Qiang Chang

Scalable Indoor Localization via Mobile Crowdsourcing and Gaussian Process

Indoor localization using Received Signal Strength Indication (RSSI) fingerprinting has been extensively studied for decades. The positioning accuracy is highly dependent on the density of the signal database. In areas without calibration data, however, this algorithm breaks down. Building and updating a dense signal database is labor intensive, expensive, and even impossible in some areas. Researchers are continually searching for better algorithms to create and update dense databases more efficiently. In this paper, we propose a scalable indoor positioning algorithm that works both in surveyed and unsurveyed areas. We first propose Minimum Inverse Distance (MID) algorithm to build a virtual database with uniformly distributed virtual Reference Points (RP). The area covered by the virtual RPs can be larger than the surveyed area. A Local Gaussian Process (LGP) is then applied to estimate the virtual RPs’ RSSI values based on the crowdsourced training data. Finally, we improve the Bayesian algorithm to estimate the user’s location using the virtual database. All the parameters are optimized by simulations, and the new algorithm is tested on real-case scenarios. The results show that the new algorithm improves the accuracy by 25.5% in the surveyed area, with an average positioning error below 2.2 m for 80% of the cases. Moreover, the proposed algorithm can localize the users in the neighboring unsurveyed area.

A Crowd-Sourcing Indoor Localization Algorithm via Optical Camera on a Smartphone Assisted by Wi-Fi Fingerprint RSSI.

Indoor positioning based on existing Wi-Fi fingerprints is becoming more and more common. Unfortunately, the Wi-Fi fingerprint is susceptible to multiple path interferences, signal attenuation, and environmental changes, which leads to low accuracy. Meanwhile, with the recent advances in charge-coupled device (CCD) technologies and the processing speed of smartphones, indoor positioning using the optical camera on a smartphone has become an attractive research topic; however, the major challenge is its high computational complexity; as a result, real-time positioning cannot be achieved. In this paper we introduce a crowd-sourcing indoor localization algorithm via an optical camera and orientation sensor on a smartphone to address these issues. First, we use Wi-Fi fingerprint based on the K Weighted Nearest Neighbor (KWNN) algorithm to make a coarse estimation. Second, we adopt a mean-weighted exponent algorithm to fuse optical image features and orientation sensor data as well as KWNN in the smartphone to refine the result. Furthermore, a crowd-sourcing approach is utilized to update and supplement the positioning database. We perform several experiments comparing our approach with other positioning algorithms on a common smartphone to evaluate the performance of the proposed sensor-calibrated algorithm, and the results demonstrate that the proposed algorithm could significantly improve accuracy, stability, and applicability of positioning.

Indoor-Outdoor Detection Using a Smart Phone Sensor

In the era of mobile internet, Location Based Services (LBS) have developed dramatically. Seamless Indoor and Outdoor Navigation and Localization (SNAL) has attracted a lot of attention. No single positioning technology was capable of meeting the various positioning requirements in different environments. Selecting different positioning techniques for different environments is an alternative method. Detecting the users’ current environment is crucial for this technique. In this paper, we proposed to detect the indoor/outdoor environment automatically without high energy consumption. The basic idea was simple: we applied a machine learning algorithm to classify the neighboring Global System for Mobile (GSM) communication cellular base station’s signal strength in different environments, and identified the users’ current context by signal pattern recognition. We tested the algorithm in four different environments. The results showed that the proposed algorithm was capable of identifying open outdoors, semi-outdoors, light indoors and deep indoors environments with 100% accuracy using the signal strength of four nearby GSM stations. The required hardware and signal are widely available in our daily lives, implying its high compatibility and availability.

Cooperative Positioning Based on Hybrid Information

A new localization method has been proposed to overcome the limitations of systems relying on GPS or other terrestrial infrastructure. This method fuses both pseudorange measurements from GNSS satellites and RSSI-based ranging measurements between peers of a wireless network, and uses improved collaborative subtree algorithm to partition the network. In each collaborative subtree, the nodes’ positions can be computed by using the least square algorithm based on Taylor series expansion-based. Simulation results showed that this method improves both availability and positioning accuracy.

Crowdsourced Fingerprint Localization Using Virtual Radio Map

The requirement of indoor localization draws a new challenge to the positioning technique. As the widely availability of WLAN infrastructures, wireless signal fingerprint localization has attracted a lot of attentions. However, it is challenging due to the complexities of the indoor radio propagation characteristics exacerbated by the frequent change of indoor environment and the mobility of the user, the positioning accuracy cannot be guaranteed. Researchers propose crowdsourced fingerprint localization. But designing a sustainable incentive mechanism of crowdsourcing remains a challenge. We propose a virtual radio map based crowdsourcing fingerprint indoor localization algorithm. The basic idea behind our proposed algorithm is simple: we propose Local Gaussian Process to create a virtual database using the training signal database. The virtual database, contains fixed number of reference points, is used for positioning. And the training database, created by user crowdsourcing, is used for updating the virtual database. Simulation results show that our algorithm improves the accuracy for more than 30 %. And the improvement keeps increasing as the change of indoor environment. A small scale experiment proves the efficiency of the algorithm.

A Single-Phase AC/DC Conversion Circuit with APFC

This system uses the MSP430F6638 and FPGA as the core, includes full bridge rectifier circuit, power factor correction circuit, Boost/Buck circuit and sample circuit. According to the sampling voltage and current value, The FPGA adjusts PWM wave duty ratio of power factor correction chip UC3854, completing single phase AC/DC conversion circuit with a voltage outer closed-loop and current inner closed-loop and active power factor correction (APFC). Test shows that the output voltage can stable in a setting value, with amplitude range ±0.05V, and when load’s impedance changes, this circuit can keep a small voltage adjustment rate and load adjustment rate. We use electronic parameter measurement device to measure the efficiency of AC/DC converter in a specified condition (voltage input Us=24V, current output Io=2A, voltage output Uo=36V), result demonstrates it’s a relatively ideal value. At the same time, under the control of UC3854, the power factor of alternating current input is 0.9734.And when we set the range of power factor between 0.8~1.0, The circuit can automatically adjust the power factor to track the set value.

Reliability Analysis for the Navstar Based on State Probability of the Constellation

The definition for the constellation availability of the navigation system is given firstly, and then calculates the availability of a single satellite by using Markov processes, and establishes the model based on the state probability of the constellation, to achieve mapping model from component reliability of the single satellite to the constellation availability. Finally, we analysis the regional availability performance of the GNSS by the algorithm, which can fit and satisfy the requirements of contradiction between accuracy and rapidity for analysis of availability for constellation of GNSS.

A Parallel Simulation Method for Performance Analysis of GNSS

In this paper, we proposed a parallel simulation method for performance analysis of the Global Navigation Satellite System (GNSS) based on simulation model portability 2(SMP2) and service-oriented modeling method. GNSS is a space engineering system with a large-scale and complex structure, and the proposed method can be used to construct large complex simulation systems to gain the reusability, composability and interoperability of heterogeneous simulation resources. Firstly, the method including the conceptual framework, system architecture and system engineering process is introduced. Then the parallel model development, composition and schedule method are detailed respectively. Finally, a distributed M&S environment based on service-oriented SMP2 is designed, and an example of navigation system volume simulation is given to validate the whole method.

A Model Integration Framework Based Portability for GNSS Simulation

Performance Simulation for Global Navigation Satellite System (GNSS) that be used for performance simulation and evaluation analysis of the GNSS, is a part of the critical algorithm test and system index analysis for GNSS. In this article, the requirements of GNSS are analyzed firstly, and the characteristic of model portability for GNSS is studied, then the model integration framework based on Simulation Model Portability is proposed. The system architecture consists of model design, development, integrating, executing and analysis. Lastly, the regional performance of GALILEO Navigation Satellite System in China is tested and analyzed based on the integration framework.

An Anchor Selection Algorithm in Wireless Sensor Network

Wireless sensor networks consist of two kinds of nodes: the anchor and the Agent. The anchor is equipped with special hardware to obtain precise location information and employed to derive the locations of Agents. Due to the resource-limited nature of single sensors, actively participating nodes should be kept to a proper number. Based on an investigation on the trade-off between the localization accuracy and the computation complexity of sensor nodes, we propose a distributed algorithm to select subsets of anchor nodes for localization and analyze this algorithm regarding the energy consumption of every node.