A Robust Wi-Fi Fingerprinting Indoor Localization Coping with Access Point Movement

A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental environmental changes,such as AP movement. In order to address this problem,a robust fingerprinting indoor localization method is initiated. In the offline phase,three attributes of Received Signal Strength Indication( RSSI) —average,standard deviation and AP's response rate—are computed to prepare for the subsequent computation. In this way,the underlying location-relevant information can be captured comprehensively. Then in the online phase, a three-step voting scheme-based decision mechanism is demonstrated, detecting and eliminating the part of AP where the signals measured are severely distorted by AP 's movement. In the following localization step,in order to achieve accuracy and efficiency simultaneously,a novel fingerprinting localization algorithm is applied. Bhattacharyya distance is utilized to measure the RSSI distribution distance,thus realizing the optimization of MAximum Overlapping algorithm( MAO). Finally,experimental results are displayed,which demonstrate the effectiveness of our proposed methods in eliminating outliers and attaining relatively higher localization accuracy.

Highly reliable relative navigation for multi-UAV formation flight in urban environments

Formation flight of multiple Unmanned Aerial Vehicles(UAVs)is expected to bring significant benefits to a wide range of applications.Accurate and reliable relative position information is a prerequisite to safely maintain a fairly close distance between UAVs and to achieve inner-system collision avoidance.However,Global Navigation Satellite System(GNSS)measurements are vulnerable to erroneous signals in urban canyons,which could potentially lead to catastrophic consequences.Accordingly,on the basis of performing relative positioning with double differenced pseudoranges,this paper develops an integrity monitoring framework to improve navigation integrity(a measure of reliability)in urban environments.On the one hand,this framework includes a fault detection and exclusion scheme to protect against measurement faults.To accommodate urban scenarios,spatial dependence in the faults are taken into consideration by this scheme.On the other hand,relative protection level is rigorously derived to describe the probabilistic error bound of the navigation output.This indicator can be used to evaluate collision risk and to warn collision danger in real time.The proposed algorithms are validated by both simulations and flight experiments.Simulation results quantitatively reveal the sensitivity of navigation performance to receiver configurations and environmental conditions.And experimental results suggest high efficiency and effectiveness of the new integrity monitoring framework.

GNSS vulnerabilities:simulation,verification,and mitigation platform design

This paper introduces the design and construction of global navigation satellite systems(GNSS)vulnerability simulation,verification,and mitigation platform.The platform contains five modules:simulation of the signal-in-space environment,simulation of the vulnerabilities in the space segment,signal quality monitoring and data processing,vulnerability assessment and validation,and integrated control.It provides a set of integrated simulations of different types of interference in the GNSS signal propagation domain,including electromagnetic interference,atmospheric disturbances,multipath,and interference in the inter-satellite link.This paper focuses on the design of the main system modules and testing through an experimental analysis.The results demonstrate both the effectiveness and realism of the modules and overall platform.