find the global optimization faster, a decreasing function is designed to improve the reliability of the particle propagation. Real indoor experiments are carried out, and results demonstrate that the positioning accuracy can be improved up to 36%, and the number of needed particles is significantly reduced.