Fusion of RSS and Phase Shift Using the Kalman Filter for RFID Tracking

Summary form only given. Indoor tracking and localization is a crucial ingredient in many ubiquitous computing applications and robotics. In many applications there is the need to know the location of the objects. While in the near future everything will be tagged with radio frequency identification (RFID) tags, the localization of these tags in their environment is becoming an important feature for the RFID based ubiquitous computing applications. Due to its lower cost and its technical capabilities, RFID tagged object tracking can have a wide diverse variety of applications including scientific, military, and public safety. In this paper we propose a tracking algorithm for objects attached by UHF RFID tags by means of two RFID antennas and landmarks to reduce the localization cost and environment complexity. This algorithm uses RFID map made from passive or active references tags with known location (landmarks) to locate any unknown tag detected by the RFID reader antennas. It measures the distances between the readers and the common detected tags using the large scale path loss propagation model, and calculates the distance between the unknown tag and all the detected landmarks (inter-tags distance). With the multilateration technique the system is able to estimate the position of the unknown tag. The location estimation of the target will be independent to the reader's position. The research challenge corresponds to achieve an accurate indoor tracking system using two mobile RFID readers taking in consideration the limitation of RFID technology. This tracking algorithm is based on received signal strength (RSS) measurement to measure the reader-tags distance and target-landmarks distance to estimate the target location. To minimize the effect of the RSS and the process measurement noises on the position estimation, maximum likelihood estimator (MLE), map matching and Kalman filter are applied. Here, we investigate the use of Kalman filter to improve the precision and RFID map matching to improve the accuracy. Results obtained after simulations demonstrate the validity and suitability of the proposed algorithm to provide high performance level in terms of accuracy and scalability.

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.

Academicians and industry professionals have demonstrated the use of Radio Frequency Identification (RFID) Tags in construction applications in the past few years. Recently building information modelling (BIM) technology is emerging as the industry standard in the architecture, engineering and construction (AEC) sector. This paper combines the two technologies to monitor the movement of RFID tags in a BIM environment. Real-time monitoring of RFID tags in a building information model is an improvement over other methods of tracking construction workers, equipment and materials on the jobsite. Strategies for combining the two technologies are explored. This method of monitoring can be used to improve the safety and productivity of construction workers on a construction jobsite. This technique of monitoring RFID tags can also be used to track equipment and construction materials on a job-site. Autodesk Revit BIM software environment was used for tracking RFID tags. The paper includes discussion about the choice of RFID tags available for this method of tracking.

The RFID (Radio Frequency IDentification) system is paid attention to as a new identification source that achieves a ubiquitous environment. Each RFID tag has the unique ID, and is attached to some object. A user reads the unique ID of a RFID tag with RFID readers and obtains the information on the object. One of the most important technologies that use the RFID system is the position estimation of RFID tags. The position estimation means to estimates the location of the object with the RFID tag. It can be very useful to acquire the location information of RFID tag. If a user can understand the position of the RFID tag, the position estimation can be applied to a navigation system for walkers. In this paper, we propose a new method named as Swift Communication Range Recognition (S-CRR) method as an extended improvement of the previous CRR method on the estimation delay. In this method, the position of RFID tag is estimated by selecting the communication area model which corresponds to those boundary angles. We carry out the performance evaluation by the experiments of RFID system and show the effectiveness of S-CRR for position estimation.

Indoor localization based on Received Signal Strengths (RSS) or on some form of power measurements is a low-cost and low-complexity solution gaining more and more interest in the research and commercial worlds. Typically, Wireless Local Area Network (WLAN) signals are employed for such purpose, due to the fact that they are widely spread in indoor environments. Nevertheless, any wireless signal available in indoor scenarios can be used for positioning based on similar power measurement approaches. One example is the radio frequency identification (RFID), which enable portable localization systems in the form of wearable REID tags. Such RFID-enabled systems are highly demanded for health-state monitoring, object tracking, and security. These applications are mostly indoor applications, and thus, we can envision a near future where multiple RFID and WLAN signals will co-exist on multiple frequency bands. The existing signal diversity can offer a benefit in indoor positioning, providing that the signal propagation effects for both WLAN and RFID are well understood and taken into account. However, measurement-based studies on indoor channel modeling, including path loss and shadowing effects of RFID signals are still missing. A comparison between RFII) and WLAN channel models for positioning purpose has yet to be made. It is the purpose of our paper to address the path-loss channel models and shadowing effects for WLAN and RFID signals based on extensive measurement campaigns in an office environment.

The radiation pattern of radio frequency identification (RFID) antennas is influenced by various factors such as design, operating frequency, and polarization, which determine characteristics like directionality, omnidirectionality, beam width, and gain. Achieving precise readings over extended distances is crucial for the effectiveness of RFID systems, enabling faster item retrieval and delivery. The read distance, a critical aspect of RFID system performance, depends on factors like transmitted power, frequency, and antenna gain. Passive backscatter RFID setups particularly benefit from optimizing read distance for efficient operation. Fuzzy logic, as a soft computing technique, addresses uncertainties inherent in RFID systems effectively. This paper presents a novel approach to RFID antenna design, utilizing fuzzy logic to dynamically adjust frequency and power transmission. By enhancing field distribution, polarization, and received signal strength, this approach aims to optimize tag readings at extended distances, thereby improving overall system effectiveness. The methodology involves implementing algorithms in a C program to control the long-range distance aspects of the RFID system. Incorporating fuzzy rule algorithms into the RFID system's control logic enhances its ability to respond intelligently to changes in the operating environment, contributing to improved performance and reliability in long-range RFID applications.

A kind of intensive positioning approach, which is based on Radio Frequency Identification (RFID) technology, is proposed in this paper. In order to achieve precise positioning in the dense communication cabinet which possesses many jacks, the fingerprint positioning method is applied in this study. At the offline stage, the Received Signal Strength Indication (RSSI) values of a certain RFID label collected by the four RFID antennas in the communication cabinet are used to compose a feature vector, and the feature vectors of all the labels are used to establish the fingerprint database. At the online stage, a matching algorithm is utilized to match the measured RSSI value vector of the jack to be positioned with the database and then perform positioning task. The ideology of matching method is to compare and find the minimal Euclidean distance with those fingerprints in the fingerprint database. The theoretical analysis and experimental results show that the positioning accuracy and reliability can be guaranteed, which ensures the applicability of the proposed approach in the practical applications.

Many indoor fingerprinting localization methods are based on signal-domain distances with large localization error and low stability. An improved fingerprinting localization method using a clustering algorithm and dynamic compensation was proposed. In the offline stage, the fingerprint database was built and clustered based on offline hybrid distance and an affinity propagation clustering algorithm. Furthermore, clusters were adjusted using transition regions and a given radius, as well as updating the corresponding position and fingerprint of the cluster centroid. In the online stage, the lost received signal strength (RSS) in the reference fingerprint would be dynamically compensated by using a minimum RSS value, rather than a fixed one. Online signal-domain distance was calculated for cluster identification based on RSS readings and compensated reference fingerprint. Then, K reference points with minimum online signal-domain distances were selected, and affinity propagation clustering was reused by position-domain distances to choose the position-concentrated sub-cluster for location estimation. Experimental results show that the proposed method outperforms state-of-the-art fingerprinting methods, with the mean error of 2.328 m, the root mean square error of 1.865 m and the maximum error of 10.722 m in a testbed of 3200 square meters. The improvement rates, in terms of accuracy and stability, are more than 21% and 13%, respectively.

Due to the uncertainty of the Radio Frequency Identification (RFID) measurements and limit of the placement of the readers, it's necessary to use the estimation method to obtain more accurate trajectory in RFID indoor tracking system. The traditional recursive estimation from K to K+1 sampling point may fail, because the measurement of RFID system is irregular sampling due to the data-driven measurement mechanism. This paper develops the tracking method for indoor RFID system, including estimation dynamic model based on the estimated states and nonlinear fusion estimation algorithm for variable-irregular sampling measurements. Two estimation methods are given based on the Extended Kalman filter (EKF) and Unscented Kalman filter (UKF), respectively. The tracking performances are compared and the simulation results show that the performance of UKF can get better performance for indoor RFID tracking, especially in the low detection rate area.

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.

Recently, the radio frequency identification (RFID) system is paid attention as an identification source that can realize a ubiquitous environment. One of the important technologies that apply the RFID system is the indoor position estimation of RFID tags. For example, it can be applied to an indoor navigation system for handicapped person and recognition of ambient environment by a mobile robot, etc. In this paper, we propose a new positional estimation method to estimate the RFID tag position by using the tune of the antenna directivity of the RFID reader (D-CRR). This method controls the directivity of the antenna by dynamically changing the distance between the antenna elements of the RFID reader. We show the effectiveness of the proposed method by computer simulations.

Indoor localization has been actively researched in recent years due to the increasing demand for location-awareness services. However, to balance localization accuracy and system cost is always a challenge for indoor localization systems. Radio frequency identification (RFID) is a promising technology to achieve both goals, because of its reasonable cost and reliability. In this paper, we propose a novel RFID indoor localization system based on angle of arrival (AoA) and phase of arrival (PoA) methods. This system leverages RFID's two experimental signal diffusion characteristics to estimate AoA. One is that the interrogation zone is constrained in a lobe, and only in this area the tag can be queried. The second is that there exits a stable pattern of received signal strength (RSS) on angle changes. We use the two features to find a general area and to pinpoint the AoA consecutively. This effectively narrows the sampling zone (where signal needs to be sampled), and helps to reduce computational complexity. In addition, we reduce the multipath effect on range estimation by determining the AoA and rotating the reader into the direction of the target. Moreover, we exploit two signals with a slightly different frequency to eliminate the phase ambiguity issue. Our system takes only one reader and achieves mean accuracy of 23 cm. The simplicity and effectiveness of our system make it convenient to be used in practice.

This work presents the combinational object localization method for radio frequency identification (RFID) systems. The method allows to combine object position estimates formed by several base localization algorithms that use measurements of different types obtained on different interrogation signal power levels. Method efficiency has been evaluated for the case when three base algorithms - namely, the intersectional algorithm, trilateration, and the k nearest neighbors algorithm are used to process proximity, received signal strength (RSS), and read rate (RR) measurements. The experiment held in the indoor facility with the aid of the passive RFID system has shown that the proposed method achieves better localization accuracy than any single base algorithm based on measurements of any single type.

With the WiFi becoming more and more popular over the last few years, the study on the technology of indoor positioning based on WiFi is given more and more attention. Being specific to the problem that not all the clustered reference points(RPs) of the traditional clustering algorithm have geometric proximity in indoor positioning, this paper puts forward the affinity propagation presentation(AP) which bases on the combination of geometry and received signal strength (RSS). Being different from the traditional clustering algorithm, this kind of algorithm can endow the RPs with geometrical and endow the RSS with corresponding weight according to the positioning conditions, it can use the features of the geometry and RSS to restrain the sort of RPs, and that can make the sort of RPs more reasonable. After that it will use k-Nearest Neighbor Algorithm(KNN) to have accurate positioning, which not only can reduce the calculation, but also improve the localization accuracy. Simulation shows that, this kind of algorithm possesses the advantage of fast location and higher localization accuracy.

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.