Localization In Indoor Environments Research Articles

AN ADAPTIVE LOCALIZATION SYSTEM USING PARTICLE SWARM OPTIMIZATION IN A CIRCULAR DISTRIBUTION FORM

Tracking the user location in indoor environment becomes substantial issue in recent research High accuracy and fast convergence are very important issues for a good localization system. One of the techniques that are used in localization systems is particle swarm optimization (PSO). This technique is a stochastic optimization based on the movement and velocity of particles. In this paper, we introduce an algorithm using PSO for indoor localization system. The proposed algorithm uses PSO to generate several particles that have circular distribution around one access point (AP). The PSO generates particles where the distance from each particle to the AP is the same distance from the AP to the target. The particle which achieves correct distances (distances from each AP to target) is selected as the target. Four PSO variants, namely standard PSO (SPSO), linearly decreasing inertia weight PSO (LDIW PSO), self-organizing hierarchical PSO with time acceleration coefficients (HPSO-TVAC), and constriction factor PSO (CFPSO) are used to find the minimum distance error. The simulation results show the proposed method using HPSO-TVAC variant achieves very low distance error of 0.19 meter.

Experience based localization in wide open indoor environments

Abstract This paper solves the problem of localization for indoor environments using visual place recognition, visual odometry and experience based localization using a camera. Our main motivation is just like a human is able to recall from its past experience, a robot should be able to use its recorded visual memory in order to determine its location. Currently experience based localization has been used in constrained environments like outdoor roads, where the robot is constrained to the same set of locations during every visit. This paper adapts the same technology to wide open maps like halls wherein the robot is not constrained to specific locations. When a robot is turned on in a room, it first uses visual place recognition using histogram of oriented gradients and support vector machine in order to predict which room it is in. It then scans its surroundings and uses a nearest neighbor search of the robot’s experience coupled with visual odometry for localization. We present the results of our approach test on a dynamic environment comprising of three rooms. The dataset consists of approximately 5000 monocular and 5000 depth images.

장거리 고정밀 측위를 위한 UWB IR 시스템 구현

Recently, the interests of the precise localization are rapidly increasing, which are linked to IoT(Internet of Things) sensors. The precise localization in indoor environment can be utilized in navigation, security, anti-collision, and various location based services etc. However, conventional positioning sensors, such as PIR, ultrasonic, microwave etc. are vulnerable to weather or insensitive to direction of subject movement or low precision performance. In this paper we implement a UWB-IR localization system for long distance and high-precision localization, which is not affected by temperature, light and weather. The proposed system was divided and designed by H/W, Antenna, S/W parts, each of which was designed based on an accurate analysis and simulation. As a result, we can implemented and verified UWB IR system with precise localization performance.

Improving Human Hand Localization in Indoor Environment by Using Frequency Domain Analysis

Improving Human Hand Localization in Indoor Environment by Using Frequency Domain Analysis

Estimation of Robot Position and Orientation Using a Stationary Fisheye Camera

A core problem in robotics is the determination of the location and pose of a mobile robot in its environment. The localization is a basic operation, which must be successfully carried out in complex environments using imprecise and/or contaminated data and is essential for a broad range of mobile robot tasks, since the robot behavior depends on its position. In this work, we propose the use of a stationary fisheye camera for real time robot localization in indoor environments. We employ a model for the formation of the image by the fisheye camera, which can be used for accelerating the segmentation of the robot's top surface, as well as for calculating the robot's true position in the real world frame of reference. The proposed algorithm for robot localization exploits the calibrated fisheye camera model and the known dimensions of the robot, whereas it does not depend on any information from the robot's sensors and does not require visual landmarks in the indoor environment. Furthermore, the pose (orientation) of the robot is determined using a triangular shape placed on top of the robot's flat top surface, using Hu's moment invariants, appropriately modified using the calibrated fisheye camera model. Initial results are presented from video sequences and are compared to the ground truth position, obtained by the robot's sensors. The dependence of the average positional error with the distance from the camera is also measured.

Invariant EKF Design for Scan Matching-Aided Localization

Localization in indoor environments is a technique that estimates the robot’s pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera. We develop both an invariant extended Kalman filter (IEKF)-based and a multiplicative extended Kalman filter-based solution to this problem. The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design.

Open-source Localization Device for Indoor Mobile Robots

Determining own location in indoor environment forms the basis for the majority of tasks performed by mobile robots. Various approaches to this problem have been proposed over the last few decades, differing in the type of perceived data. The most reliable and accurate methods are based on detection of artificial markers placed in the environment. Surprisingly there are very few products available on the market, which would offer the functionality of determining mobile robot's position using artificial markers. Therefore we decided to design and build an affordable, robust and extensible localization device, which could be used in various robotics applications. The created device uses an ARM-based microcomputer and a dedicated camera to autonomously capture and process images of the environment in order to calculate its location. It is resistant to changing light conditions and offers the performance of more than 30 frames per second with average positioning error of less than 5cm. In this paper we present details concerning the hardware and software architecture of the device together with experimental results.

Three-dimensional indoor localisation system using radio frequency identification tags

Radio Frequency Identification (RFID) is an information exchange technology based on radio wave communication. It is also a possible solution to indoor localisation. Owing to multipath propagation and anisotropic interference in the indoor environment, theoretical propagation models are generally not sufficient for RFID-based localisation. In fact, the received radio frequency signal distribution may not even be monotonic and this makes range-based localisation algorithms less accurate. On the other hand, range-free localisation algorithms, such as k Nearest-Neighbour (kNN), require reference tags to be spread throughout the whole three-dimensional (3D) space which is frequently not practical. In this work, a hybrid real-time localisation algorithm that combines reference tags with Received Signal Strength Indicator (RSSI) ranging is introduced to improve RFID-based 3D localisation in indoor environments. The localisation algorithm is implemented in MATLAB and is synchronised with radio signal data in real-time. Results show that the proposed hybrid algorithm achieves an average 3D localisation error of 1.08 m which represents a significant improvement over algorithms that use only kNN or RSSI.

Dual-Band Sensor Network for Accurate Device-Free Localization in Indoor Environment with WiFi Interference

Dual-Band Sensor Network for Accurate Device-Free Localization in Indoor Environment with WiFi Interference

Joint TOA and DOA Localization in Indoor Environment Using Virtual Stations

This letter presents a geometrical localization approach that is able to locate a mobile station in non-line-of-sight (NLOS) and multipath conditions using time-of-arrival (TOA) and direction-of-arrival (DOA) estimates. The novelty of our work resides in converting the NLOS problem into a line-of-sight problem with virtual stations, even if the signal undergoes multiple-bound scattering, which has not been resolved in previous works. We also develop a two-step weighted least squares (TSWLS) positioning estimator using the hybrid TOA and DOA values. Simulation results demonstrate the effectiveness of the proposed method.

An Empirical Evaluation of a Probabilistic RF Signature for WLAN Location Fingerprinting

Localization for indoor environments has gained considerable attention over the last decade. The most popular technique is based on location fingerprinting using received signal strength (RSS) mainly due to the fact that it exploits the available wireless infrastructure and that RSS fingerprints are readily available using different wireless standards (IEEE 802.11, etc.). This simplicity however incurs a cost in accuracy and researchers focus on improving the performance from a pattern recognition perspective. Recently improvement in performance has been demonstrated using physical layer channel-based fingerprints such as the Channel Transfer Function (CTF) and Channel Impulse Response (CIR) at a cost of increased storage and computation requirements. In this paper we experimentally evaluate the performance of a probabilistic physical layer fingerprint that is based on entropy of the magnitude and phase of the CTF. We will show through extensive frequency domain channel measurements in an indoor office environment that entropy can be a practical alternative to RSS fingerprinting; where it shares the latter's simplicity of structure (scalar) but outperforms RSS and complex CIR fingerprints. We further investigate the impact of realistic channel and system impairments such as small-scale fading (Doppler), Signal-to-noise ratio (SNR) and interference on the performance of the proposed fingerprint signature.

WPAN에서 최대 측정거리 값을 이용한 이동객체 위치추정 보정 알고리즘

최근, 스마트폰의 GPS (Global Positioning System) 및 Wi-Fi를 이용한 위치기반서비스 (Location Based Services : LBS)에 대한 관심과 수요가 국내외에서 증가하고 있다. 위치추정 실험결과, WPAN (Wireless Personal Area Network)에서 실내 위치추정의 경우 고정노드와 이동노드간의 거리 측정 시 빈번하게 많은 오차 값이 발생함을 확인하였다. 본 논문에서는 최대 측정거리 값을 이용하여 이동객체의 위치추정 성능을 향상시킬 수 있는 위치추정 보정 알고리즘 (<TEX>$LCA_{MMV}$</TEX>)을 제안하고, 이동노드가 이동하는 상황을 3가지 시나리오로 구성하여 실험을 통해 성능을 분석하였다. 성능분석 결과, 제안한 알고리즘의 평균 위치추정 정확도는 SDS-TWR (Symmetric Double-Sided Two-Way Ranging)과 삼변측량법 보다 시나리오 1-3에서 각각 40.9cm, 77.6cm, 6.3cm 더 정확하게 측정됨을 확인하였다. Concerns and demands for the Location Based Services (LBS) using Global Positioning System (GPS) and Wi-Fi are largely increased in the world in the present. In some experimental results, it was noted that many errors are frequently occurred when the distances between an anchor node and a mobile node acre measured in indoor localization environment of Wireless Personal Area Network (WPAN). In this paper, localization compensation algorithm based on maximum measurement value (<TEX>$LCA_{MMV}$</TEX>) for moving objects in WPAN is proposed, and the performance of the algorithm is analyzed by experiments on three scenarios for movement of mobile nodes. From the experiments, it was confirmed that the average localization accuracy of suggested algorithm was more increased than Symmetric Double-Sided Two-Way Ranging (SDS-TWR) and triangulation as average 40.9cm, 77.6cm and 6.3cm, respectively on scenario 1-3.

Methods for Analysis of Spatio-Temporal Bluetooth Tracking Data

Analysis of people's movements represented by continuous sequences of spatio-temporal data tuples have received lots of attention in recent years. The focus of those studies was mostly GPS data recorded on a constant sample rate. However, the creation of intelligent location-aware models and environments also requires reliable localization in indoor environments as well as in mixed indoor/outdoor scenarios. In these cases, signal loss makes usage of GPS infeasible; therefore other recording technologies evolved. Our approach is analysis of episodic movement data. This data contains some uncertainties among time (continuity), space (accuracy), and the number of recorded objects (coverage). Prominent examples of episodic movement data are spatio-temporal activity logs, cell-based tracking data, and billing records. To give one detailed example, Bluetooth tracking monitors the presence of mobile phones and intercoms within a sensor's footprints. Usage of multiple sensors provides flows among the sensors. Most existing data mining algorithms use interpolation and therefore are infeasible for this kind of data. For example, speed and movement direction cannot be derived directly from episodic data; trajectories may not be depicted as a continuous line; and densities cannot be computed. Still, the data hold much information on group movement. Our approach is to aggregate movement in order to overcome the uncertainties. Deriving a number of objects for the spatio-temporal compartments and transitions among them gives interesting insights on the spatio-temporal behavior of moving objects. As a next step to support analysts, we propose clustering the spatio-temporal presence and flow situations. This work focuses as well on creation of a descriptive probability model for the movement based on Spatial Bayesian Networks. We present our methods on a real world data set collected during a football game in Nîmes, France in June 2011.

Three-Dimensional Gas-Source Localization in Indoor Environment

Gas sensors are used to detect the presence of gaseous chemical species in air. Once the target gas is detected, it is often required to track the gas and locate its source. In principle, the gas source location can be determined by tracking the spatial gas concentration gradient, which can be measured using a spatially aligned array of gas sensors. However, it is not an easy task as it appears to be. Diffusion of gas molecules into air is generally extremely slow. Even in a closed room with no ventilation, there exists convective airflow, and its velocity dominates the molecular diffusion. Therefore, the gas molecules released from their source are carried by airflow and form an aerial trail. Since the gas plume thus formed has a shape elongated in the downwind direction, the gas concentration gradient along the airflow direction is extremely small. Moreover, the airflow in environments of practical interest is almost always turbulent. Since the airflow direction and velocity fluctuate randomly, the gas concentration measurements are unstable.In order to overcome these issues, we have proposed a directional gas-plume detector that consists of an anemometer in addition to an array of gas sensors. Here we report the experimental results on three-dimensional tracking of a gas plume under various conditions in an indoor environment. The main element of the gas-plume detector is an ultrasonic anemometer (Model 81000, Young). It allows accurate measurement of the three-dimensional direction of the airflow for a wide range of velocities ranging from 1 cm/s to 40 m/s. Six metal-oxide gas sensors (TGS2620, Figaro Engineering) are aligned around the anemometer, and are used to estimate the three-dimensional gas concentration gradient vector. The direction of the gas source is obtained by taking a linear combination of the airflow velocity vector and the gas concentration gradient vector. Owing to the elongated shape of the gas plume, the gas concentration gradient vector points not directly to the location of the gas source but rather toward the centerline of the plume. Meanwhile, if the presence of a gas plume is detected by the gas sensors, its source is estimated to be in the upwind direction. Therefore, the airflow velocity vector and the gas concentration gradient vector allow us to track the gas plume in the upwind direction while staying near the plume centerline. In the gas-source localization experiments, a smoke generator (SLN-200, Nissho Electric Works) was used as a gas source. White smoke of glycol mist was released from the smoke generator, and a smoke plume was formed in an experimental room. The smoke is detectable with the gas sensors, and at the same time, allows us to visually check the extension of the plume. The plume detector was fixed on a tripod, and was first placed in the downstream part of the plume. Then, the sensor readings were measured for 60 s, and the detector was manually moved by 20 cm in the estimated direction of the gas source. By repeating this process, the plume detector succeeded in tracking the gas plume three-dimensionally. As the detector approached the gas source, the plume becomes narrower. Therefore, a slight change in the airflow direction causes large fluctuations in the gas concentration at the locations of the gas sensors. As a result, the standard deviations of the gas sensor responses in the 60-s time period increased as the detector approached the gas source. These standard deviations can be used as indices showing the proximity of the gas-plume detector to the gas source.

Low Cost Metric for Comparing the Localization Efficacy of WLAN Access Points Using RF Site Survey Data

Location fingerprinting utilizes periodic beacons transmitted by Wireless Local Area Network (WLAN) Access Points (APs) to provide localization in indoor environments. Currently no method is able to quantify the effectiveness of localization information provided by individual APs. Such a metric would enable the optimal placement of new APs as well as eliminating redundant APs so as to reduce the resources consumed by indoor localization software in client devices. This paper proposes LocationInfo, a metric that utilizes walk test data for quantifying the localization efficacy of APs. The performance of LocationInfo is evaluated using two experimental settings. First, it is used for identifying the optimal location for new APs. Second, it is used for filtering out excess APs in a crowded WLAN environment. In both experiments, LocationInfo outperforms existing metrics.

New algorithm for footstep localization using seismic sensors in an indoor environment

Abstract In this study, we consider the use of seismic sensors for footstep localization in indoor environments. A popular strategy of localization is to use the measured differences in arrival times of source signals at multiple pairs of receivers. In the literature, most algorithms that are based on time differences of arrival (TDOA) assume that the propagation velocity is a constant as a function of the source position, which is valid for air propagation or even for narrow band signals. However a bounded medium such as a concrete slab (encountered in indoor environment) is usually dispersive and damped. In this study, we demonstrate that under such conditions, the concrete slab can be assimilated to a thin plate; considering a Kelvin–Voigt damping model, we introduce the notion of perceived propagation velocity, which decreases when the source-sensor distance increases. This peculiar behavior precludes any possibility to rely on existing localization methods in indoor environment. Therefore, a new localization algorithm that is adapted to a damped and dispersive medium is proposed, using only on the sign of the measured TDOA (SO-TDOA). A simulation and some experimental results are included, to define the performance of this SO-TDOA algorithm.

Forecast-Driven Enhancement of Received Signal Strength (RSS)-Based Localization Systems

Real-time user localization in indoor environments is an important issue in ambient assisted living (AAL). In this context, localization based on received signal strength (RSS) has received considerable interest in the recent literature, due to its low cost and energy consumption and to its availability on all wireless communication hardware. On the other hand, the RSS-based localization is characterized by a greater error with respect to other technologies. Restricting the problem to localization of AAL users in indoor environments, we demonstrate that forecasting with a little user movement advance (for example, when the user is about to leave a room) provides significant benefits to the accuracy of RSS-based localization systems. Specifically, we exploit echo state networks (ESNs) fed with RSS measurements and trained to recognize patterns of user’s movements to feed back to the RSS-based localization system.

Optimal receiver antenna location in indoor environment using dynamic differential evolution and genetic algorithm

Abstract Using the impulse responses of these multipath channels, the bit error rate (BER) performance for binary pulse amplitude modulation impulse radio ultra-wideband communication system is calculated. The optimization location of receiving antenna is investigated by dynamic differential evolution (DDE) and genetic algorithm (GA) to minimize the outage probability. Numerical results show that the performance for reducing BER and outage probability by DDE algorithm is better than that by GA.

Efficient Sensor Localization for Indoor Environments Using Classification of Link Quality Patterns

In ubiquitous sensor networks (USN), sensor node localization is normally performed with global positioning system (GPS) or radio signal strength (RSS) between a target node and reference nodes. Because GPS is not available in indoor environments, RSS-based approach is commonly used for indoor localization. However, RSS-based approach is hard to be applied to real indoor environments (e.g., home) because of signal interferences with various indoor obstacles such as walls, doors, furniture, and electric appliances. In this paper, we propose an efficient indoor localization method for Zigbee sensor nodes by classifying link quality indicator (LQI) patterns between a target node and multiple reference nodes rather than using calculation with RSS values. And we also present the results of indoor localization experiments in our ubiquitous home network test bed using the proposed localization method.

A multiclassifier approach for topology-based WiFi indoor localization

People localization is required for many novel applications like for instance proactive care for the elders or people suffering degenerative dementia such as Alzheimer's disease. This paper introduces a new system for people localization in indoor environments. It is based on a topology-based WiFi signal strength fingerprint approach. Accordingly, it is a robust, cheap, ubiquitous and non-intrusive system which does require neither the installation of extra hardware nor prior knowledge about the structure of the environment under consideration. The well-known curse of dimensionality critically emerges when dealing with complex environments. The localization task turns into a high dimensional classification task. Therefore, the core of the proposed framework is a fuzzy rule-based multiclassification system, using standard methodologies for the component classifier generation such as bagging and random subspace, along with fuzzy logic to deal with the huge uncertainty that is characteristic of WiFi signals. Achieved results in two real environments are encouraging, since they clearly overcome those ones provided by the well-known nearest neighbor fingerprint matching algorithm, which is usually considered as a baseline for WiFi localization.