Indoor positioning and tracking based on the received signal strength

Received Signal Strength Indicator (RSSI)-based indoor Location and Tracking (L&T) is a promising and challenging technology that enables mobile users/nodes to obtain their location information. This dissertation focuses on overcoming the challenges as well as improving the positioning accuracy for the RSSI-based L&T. In particular, the author considers 4 L&T solutions. In the first, the author develops a L&T solution by designing the Kalman Filter (KF) to work linearly within the positioning framework. To elaborate on this implementation, the equations of the KF are presented in a consistent manner with the implementation. In the second, the author designs a L&T solution based on the Iterated Extended Kalman Filter (IEKF) to improve the accuracy compared with the popular Extended Kalman Filter (EKF). In the third, the author overcomes the particular implementation challenges of the EKF by designing a L&T solution based on the implementation of the Scaled Unscented Transformation (SUT) to the KF. The author calls the resulting filter Scaled Unscented Kalman Filter (SUKF). In the forth, the author overcomes the implementation difficulties of the EKF by designing a L&T solution based on the implementation of the Spherical Simplex Unscented Transformation (SSUT) to the KF. The author calls the resulting filter the Spherical Simplex Unscented Kalman Filter (SSUKF). The proposed solutions with their corresponding achievements enhance the role of RSSI-based L&T in wireless positioning systems. The contributions led to significant improvement in the positioning accuracy, reliability and the ease of implementation

Statistical analysis of indoor RSSI read-outs for 433 MHz, 868 MHz, 2.4 GHz and 5 GHz ISM bands

This paper presents statistical analysis of RSSI read-outs recorded in indoor environment. Many papers concerning indoor location, based on RSSI measurement, assume its normal probability density function (PDF). This is partially excused by relation to PDF of radio-receiver's noise and/or together with influence of AWGN (average white Gaussian noise) radio-channel – generally modelled by normal PDF. Unfortunately, commercial (usually unknown) methods of RSSI calculations, typically as "side-effect" function of receiver's AGC (automatic gain control), results in PDF being far different from Gaussian PDF. This paper presents results of RSSI measurements in selected ISM bands: 433/868 MHz and 2.4/5 GHz. The measurements have been recorded using low-cost integrated RF modules (at 433/868 MHz and 2.4 GHz) and 802.11 WLAN access points (at 2.4/5 GHz). Then estimated PDF of collected data is shown and compared to normal (Gaussian) PDF

Comparative analysis of digital filters for received signal strength indicator

Increasing demand in Internet of Things applications has drawn researchers to explore deeper into alternative methods that provide efficiency in terms of application, energy, cost and etc. One of the techniques proposed by the current trend is the use of Received Signal Strength Indicator value for different Internet of Things applications. It is imperative to investigate the digital signal filter for the Received Signal Strength Indicator readings to interpret it into a more reliable data. A comparative analysis of three different types of digital filters is discussed in this paper which are Simple Moving Average filter, Alpha Trimmed Mean filter and Kalman filter. There are three criteria used to observe the performance of the digital filters which are noise reduction, data proximity and delays. Based on the criteria, the choice of digital signal processing filter can be determined in accordance with its implementations. Hence, this paper portrays the possibilities of Received Signal Strength Indicator in different Internet of Things applications given a proper choice of digital signal processing filter

Sulautettu ohjelmistototeutus reaaliaikaiseen paikannusjärjestelmään

Asset tracking often necessitates wireless, radio-frequency identification (RFID). In practice, situations often arise where plain inventory operations are not sufficient, and methods to estimate movement trajectory are needed for making reliable observations, classification and report generation. In this thesis, an embedded software application for an industrial, resource-constrained off-the-shelf RFID reader device in the UHF frequency range is designed and implemented. The software is used to configure the reader and its air-interface operations, accumulate read reports and generate events to be reported over network connections. Integrating location estimation methods to the application facilitates the possibility to make deploying middleware RFID solutions more streamlined and robust while reducing network bandwidth requirements. The result of this thesis is a functional embedded software application running on top of an embedded Linux distribution on an ARM processor. The reader software is used commercially in industrial and logistics applications. Non-linear state estimation features are applied, and their performance is evaluated in empirical experiments.Tavaroiden seuranta edellyttää usein langatonta radiotaajuustunnistustekniikkaa (RFID). Käytännön sovelluksissa tulee monesti tilanteita joissa pelkkä inventointi ei riitä, vaan tarvitaan menetelmiä liikeradan estimointiin luotettavien havaintojen ja luokittelun tekemiseksi sekä raporttien generoimiseksi. Tässä työssä on suunniteltu ja toteutettu sulautettu ohjelmistosovellus teolliseen, resursseiltaan rajoitettuun ja kaupallisesti saatavaan UHF-taajuusalueen RFID-lukijalaitteeseen. Ohjelmistoa käytetään lukijalaitteen ja sen ilmarajapinnan toimintojen konfigurointiin, lukutapahtumien keräämiseen ja raporttien lähettämiseen verkkoyhteyksiä pitkin. Paikkatiedon estimointimenetelmien integroiminen ohjelmistoon mahdollistaa välitason RFID-sovellusten toteuttamisen aiempaa suoraviivaisemin ja luotettavammin, vähentäen samalla vaatimuksia tietoverkon kaistanleveydelle. Työn tuloksena on toimiva sulautettu ohjelmistosovellus, jota ajetaan sulautetussa Linux-käyttöjärjestelmässä ARM-arkkitehtuurilla. Lukijaohjelmistoa käytetään kaupallisesti teollisuuden ja logistiikan sovelluskohteissa. Epälineaarisia estimointiominaisuuksia hyödynnetään, ja niiden toimivuutta arvioidaan empiirisin kokein

Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System

Tracking a mobile node using wireless sensor network (WSN) under cooperative system among anchor node and mobile node, has been discussed in this work, interested to the indoor positioning applications. Developing an indoor location tracking system based on received signal strength indicator (RSSI) of WSN is considered cost effective and the simplest method. The suitable technique for estimating position out of RSSI measurements is the extended Kalman filter (EKF) which is especially used for non linear data as RSSI. In order to reduce the estimated errors from EKF algorithm, this work adopted forward data processing of the EKF algorithm to improve the accuracy of the filtering output, its called iterated extended Kalman filter (IEKF). However, using IEKF algorithm should know the stopping criterion value that is influenced to the maximum number iterations of this system. The number of iterations performed will be affected to the computation time although it can improve the estimation position. In this paper, we propose modified IEKF for mobile cooperative tracking system within only 4 iterations number. The ilustrated results using RSSI measurements and simulation in MATLAB show that our propose method have capability to reduce error estimation percentage up to 19.3% , with MSE (mean square error) 0.88 m compared with conventional IEKF algorithm with MSE 1.09 m. The time computation perfomance of our propose method achived in 3.55 seconds which is better than adding more iteration process.     

Accurate Range-based Indoor Localization Using PSO-Kalman Filter Fusion

Accurate indoor localization often depends on infrastructure support for distance estimation in range-based techniques. One can also trade off accuracy to reduce infrastructure investment by using relative positions of other nodes, as in range-free localization. Even for range-based methods where accurate Ultra-WideBand (UWB) signals are used, non line-of-sight (NLOS) conditions pose significant difficulty in accurate indoor localization. Existing solutions rely on additional measurements from sensors and typically correct the noise using a Kalman filter (KF). Solutions can also be customized to specific environments through extensive profiling. In this work, a range-based indoor localization algorithm called PSO - Kalman Filter Fusion (PKFF) is proposed that minimizes the effects of NLOS on localization error without using additional sensors or profiling. Location estimates from a windowed Particle Swarm Optimization (PSO) and a dynamically adjusted KF are fused based on a weighted variance factor. PKFF achieved a 40% lower 90-percentile root-mean-square localization error (RMSE) over the standard least squares trilateration algorithm at 61 cm compared to 102 cm

A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives

Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored

A performance analysis of distributed filtering algorithms for indoor pedestrian tracking

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2017.A literatura recente indica que algoritmos de filtragem distribuída podem ter várias vantagens em relação a algoritmos centralizados, especialmente no que se refere a robustez, escalabilidade, uso de recursos de comunicação e flexibilidade. Neste contexto, este trabalho objetiva fornecer uma análise da performance de algoritmos de filtragem distribuída aplicados à estimação da posição de pesdestres em ambientes fechados. Particularmente, duas formas de algoritmos distribuídos serão testados: consenso por média ponderada e difusão. Um esquema de localização baseado em RFID é utilizado como teste para avaliar a performance dos algoritmos. Em especial, técnicas baseadas na diferença da fase de chegada são utilizadas para fornecer medições das distâncias e ângulos relativos entre as antenas e a etiqueta RFID. Simulações realizadas no software MATLAB indicam que algoritmos de difusão tem performance superior do que algoritmos de consenso na aplicação tratada neste trabalho. Além disso, a pequena diferença entre os erros quadráticos médios dos algoritmos centralizados e de difusão inspiram a utilização do último por seus outros benefícios.Recent literature suggests that distributed filtering algorithms may have several advantages over centralized ones, especially concerning robustness, scalability, use of communication resources and flexibility. In this context, this work aims to provide a performance analysis of distributed filtering algorithms for indoor pedestrian tracking, where a set of sensors are employed to estimate the position of a person in a room. More specifically, two forms of distributed algorithms will be investigated: weighted average consensus and diffusion. A localization scheme based on RFID is used as a testbed for the assessment of the performance of the algorithms. Particularly, phase difference of arrival techniques are applied to provide measurements of the relative distance and angle from the antennas to the RFID tag. Simulation experiments carried out in MATLAB indicate that the diffusion algorithm has superior performance than the weighted average consensus algorithm. Furthermore, the small difference in the root-mean-square error of centralized and diffusion algorithms inspires the use of the latter for its other benefits

RSS-based wireless LAN indoor localization and tracking using deep architectures

Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of 1.73 m, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: 10.35 m) and Nonparametric Information (NI) filter with variable acceleration (RMSE: 5.2 m) under the same experiment environment.ECSEL Joint Undertaking ; European Union's H2020 Framework Programme (H2020/2014-2020) Grant ; National Authority TUBITA