Pedestrian Positioning Using a Double-Stacked Particle Filter in Indoor Wireless Networks

In this paper, we propose a surface correlation based fingerprinting method in sparse Wi-Fi environment. The proposed method can estimate location even with very few Wi-Fi APs by using accumulated spatial RSS pattern. We verify performance of the proposed method through several tests in a building with complex paths. Many studies have developed localization technologies that use measurements such as RSS (Received Signal Strength) or RTT (Round Trip Time) for Wi-Fi signal. All RF have signal noise and distortion problems as well as multipath problem in indoor environments. As the distance from the RF signal source increases, the discrimination of the RSS decreases due to RF signal noise and distortion. Therefore, ambiguity of RSS increases as the distance increases, the performance deteriorates. The conventional RF based localization method have tried to maintain dense RF environment to guarantee stable and accurate performance. However, in order to maintain the dense RF environment, it is becoming a barrier to commercialization of localization technology due to high cost of constructing RF infrastructure and maintenance difficulty. In this paper, we propose a Surface Correlation (SC) based fingerprinting technique to maintain high positioning performance even with few Wi-Fi APs. The surface is the accumulated RSS pattern data during the movement of a pedestrian, which means the spatial RSS pattern. Conventional fingerprinting methods using kNN (k-Nearest Neighbors) and WkNN (Weighted kNN) are very vulnerable to signal noise and distortion because they utilize instantaneous RSS data. The performance of Particle Filter (PF) based localization technology is also degraded in areas where Wi-Fi AP is sparsely distributed. However, the proposed method is very robust against signal noise and distortion because it compares the spatial RSS pattern with the reference RSS pattern on the fingerprinting database. This increases localization accuracy and availability for Wi-Fi coverage. For localization, the proposed method conduct correlation process for the spatial RSS pattern of a pedestrian with reference RSS pattern of database. So, the reference RSS pattern should be extracted from a reference trajectory that matches a pedestrian`s walking path. The proposed method utilize a database of ‘link-node’ structure to dynamically generate a reference trajectory in indoor environment with complicated path. It generate candidate reference trajectories using only MAC address and turn event of Pedestrian Dead-Reckoning (PDR). The proposed method fix a reference trajectory between these candidate trajectories through correlation process of measured RSS pattern with each reference RSS pattern. A reference point on the fixed reference having most similar RSS pattern to a pedestrian`s is considered as a current location. In order to improve performance at a fork where RSS ambiguity increases, the proposed method estimates preferentially for a reference trajectory matching with PDR turn event. The turn event can be wrong due to various motion of a pedestrian. The proposed method selects a correct reference trajectory by continuously performing correlation process on candidate reference trajectories. In order to verify the performance of the proposed surface correlation based fingerprinting method, we conducted several tests in a three-story building. Before the tests, we conducted a survey of Wi-Fi environment to construct fingerprinting database in the building. As a result of the survey, 170 APs are distributed in the building. We reduced the number of APs by 20% from 170 to 17, and analyzed performance of the proposed and conventional localization methods. The test results show that the performance of the conventional localization methods degrade as the number of applied APs decreases. The proposed fingerprinting method shows a 3 meter localization error even with very few Wi-Fi APs, and unlike the conventional fingerprinting method, the localization error is bounded to be within 5m due to the use of spatial RSS pattern. In this paper, we presented the fingerprinting method using the surface correlation in sparse Wi-Fi environment. The proposed fingerprinting method accumulates Wi-Fi RSS during movement of a pedestrian. The proposed method compares the spatial RSS pattern, which is accumulated RSS data, with reference RSS patterns of fingerprinting database. By comparing the RSS patterns, the proposed method greatly improves accuracy as well as stability. In addition, it can be effectively applied in a sparse Wi-Fi environment by improving the availability of ambiguous areas of Wi-Fi coverage. Through these results, we expect to be able to provide accurate and reliable location information even in indoor weak RF environment.

For the traditional fingerprinting-based positioning approach, it is essential to collect measurements at known locations as reference fingerprints during a training phase, which can be time-consuming and labor-intensive. This paper proposes a novel approach to track a user in an indoor environment by integrating similarity-based sequence and dead reckoning. In particular, we represent the fingerprinting map as location sequences based on distance ranking of the APs (access points) whose positions are known. The fingerprint used for online positioning is represented by a ranked sequence of APs based on the measured Received Signal Strength (RSS), which is refereed to as RSS sequence in this paper. Embedded into a particle filter, we achieve the tracking of a mobile user by fusing the sequence-based similarity and dead reckoning. Extensive experiments are conducted to evaluate the proposed approach.

With growing technology, Location Based Services (LBSs) are becoming a vital part of life. The rapid development of mobile internet has offered an opportunity for Wi-Fi indoor positioning to come under the spotlight due to its low cost. Here, indoor positioning is performed using Wi-Fi. The earlier solutions like GPS does not provide efficient positioning in indoor environment due to weak signal reception which makes Wi-Fi more reliable. However, nowadays the accuracy of the traditional indoor positioning algorithms like trilateration cannot meet the practical requirement. This paper proposes the performance analysis of indoor positioning algorithms using Wi-Fi technology based on Received Signal Strength (RSS) from the access points in the network. The positioning algorithms are based on location fingerprinting consisting of two stages: The offline database creation and the online positioning. The RSS values obtained by the mobile device at reference points are sent to the server where all the location fingerprints of all reference points are stored in a database. Coordinates of user position are calculated using positioning algorithms and the position is displayed in terms of 2Dcoordinates of the room and indoor map. Basic Fingerprint algorithm, RSS Fingerprint with minimum Euclidean Distance algorithm and RSS Fingerprint with Euclidean Distance and k-nearest neighbors' algorithm were implemented. Moreover comparison of their performance was carried out in terms of accuracy of position. From the result, it is found that an average accuracy of 0.4962 meters was achieved by RSS Fingerprint with minimum Euclidean distance algorithm.

In location fingerprinting based indoor positioning system, received signal strength (RSS) indications from a set of Wi-Fi access points are used as a unique fingerprint to identify a specific location. However these RSS fingerprints may become outdated when there are unanticipated environmental changes. Re-measuring RSS fingerprints for all locations to maintain an up-to-date RSS database incurs high operational cost, which is impractical in dynamically changed environment. In this paper, we propose a method to estimate the RSS fingerprint of a specific location from a set of neighboring re-measured RSS fingerprints, called “feedbacks”. The proposed method searches for new feedbacks and some necessary old RSS fingerprints in the cut-off area and then applies plane-interpolation to calculate the new RSS fingerprint for a specific location. Based on simulation results, about 5% of re-measured RSS feedbacks are required to satisfy 80% of positioning correctness in the simulated 30×30 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> area.

Received signal strength (RSS) fingerprint-based indoor localization has received increasing popularity over the past decades. However, it suffers from the high calibration effort for fingerprint collection. In this paper, a Centralized indooR localizatioN method using Pseudo-label (CRNP) is proposed, which employs a small set of labeled data (RSS fingerprint) along with large volumes of unlabeled data (RSS values without coordinates) to reduce the workload of labeled data collection and improve the indoor localization performance. However, the rich location data is large in quantity and privacy sensitive, which may lead to high network cost (i.e., data transmission cost, data storage cost) and potential privacy leakage for data transmission to the central server. Therefore, a decentralized indoor localization method incorporating CRNP and federated learning is devised, which keeps the location data on local users' devices and improves the shared CRNP model by aggregating users' updates of the model. The experiment results demonstrate that (i) the proposed CRNP enables to improve the indoor localization accuracy by using unlabeled crowdsourced data; (ii) the designed decentralized scheme is robust to different data distribution and is capable to reduce the network cost and prevent users' privacy leakage.

Abstract. Indoor localization has attracted the attention of researchers for wide applications in areas like construction, facility management, industries, logistics, and health. The Received Signal Strength (RSS) based fingerprinting method is widely adopted because it has a lower cost over other methods. RSS is a measurement of the power present in the received radio signal. While this fingerprinting method is very popular, there is a significant amount of effort required for collecting fingerprints for indoor space. In this paper, we propose an RSS fingerprinting method using Augmented Reality (AR) that does not rely on an external sensor resulting in ease of use and maintenance. This method uses spatial mapping techniques to help align the floor plan of existing buildings; then, after the alignment, we map local device coordinates to global coordinates. After this process, we partition the space in equally distanced reference points for RSS fingerprint collection. We developed an application for Microsoft HoloLens to align the floor plan and collect fingerprints on reference points. Then we tested collected fingerprints with existing RSS based indoor localization methods for its accuracy and performance.

Data assimilation is the process of estimating the state of dynamic systems (linear or nonlinear, Gaussian or non-Gaussian) as accurately as possible from noisy observational data. Although the Three Dimensional Variational (3D-VAR) methods, Four Dimensional Variational (4D-VAR) methods and Ensemble Kalman filter (EnKF) methods are widely used and effective for linear and Gaussian dynamics, new methods of data assimilation are required for the general situation, that is, nonlinear non-Gaussian dynamics. General Bayesian recursive estimation theory is reviewed in this thesis. The Bayesian estimation approach provides a rather general and powerful framework for handling nonlinear, non-Gaussian, as well as linear, Gaussian estimation problems. Despite a general solution to the nonlinear estimation problem, there is no closed-form solution in the general case. Therefore, approximate techniques have to be employed. In this thesis, the sequential Monte Carlo (SMC) methods, commonly referred to as the particle filter, is presented to tackle non-linear, non-Gaussian estimation problems. In this thesis, we use the SMC methods only for the nonlinear state estimation problem, however, it can also be used for the nonlinear parameter estimation problem. In order to demonstrate the new methods in the general nonlinear non-Gaussian case, we compare Sequential Monte Carlo (SMC) methods with the Ensemble Kalman Filter (EnKF) by performing data assimilation in nonlinear and non-Gaussian dynamic systems. The models used in this study are referred to as state-space models. The Lorenz 1963 and 1966 models serve as test beds for examining the properties of these assimilation methods when used in highly nonlinear dynamics. The application of Sequential Monte Carlo methods to different fixed parameters in dynamic models is considered. Four different scenarios in the Lorenz 1063 [sic] model and three different scenarios in the Lorenz 1996 model are designed in this study for both the SMC methods and EnKF method with different filter siz

With the technical advances of wireless sensor networking and smart mobile device, the demand for position information of pedestrians (especially in the indoor environment) has increased remarkably. In this paper, we proposed an indoor localization approach based on received signal strength (RSS) and Bayesian filter. In the following sections, we describe our virtual modeling method of environment and the way we take object's movement sequences as history conditions in Bayesian filter. The experiment results show that our solution provides accurate tracking results (within 80 centimeters for moving object). The contribution of this research is that it provides a general implementation utilizing Bayesian filter which is able to estimate location precisely with off-the-shelf hardware. And Bluetooth Low Energy (BLE) is employed which reduces power consumption considerably. Meanwhile the accuracy is sufficient for pedestrian tracking in real application scenarios where BLE devices can be easily deployed. Simply connecting static points will distort the real trajectory. One solution for this problem is filter technique. Filter technique is not only can be used in signal measurements, but also on general position estimations. In this paper we propose a Bluetooth indoor pedestrian tracking system which employs RSS as scene profiling basis. During the tracking process, our system keeps iterating a recursion of prediction and update in which Bayes filter (4) is the mathematical fundamental. Other than the classic procedure of filtering the measurement data only, our filter integrates context information and history record as conditions for the estimation. Our evaluation shows that our combination of current measurements with history records increases both the accuracy and efficiency. The rest of this paper is organized as follows: Section II overviews fundamental and essential related works. Then Section III describes detailed implementation. After that in Section IV we present an evaluation of the proposed algorithm by comparing it with existing RSS-based fingerprint approaches. Finally, Section VI summarizes the main conclusions and gives directions for future work.

The computational capabilities of off-the-shelf wireless sensors networks presents a limitation when more complex forms of localization algorithms are employed for location estimation purposes, particularly in an indoor environment. Range-free algorithms rely on Received Signal Strength (RSS) from sensors that are location aware (anchor nodes) as the major means of distance estimation. This paper presents a non-site specific algorithm for better estimating RSS relationship with distance. By employing a unique form of rationalization of raw RSS with respect to distance using the proposed algorithm, it is possible to enhance the reliability of RSS when employed in indoor Localization Algorithms. Consequently, this paper presents an innovative RSS-Distance rationalization algorithm for localization of objects in an indoor environment. The paper compared the proposed algorithm with Simple Moving Average (SMA) algorithm due to the wide applicability and ease of manipulation of SMA. The analysis of the proposed algorithm and SMA shows that the proposed algorithm better modifies RSS for more accurate position estimation in an indoor environment.

Assessing the fundamental performance limitations in Bayesian filtering can be carried out using the parametric Cramer-Rao bound (CRB). The parametric CRB puts a lower bound on mean square error (MSE) matrix conditioned on a specific state trajectory realization. In this work, we derive the parametric CRB for state-space models, where the measurement equation is modeled by a Gaussian process regression. These models appear, for instance in proximity report-based positioning, where proximity reports are obtained by hard thresholding of received signal strength (RSS) measurements, that are modeled through Gaussian process regression. The proposed parametric CRB is evaluated on selected state trajectories and further compared with the positioning performance obtained by the particle filter. The results corroborate that the positioning accuracy achieved in this framework is close to the parametric CRB.

I present an innovative registration algorithm using the particle flow filter. The particle flow filter is a Bayesian filter that uses particles to represent probability densities. The particle flow filter is not constrained to the highly restrictive unimodal, linear, and Gaussian assumptions of many Bayesian filters such as the Kalman filter. Additionally, the particle flow filter is computationally more efficient than other multimodal filters such as the better-known particle filter (PF). Unlike the PF, the particle flow filter does not require particle resampling or importance weight updates. Rather, the proposal density is formed by flowing the prior probability to the posterior using the Fokker–Planck equation. The particle flow filter algorithms were implemented using MATLAB. Both 2D and 3D rigid body point-set registration were conducted using the Gromov particle flow filter variant. Additionally, the PF method and iterative closest point (ICP) algorithms were implemented for comparison. For the same alignment accuracy, the new particle flow filter approach was 244% faster than the PF for certain challenging problems. For the same alignment time, the particle flow filter reduced misalignment by as much as 35% over that of the PF. The particle flow filter achieved 100% alignment with enough particles, and reduced misalignment by as much as 75% over that of ICP. These results demonstrate that image registration via the particle flow filter significantly outperforms the PF and ICP algorithms in the presence of noise and for a high degree of initial misalignment.

Indoor localization schemes have significant potential for use in location-based services in areas such as smart factories, mixed reality, and indoor navigation. In particular, received signal strength (RSS)-based fingerprinting is used widely, given its simplicity and low hardware requirements. However, most studies tend to focus on estimating the 2D position of the target. Moreover, it is known that the fingerprinting scheme is computationally costly, and its positioning accuracy is readily affected by random fluctuations in the RSS values caused by fading and the multipath effect. We propose an indoor 3D localization scheme based on both fingerprinting and a 1D convolutional neural network (CNN). Instead of using the conventional fingerprint matching method, we transform the 3D positioning problem into a classification problem and use the 1D CNN model with the RSS time-series data from Bluetooth low-energy beacons for classification. By using the 1D CNN with the time-series data from multiple beacons, the inherent drawback of RSS-based fingerprinting, namely, its susceptibility to noise and randomness, is overcome, resulting in enhanced positioning accuracy. To evaluate the proposed scheme, we developed a 3D positioning system and performed comprehensive tests, whose results confirmed that the scheme significantly outperforms the conventional common spatial pattern classification algorithm.

Received signal strength (RSS) based location fingerprinting is a powerful wireless positioning technique. It estimates the target location by consulting a preliminary database and searching for the best matched RSS fingerprints. The construction and maintenance of a sufficient fingerprint database could be laborious and problematic. This paper proposes a new approach that utilizes the Kriging spatial interpolation algorithm to build complete fingerprint databases from sparsely collected measurements. The interpolation performance is analyzed over various extents of sparsity and number of measurements. The constructed fingerprint databases are utilized to locate a static target and the localization performances are analyzed. It is shown that the Kriging algorithm can be used to build RSS fingerprint databases of good accuracy based on sparsely collected measurements.

In recent years, Location-Based Services (LBS) have been widely applied in people's life with various localization technologies. Because outdoor localization methods are not suitable for indoor environments, various indoor localization methods have been developed. Among the existing indoor localization methods, Wi-Fi fingerprinting localization has attracted great concerns because of its wide applicability, simple deployment, and comparable performance. This paper proposed an interpolation method for radio map establishment based on RSS clustering and propagation model optimization. Fuzzy C-Means (FCM) clustering algorithm is used to cluster the Received Signal Strength (RSS) samples collected at Reference Points (RPs). In each cluster, propagation model parameters are optimized. Then RSS samples are estimated at select locations for radio map establishment. With the radio map after interpolation, more accurate localization results can be computed using K Nearest Neighbors (KNN) fingerprinting algorithm.

Particle filters are recursive Bayesian estimators, which are being applied to many areas of engineering in recent years to estimate states and parameters, regarding fire spread, tumors, oil pipelines, heat transfer, chemical reactors, etc. The key idea behind particle filters is that they use an initial distribution (sample), based on the previous state estimate, to calculate the best estimate for the current state, relying only on the current available measurements and the knowledge about the system. The greatest advantage of these methods is the easy computational implementation. However, setting the standard deviation for the initial distribution is very important for the success of the method. For this reason, standard formulation of these methods may not provide good results in problems with large discontinuities (or irregular/abrupt changes). For example, this would be the case of estimating step changes in the heat flux on a plate. Although several solutions have been proposed to improve the estimation performance, they still suffer from the curse of discontinuity. This occurs because particle filters proposed in the literature are not adaptive methods. In the example mentioned above, particle filters can have both a priori information and sample satisfactory before the change. However, after the change begins, the available information could be not enough to draw a suitable sample for the estimation. At this point, it is necessary to modify the standard deviation to broaden the particle search field or to move the a priori information to a new region where a new sample should be drawn. In this regard, the aim of this chapter is to propose a hybrid estimation scheme based on Particle Swarm Optimization (PSO) built into the particle filter Sampling Importance Resampling (SIR) to project the a priori information to a new search region, according to the current observation. To demonstrate the proposal, the problem of estimating step changes on the heat flux on a plate is taken into account, considering experimental measurements. The results allow to state that the scheme combining PSO and SIR provides good performance for this type of problem.