Kalman/Map Filtering-Aided Fast Normalized Cross Correlation-Based Wi-Fi Fingerprinting Location Sensing

In this paper we propose a fast normalized cross correlation (NCC) algorithm for pattern matching based on combining adaptive multilevel partition with the winner update scheme. This winner update scheme is applied in conjunction with an upper bound for the cross correlation derived from Cauchy-Schwarz inequality. To apply the winner update scheme, we partition the summation of cross correlation into different levels with the partition order determined by the gradient energies of the partitioned regions in the template. Thus, this winner update scheme can be employed to skip the unnecessary calculation. Experimental results show the proposed algorithm is very efficient for image matching under different lighting conditions.

Normalized cross-correlation has been used extensively for many signal processing applications, but the traditional normalized correlation operation does not meet speed requirements for time-critical applications. In this paper, a new fast algorithm for the computation of the normalized cross-correlation (NCC) without using multiplications is presented. For a search window of size M and a template of size N the fast NCC requires only approximately 2N?(M?N+1) additions/subtractions without multiplications. Simulation results with 100,000 test signals show that the use of the fast NCC instead of the traditional approaches for the determination of the degree of similarity between a test signal and a reference signal (template) brings about a significant improvement in terms of false negative rate, identification rate and computational cost without a significant increase in false positive rate, especially when the signal-to-noise ratio (SNR) is higher than 3 dB.

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.

Biometric authentication for personal identification is very popular now a days. Human ear recognition system is a new technology in this field. The change of appearance with the expression was a major problem in face biometrics but in case of ear biometrics the shape and appearance is fixed. That is why it is advantageous to use it for personal identification. In this paper, we have proposed a new approach for an automated system for human ear identification. Our proposed method consists of three stages. In the first stage, preprocessing of ear image is done for its contrast enhancement and size normalization. In the second stage, features are extracted through Haar wavelets followed by ear identification using fast normalized cross correlation in the third stage. The proposed method is applied on USTB ear image database and IIT Delhi. Experimental results show that our proposed system achieves an average accuracy of 97.2% and 95.2% on these databases respectively.

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.

Received Signal Strength (RSS) localization is widely used due to its simplicity and availability in most mobile devices. The RSS channel model is defined by the propagation losses and the shadow fading. These parameters might vary over time because of changes in the environment. In this paper, the problem of tracking a mobile node by RSS measurements is addressed, while simultaneously estimating a two-slope RSS model. The methodology considers a Kalman filter with Interacting Multiple Model architecture, coupled to an on-line estimation of the observation's variance. The performance of the method is shown through numerical simulations in realistic scenarios.

Wi-Fi localization is currently the most promising approach to build indoor localization systems. Especially after the recent release of Google Indoor Map for the Android system smart phones, many companies such like Skyhook and TCS etc. have flourished into the business of developing accurate Wi-Fi based indoor localization techniques due to their numerous applications. In this paper, we proposed an accurate Wi-Fi based indoor localization algorithm with the help of Google Indoor Map. The initial position of the mobile station (MS) is estimated according to the received signal strength (RSS) from the calibrated Wi-Fi access points (APs). Then, the position of the MS is allocated by using the simulated annealing (SA) algorithm to search for a better solution. During the searching process, the SA takes the indoor map structure into consideration and updates the cost function weights accordingly. This procedure makes sure the final estimation reaches to a better convergence. Extensive experimental results confirm that our solution is able to provide a much better result compared with other existing indoor localization techniques.

In recent years, the knowledge of location is of prime importance for success of many location-based services (LBS) applications such as indoor navigation, emergency management, elderly tracking, etc. There are many popular approaches in the literature which utilize received signal strengths (RSSs) in wireless sensor network (WSN) to track the moving target. Many times for the applications wherein localization accuracy to the scale of few centimetres is required, the application of one localization approach is not sufficient. The important reason is the highly fluctuating nature of RSS’s, and dynamicity in target motion and surrounding environment. Although generalized regression neural network (GRNN) has very nice estimation ability, we believe that the addition of the concept centroid to the GRNN architecture and fusing it with kalman filter (KF) can further improve its target localization performance. This paper proposes two robust range-free RSS based target localization algorithms namely, CGRNN+KF, and CGRNN+UKF, obtained by the fusion of fundamentals of centroid, GRNN, and KF. From the results obtained, it is observed that the proposed CGRNN+KF, and CGRNN+UKF algorithms yield positioning accuracy of few centimetres and effectively deal with the highly fluctuating nature of RSS’s, and dynamicity in target motion and surrounding environment.

We present a novel hybrid localization algorithm for wireless sensor networks in the absence of knowledge regarding the transmit power and path-loss exponent. Transmit power and the path-loss exponent are critical parameters for target localization algorithms in wireless sensor networks, which help extract target position information from the received signal strength. In the absence of information on transmit power and path-loss exponent, it is critical to estimate them for reliable deployment of conventional target localization algorithms. In this paper, we propose a simultaneous estimation of transmit power and path-loss exponent based on Kalman filter. The unknown transmit power and path-loss exponent are estimated using a Kalman filter with the tentatively estimated target position based solely on angle information. Subsequently, the target position is refined using a hybrid method incorporating received signal strength measurements based on the estimated transmit power and path-loss exponent. Our proposed algorithm accurately estimates transmit power and path-loss exponent and yields almost the same target position accuracy as the simulation results confirm, as the hybrid target localization algorithms with known transmit power and path-loss exponent. Simulation results confirm the proposed algorithm achieves 99.7% accuracy of the target localization performance with known transmit power and path-loss exponent, even in the presence of severe received signal strength measurement noise.

Localization is a key issue for wireless sensor network. The traditional localization methods achieve high location accuracy using received signal strength (RSS) in line of sight (LOS) environment. But the localization accuracy degrades significantly in nonline of sight (NLOS) environment. So the localization in NLOS environment is one of the most challenging for wireless sensor network. In this paper, we propose an RSS based localization algorithm in NLOS environment. A NLOS identification algorithm is firstly proposed. This algorithm does not need the parameters of the NLOS in prior. Then we correct the NLOS measurements by subtracting the mean of NLOS errors. Finally, the Kalman filter is employed to mitigate the process noise. In simulations, we demonstrate that the proposed algorithm achieves higher localization accuracy compared with other methods.

In this paper, a new framework is presented for indoor visible light communication (VLC) system, based on Yolo v3, EfficientNetB3, and DenseNet121 deep learning (DL) models, as well as an optimization strategy. The proposed framework consists of two steps: data collecting and DL model training. To start, data is acquired using MATLAB and Kalman Filtering (KF) with averaging approaches. Second, the received signal strength (RSS) is employed as the DL models input, with the Cartesian coordinates as the DL models output. The averaging RSS approach combined with KF algorithm are used in the suggested framework. This work introduces the impacts of Non-Line-of-Sight (NLoS) for initial reflection and Line-of-Sight (LoS) based on the three mentioned DL models. Furthermore, we used Bayesian optimization and automatic hyper-parameter (HP) optimization to increase system efficiency and to reduce positioning error in DL models. The obtained results show that the models outperform existing the HP-RSS-KF-LoS-DL models in terms of localization error when compared to traditional RSS signal-based localization techniques. Many performance indicators are considered to evaluate the proposed framework resiliency, including accuracy (ACC), area under the curve (AUC), sensitivity (Se), and precision (Pr), as well as F1-score, root mean square error (RMSE), training, and testing time. The DL models are generated and trained using Python software on a Kaggle Notebook GPU cloud (2 CPU cores and 13 GB RAM). The achieved results are: 99.99% ACC, 99.98% AUC, 98.88% Se, 98.98% Pr, 99.97% F1-score, 0.112 cm RMSE, and 0.29 s testing time. The proposed system could be easily deployed for autonomous applications, based on the analysis of the experimental data. Several applications can be used depending on enhancing the localization of VLC system in military systems, underwater systems, and indoor systems like hospitals, hotels, libraries and malls.

In this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the APF by decreasing its update frequency and the number of particles used in this research. In the lower filter (Kalman filter), zero velocity update and non-holonomic constraints are used to correct the error of the inertial navigation-derived solutions. The innovation of the design lies in the combination of upper filter (particle filter) map-matching and map-aiding methods to further constrain the navigation solutions. This proposed navigation method simplifies indoor positioning and makes it accessible to individual and group users, while guaranteeing the system’s accuracy. The availability and accuracy of the proposed algorithm are tested and validated through experiments in various practical scenarios.

Due to the significance of positioning in complicated electromagnetic environment of its variety of applications, wireless localization techniques have attracted significant research interests with the trends moving toward boosting the capabilities of multi-objective cooperative networks. This paper addresses the problem of locating multiple targets in 3-D cooperative wireless sensor network. A novel hybrid received signal strength (RSS) and time-of-arrival (TOA) positioning scheme is proposed. Employing RSS and TOA measurements, four valuable expressions are drawn. The TOA and RSS technologies are combined with path loss difference variable being introduced. Based on the weighted least squares criterion, the rough solutions of multiple targets positions are derived. Finally, the locations of obtained targets are optimized by cooperative wireless communication between the target and another target. The location accuracy of the proposed estimation algorithm is analyzed and compared with the conventional RSS and TOA estimation algorithms through simulation experiments. The simulation result demonstrates that the proposed hybrid RSS and TOA localization algorithm for multi-objective cooperative wireless networks outperforms traditional approaches.

Advances in miniaturized wireless and sensing technologies have enabled the construction of cheap, low-powered, portable wireless devices capable of forming ad hoc networks. While these networks have shown enormous potential in applications such as remote sensing and target tracking, these applications require the devices to determine their own location. Additionally, devices capable of self-localization can also be used to implement location-based services or to improve coordination between first-responders to disaster sites or infantry in tactical situations. Existing techniques such as GPS may not be available due to design or environmental constraints, so other methods need to be devised. Previous works have proposed methods for wireless devices to self-localize based on received signal strength (RSS), but these methods offer limited accuracy due to the large error in RSS measurements. Recognizing the trend for these portable wireless devices to contain acceleration sensors, we propose an algorithm to combine these acceleration measurements with RSS readings to achieve accurate localization. We apply a distributed extended Kalman filter to track position based on these two measurements and a kinematic node movement model. This algorithm is able to take advantage of correlations between successive location estimates to improve estimation accuracy. We calculate the posterior Cramer-Rao bound for this algorithm and analyze it through simulation. Our analysis shows that by utilizing the acceleration information, the network is able to self-localize despite the large inaccuracy in RSS readings.

Radio frequency identification (RFID)-based indoor tracking system has been used for the localization and tracking of forklift and automated guided vehicles in warehouses. Most of the existing tracking methods rely on either received signal strength (RSS) or phase shift alone. This paper presents a new RFID tracking method that fuses the RSS and phase shift together to predict the instant position of a mobile target. The fingerprints database is first constructed by collecting the RSS values at reference points. After data normalization, the fingerprints are fed into the extreme learning machine for the rough position estimation. The radial speed of the RFID tag can be calculated by measuring the phase difference between two consecutive time steps. Then, the linear least squares method is exploited to fit the instant speed of the mobile tag. On the assumption that the trajectory of the mobile tag could be approximated by a series of line segments, the Kalman filter is utilized afterward to enhance the position estimation by combing the rough location estimation and the instant velocity. The performance of our method is studied in simulations and experimentally verified in our lab. The results demonstrate that our method is feasible and achieves higher accuracy than traditional tracking methods.