Advanced Pedestrian Positioning System to Smartphones and Smartwatches

In recent years, there has been an increasing interest in the development of pedestrian navigation systems for satellite-denied scenarios. The popularization of smartphones and smartwatches is an interesting opportunity for reducing the infrastructure cost of the positioning systems. Nowadays, smartphones include inertial sensors that can be used in pedestrian dead-reckoning (PDR) algorithms for the estimation of the user's position. Both smartphones and smartwatches include WiFi capabilities allowing the computation of the received signal strength (RSS). We develop a new method for the combination of RSS measurements from two different receivers using a Gaussian mixture model. We also analyze the implication of using a WiFi network designed for communication purposes in an indoor positioning system when the designer cannot control the network configuration. In this work, we design a hybrid positioning system that combines inertial measurements, from low-cost inertial sensors embedded in a smartphone, with RSS measurements through an extended Kalman filter. The system has been validated in a real scenario, and results show that our system improves the positioning accuracy of the PDR system thanks to the use of two WiFi receivers. The designed system obtains an accuracy up to 1.4 m in a scenario of 6000 m2

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.     

CIR Parametric Rules Precocity For Ranging Error Mitigation In IR-UWB

The cutting-edge technology to support high ranging accuracy within the indoor environment is Impulse Radio Ultra Wide Band (IR-UWB) standard. Besides accuracy, IR-UWB’s low-complex architecture and low power consumption align well with mobile devices. A prime challenge in indoor IR-UWB based localization is to achieve a position accuracy under non-line-of-sight (NLOS) and multipath propagation (MPP) conditions. Another challenge is to achieve acceptable accuracy in the conditions mentioned above without any significant increase in latency and computational burden. This dissertation proposes a solution for addressing the accuracy and reliability problem of indoor localization system satisfying acceptable delay or computational complexity overhead. The proposed methodology is based on rules for identification of line-of-sight (LOS) and NLOS and the range error bias estimation and correction due to NLOS and MPP conditions. The proposed methodology provides accuracy for two major application domains, namely, wireless sensor networks (WSNs) and indoor tracking and navigation (ITN). This dissertation offers two different solutions for the localization problem. The first solution is a rules-based classification of LOS / NLOS and geometric-based range correction for WSN. In the first solution, the Boolean logic based classification is designed for identification of LOS/NLOS. The logic is based on channel impulse response (CIR) parameters. The second solution is based on fuzzy logic. The fuzzy based solution is appealing well for the stringent precision requirements in ITN. In this solution, the parametric Boolean logic from the first solution is converted and expanded into rules. These rules are implemented into a fuzzy logic based mechanism for designing a fuzzy inference system. The system estimates the ranging errors and correcting unmitigated ranges. The expanded rules and designed methodology are based on theoretical analysis and empirical observations of the parameters. The rules accommodate the parameters uncertainties for estimating the ranging error through the relationship between the input parameters uncertainties and ranging error using fuzzy inference mechanism. The proposed solutions are evaluated using real-world measurements in different indoor environments. The performance of the proposed solutions is also evaluated in terms of true classification rate, residual ranging errors’ cumulative distributions and probability density distributions, as well as outage probabilities. Evaluation results show that the true classification rate is more than 95%. Moreover, using the proposed fuzzy logic based solution, the residual errors convergence of 90% is attained for error threshold of 10 cm, and the reliability of the localization system is also more than 90% for error threshold of 15 cm

INS/WSN-Integrated Navigation Utilizing LS-SVM and H

In order to achieve continuous navigation capability in areas such as tunnels, urban canyons, and indoors a new approach using least squares support vector machine (LS-SVM) and H∞ filter (HF) for integration of INS/WSN is proposed. In the integrated system, HF estimates the errors of position and velocity while the signals in WSNs are available. Meanwhile, the compensation model is trained by LS-SVM with corresponding HF states. Once outages of the signals in WSNs, the model is used to correct INS solution as HF does. Moreover, due to device reasons, there are slight fluctuations in sampling period in practice. For overcoming this problem of integrated navigation, the theoretical analysis and implementation of HF for an integrated navigation system with stochastic uncertainty are also given. Simulation shows the performance of HF is more robust compared with INS-only solution and Kalman filter (KF) solution, and the prediction of LS-SVM has the smallest error compared with INS-only and back propagation (BP), the improvement is particularly obvious

Fuzzy Mobile-Robot Positioning in Intelligent Spaces Using Wireless Sensor Networks

This work presents the development and experimental evaluation of a method based on fuzzy logic to locate mobile robots in an Intelligent Space using Wireless Sensor Networks (WSNs). The problem consists of locating a mobile node using only inter-node range measurements, which are estimated by radio frequency signal strength attenuation. The sensor model of these measurements is very noisy and unreliable. The proposed method makes use of fuzzy logic for modeling and dealing with such uncertain information. Besides, the proposed approach is compared with a probabilistic technique showing that the fuzzy approach is able to handle highly uncertain situations that are difficult to manage by well-known localization methods

Practical implementation of a hybrid indoor localization system

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIndoor localization systems occupy a significant role to track objects during their life cycle, e.g., related to retail, logistics and mobile robotics. These positioning systems use several techniques and technologies to estimate the position of each object, and face several requirements such as position accuracy, security, coverage range, energy consumption and cost. This master thesis describes a real-world scenario implementation, based on Bluetooth Low Energy (BLE) beacons, evaluating a Hybrid Indoor Positioning System (H-IPS) that combines two RSSI-based approaches: Multilateration (MLT) and Fingerprinting (FP). The objective is to track a target node, assuming that the object follows a linear motion model. It was employed Kalman Filter (KF) to decrease the positioning errors of the MLT and FP techniques. Furthermore a Track-to-Track Fusion (TTF) is performed on the two KF outputs in order to maximize the performance. The results show that the accuracy of H-IPS overcomes the standalone FP in 21%, while the original MLT is outperformed in 52%. Finally, the proposed solution demonstrated a probability of error < 2 m of 80%, while the same probability for the FP and MLT are 56% and 20%, respectively.Os sistemas de localização de ambientes internos desempenham um papel importante na localização de objectos durante o seu ciclo de vida, como por exemplo os relacionados com o varejo, a logística e a robótica móvel. Estes sistemas de localização utilizam várias técnicas e tecnologias para estimar a posição de cada objecto, e possuem alguns critérios tais como precisão, segurança, alcance, consumo de energia e custo. Esta dissertação de mestrado descreve uma implementação num cenário real, baseada em Bluetooth Low Energy (BLE) beacons, avaliando um Sistema Híbrido de Posicionamento para Ambientes Internos (H-IPS, do inglês Hybrid Indoor Positioning System) que combina duas abordagens baseadas no Indicador de Intensidade do Sinal Recebido (RSSI, do inglês Received Signal Strength Indicator): Multilateração (MLT) e Fingerprinting (FP). O objectivo é localizar um nó alvo, assumindo que o objecto segue um modelo de movimento linear. Foi utilizado Filtro de Kalman (FK) para diminuir os erros de posicionamento do MLT e FP, além de aplicar uma fusão de vetores de estado nas duas saídas FK, a fim de maximizar o desempenho. Os resultados mostram que a precisão do H-IPS supera o FP original em 21%, enquanto que o MLT original tem um desempenho superior a 52%. Finalmente, a solução proposta apresentou uma probabilidade de erro de < 2 m de 80%, enquanto a mesma probabilidade para FP e MLT foi de 56% e 20%, respectivamente

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