Indoor Location in WLAN Based on Competitive Agglomeration Algorithm

Abstract In the area of Wireless Local Area Network (WLAN) based indoor localization, the k-nearest neighbors (KNN) fusion clustering algorithm has been studied extensively. But the number of the clustering and the value of K is set manually and fixed, so it can't adapt to the environment changes. Besides, the algorithm localization with a single Received Signal Strength (RSS), and ignored other deeper information such as the physical location information. Aiming at the shortcomings of the fusion algorithm, in this paper, we proposed a novel indoor localization algorithm based on competitive agglomeration (CA). The algorithm soft partition radio map based on RSS and physical location information in succession, and select the clustering number based on real time information in the environment to estimate user's position coordinates. Finally, based on the extensive experiments conducted in a real WLAN indoor environment, our proposed algorithm is proved to outperform traditional positioning algorithm

Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error

The emerging techniques in the Fifth Generation (5G) communication system, like the millimeter-Wave (mmWave) and massive Multiple Input Multiple Output (MIMO), make it possible to measure the Angle-Of-arrival (AOA), Receive Signal Strength (RSS) and Time-Of-flight (TOF) by using various types of mobile devices. At the same time, there is always significant interest in the high-precision localization techniques based on the joint metric of AOA/RSS/TOF, which enable one to overcome the drawback of the single metric-based localization. Motivated by this concern, we rely on the Cramer–Rao Lower Bound (CRLB) to analyze the localization errors of RSS/AOA, RSS/TOF, AOA/TOF and the Joint Metric of AOA/RSS/TOF (JMART)-based localization. The error bounds derived in this paper can be selected as the benchmarking results to evaluate the indoor localization performance. Finally, extensive simulations are conducted to support our claim

Experimental Study on Compression Failure of Composite Laminates with Prefabricated Surface Cracks

A new compression test fixture was designed in the present work to study the damage tolerance of composite laminates with surface cracks or notches. The compression failure behaviors of CCF300/5228A quasi-isotropic composite laminates with prefabricated surface cracks were studied experimentally. Through the size design of the test fixture and specimens and an application of a simple test method, the complex crack growth process was captured. The experimental results showed that the compression failure modes were mainly affected by crack angles and depths, and there were two typical failure modes, which were local intra- and inter-laminar damage propagating from the crack tips and delamination growth induced from the crack leading edge. This study verified the validity of the test fixture and test method, and revealed the compression failure mechanisms of composite laminates with surface cracks

Indoor Localization Using Semi-Supervised Manifold Alignment with Dimension Expansion

Location estimation plays a crucial role in Location-Based Services (LBSs) with satisfactory user experience. The Wireless Local Area Network (WLAN) localization approach is preferred as a cost-efficient solution to indoor localization on account of the widely-deployed WLAN infrastructures. In this paper, we propose a new WLAN Received Signal Strength (RSS)-based indoor localization approach using the semi-supervised manifold alignment with dimension expansion. In concrete terms, we first construct an innovative objective function based on the augmented physical coordinates and the corresponding WLAN RSS measurements. Second, the closed-form solution to the objective function is derived out according to the Lagrange multiplier equation, which results in the manifold in physical coordinate space. Third, the target location is estimated by matching the transformed newly-collected RSS against the manifold. The localization performance with noise perturbation is analyzed upon the constructed objective function, and meanwhile, the closed-form solution to the objective function with respect to multiple types of measurements is also derived out for the sake of leveraging all of the potential measurements for indoor localization. The extensive testing results show that the proposed approach performs well in localization accuracy even at low calibration load, and its performance can be further improved by using multiple types of measurements for localization

Hardware and software design of BMW system for multi-floor localization

Abstract Although the Micro Electro Mechanical System (MEMS) sensors are capable of providing short-term high positioning accuracy, every positioning result significantly depends on the historical ones, which inevitably leads to the long-term error accumulation. The Bluetooth Low Energy (BLE) is independent of the accumulative error, but the positioning accuracy is suffered by the irregular jump error resulted from the Received Signal Strength Indicator (RSSI) jitter. Considering the requirement of accurate, seamless, and consecutive positioning by the existing commercial systems, we propose a new integrated BLE and MEMS Wireless (BMW) system for multi-floor positioning. In concrete terms, first of all, the way of fingerprint database construction with the reduced workload is introduced. Second, the fingerprint database is denoised by the process of affinity propagation clustering, outlier detection, and RSSI filtering. Third, the robust M estimation-based extended Kalman filter is applied to estimate the two-dimensional coordinates of the target on each floor. Finally, the barometer data are used to calculate the height of the target. The extensive experimental results show that the proposed system can not only restrain the accumulative error caused by the MEMS sensors but also eliminate the irregular jump error from the BLE RSSI jitter. In an actual multi-floor environment, the proposed system is verified to be able to achieve the Root Mean Square (RMS) positioning error within 1 m

Low-Cost BD/MEMS Tightly-Coupled Pedestrian Navigation Algorithm

Pedestrian Dead Reckoning (PDR) by combining the Inertial Measurement Unit (IMU) and magnetometer is an independent navigation approach based on multiple sensors. Since the inertial component error is significantly determined by the parameters of navigation equations, the navigation precision may deteriorate with time, which is inappropriate for long-time navigation. Although the BeiDou (BD) navigation system can provide high navigation precision in most scenarios, the signal from satellites is easily degraded because of buildings or thick foliage. To solve this problem, a tightly-coupled BD/MEMS (Micro-Electro-Mechanical Systems) integration algorithm is proposed in this paper, and a prototype was built for implementing the integrated system. The extensive experiments prove that the BD/MEMS system performs well in different environments, such as an open sky environment and a playground surrounded by trees and thick foliage. The proposed algorithm is able to provide continuous and reliable positioning service for pedestrian outdoors and thereby has wide practical application

Smartphone-Based Indoor Integrated WiFi/MEMS Positioning Algorithm in a Multi-Floor Environment

Indoor positioning in a multi-floor environment by using a smartphone is considered in this paper. The positioning accuracy and robustness of WiFi fingerprinting-based positioning are limited due to the unexpected variation of WiFi measurements between floors. On this basis, we propose a novel smartphone-based integrated WiFi/MEMS positioning algorithm based on the robust extended Kalman filter (EKF). The proposed algorithm first relies on the gait detection approach and quaternion algorithm to estimate the velocity and heading angles of the target. Second, the velocity and heading angles, together with the results of WiFi fingerprinting-based positioning, are considered as the input of the robust EKF for the sake of conducting two-dimensional (2D) positioning. Third, the proposed algorithm calculates the height of the target by using the real-time recorded barometer and geographic data. Finally, the experimental results show that the proposed algorithm achieves the positioning accuracy with root mean square errors (RMSEs) less than 1 m in an actual multi-floor environment

PILA: Sub-Meter Localization Using CSI from Commodity Wi-Fi Devices

The aim of this paper is to present a new indoor localization approach by employing the Angle-of-arrival (AOA) and Received Signal Strength (RSS) measurements in Wi-Fi network. To achieve this goal, we first collect the Channel State Information (CSI) by using the commodity Wi-Fi devices with our designed three antennas to estimate the AOA of Wi-Fi signal. Second, we propose a direct path identification algorithm to obtain the direct signal path for the sake of reducing the interference of multipath effect on the AOA estimation. Third, we construct a new objective function to solve the localization problem by integrating the AOA and RSS information. Although the localization problem is non-convex, we use the Second-order Cone Programming (SOCP) relaxation approach to transform it into a convex problem. Finally, the effectiveness of our approach is verified based on the prototype implementation by using the commodity Wi-Fi devices. The experimental results show that our approach can achieve the median error 0.7 m in the actual indoor environment

Cost-efficient BLE fingerprint database construction approach via multi-quadric RBF interpolation

Abstract The demand for indoor localization is becoming urgent, but the traditional location fingerprint approach takes a lot of manpower and time to construct a fine-grained location fingerprint database. To address this problem, we propose to use the approach of combining dynamic collection of fingerprint samples with Radial Basis Function (RBF) interpolation. Specifically, the raw sparse fingerprint database is constructed from a small number of fingerprints collected on a few paths, in which the pedestrian track correction algorithm improves the validity and accuracy of the sparse fingerprint database. Then, the RBF interpolation approach is applied to enrich the sparse fingerprint database, in which the Genetic Algorithm (GA) is used to optimize the free shape parameter and the cut-off radius is determined according to the experimental results. Extensive experiments show that the proposed approach guarantees high interpolation and localization accuracy and also significantly reduces the effort of manual collection of fingerprint samples