Theoretical Model for RSS Localization

Determining the direction-of-arrival (DOA) of any signal of interest has long been of great interest to the wireless localization research community for military and civilian applications. To efficiently facilitate the deployment of DOA systems, the accuracy of wireless localization is critical. Hence, this paper proposes a novel method to improve the prediction result of a wireless DOA localization system. By considering the signal variation existing in the complex environment, the actual location of the target can be determined including the maximum prediction error. Moreover, the scenario of the moving target is further investigated by incorporating the adaptive Kalman Filter algorithm to obtain the prediction route of the flying drone based on the accuracy assessment method. This proposed adaptive Kalman Filter is a high-efficiency algorithm that can filter out the noise in the multipath area and optimize the predicted data in real-time. The simulation result agrees well with the measured data and thus validates the proposed DOA system with the adaptive Kalman Filter algorithm. The measured DOA of the fixed radiation source obtained by a single base station and the moving route of a flying drone from a two-base station localization system are presented and compared with the calculated results. Results show that the prediction error in an outdoor region of is about 10–20 m, which demonstrate the usefulness of the proposed wireless DOA system deployment in practical applications.

Location accuracy is one of the key indicators in measuring the performance of wireless sensor localization systems. Apparently, the localization performance is largely determined by ranging precision in the distance-based localization system. In the RSSI-based distance estimation techniques, the accuracy of their estimation is vulnerable to the change of RSSI measured values. As is known, the Received Signal Strength Indicator (RSSI) is environment dependent in certain extent. In addition, the received signal strength mainly depends on the emitting power of the sender. In this paper, the RSSI measured values are investigated when the sending power gets changed due to low supply voltage of the sender, and then a calibrated method is proposed. Experiments show that the proposed method can significantly mitigate distance estimation errors caused by low supply voltage, thus it can be widely used in battery powered wireless sensor localization systems.

It has always been important to know where we are in the world. In the past we used maps and compasses to find our way but today we are using GPS devices when travelling by foot, car or bike. The developments in location-based techniques and the growing usefulness of ubiquitous computing and context-dependent information have resulted in an increase business interest in location-based applications and services. Currently, there are many applications requiring precise location or real-time tracking of humans, animals or other things inside buildings. The current global positioning system is unreliable for interior spaces. Therefore, demand for indoor localization services is becoming a key requirement.This paper makes an analysis of recent developments in wireless indoor localization methods and systems, to have a better understanding of the state of the art and to inspire new scientific initiatives in this promising area.In order to achieve the purpose of the paper, the existing wireless localization systems and location methods are reviewed, and also comparing the related techniques and systems with future trends.

In this paper, the novel principle of the switched injection-locked oscillator is introduced. It is shown that the concept is ideally suited for transponder and secondary radar systems with outstanding performance. A switched injection-locked oscillator transponder can produce an approximately phase coherent high-power response to an interrogating signal and, consequently, allows for long-range transponder systems and precise distance measurement between the reader and transponder. The simple and elegant round-trip time-of-flight measurement concept introduced in this paper enables innovative localization and navigation systems. Furthermore, it can be applied in numerous areas such as sensor networks, RF identification and localization, ubiquitous computing, location sensitive billing, context dependent information services, or tracking and guiding.

Wireless indoor localization has attracted extensive research recently due to its potential for large-scale deployment. However, the performances of different systems vary and it is difficult to compare these systems systematically in different indoor scenarios. In this work, we propose \(E^3\), a Gaussian process based error estimation approach for fingerprint-based wireless indoor localization systems. With an efficient error estimation algorithm, \(E^3\) is able to efficiently estimate the localization errors of the localization systems without requiring the expensive site evaluations. Our evaluation results show that the proposed approach efficiently estimates the performance of fingerprint-based indoor localization systems and can be used as an efficient tool to tune system parameters.

A complete system for dynamic structural identification test under ambient loads is presented. The main objective of these improvements is to achieve significant cost reductions. In this way the system can be suitable for application to a large number of structures within the framework of a comprehensive optimal maintenance program of structures. To achieve this cost reduction of the tests, the following improvements have been introduced: (a) a complete wireless system to acquire sensor data, (b) a wireless localization system that permits referring sensors to a local coordinate system. This localization system has been the object of recent patent, and ( c) finally, further developments of software aimed to diminishing human intervention in the structural identification process. In case of standard bridge monitoring the application of above improvements can achieve the generation of an automatic identification report. This goal is to be attained eventually, and to which the described developments contribute. Therefore these improvements can reduce engineer's intervention mainly to propose general guidelines for a suitable sensors placement.

Bluetooth Low Energy (BLE) based Wireless Indoor Localization System (WILS) with high localization accuracy and high localization precision is a key requirement in enabling the Internet of Things (IoT) in today’s applications. In this paper, we investigated the effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels. This issue is not often discussed as most of the works on localization algorithms use the highest power levels but has important practical implications for energy efficiency, e.g., if a designer would like to trade-off localization performance and node lifetime. To analyze the impact, we used the established trilateration based localization model with two methods i.e., Centroid Approximation (CA) and Minimum Mean Square Error (MMSE). We observed that trilateration based localization with MMSE method outperforms the CA method. We further investigated the use of two filters i.e., Low Pass Filter (LPF) and Kalman Filter (KF) and evaluated their effects in terms of mitigating the random variations from BLE signal. In comparison to non-filter based approach, we observed a great improvement in localization accuracy and localization precision with a filter-based approach. Furthermore, in comparison to LPF based trilateration localization with CA, the performance of a KF based trilateration localization with MMSE is far better. An average of 1 m improvement in localization accuracy and approximately 50% improvement in localization precision is observed by using KF in trilateration based localization model with the MMSE method. In conclusion, with KF in trilateration based localization model with MMSE method effectively eliminates random variations in BLE RSS with multiple transmission power levels and thus results in a BLE based WILS with high accuracy and high precision.

Wireless localization technologies have undergone a paradigm shift with the advent of the emerging trend which is the Reconfigurable Intelligent Surfaces (RISs). This paper presents a comprehensive survey of the state-of-the-art RIS-aided localization techniques, exploring the transformative impact of intelligent surfaces on location-based services. The survey comprehensively reviews existing wireless localization methods, ranging from the localization techniques in the wireless communications generations. It addition the challenges posed by conventional localization methods are discussed, including accuracy and coverage issues. In response to these challenges, the incorporation of RIS is explored as a promising paradigm to enhance the precision and reliability of wireless localization. The core of the paper focuses on the integration of RIS into localization systems, highlighting how these RISs can be strategically deployed to enhance accuracy, mitigate multipath effects, integration in different propagation environment and solve non feasible localization problems. The survey encompasses a wide range of applications, including outdoor positioning, indoor positioning, and Internet of Things (IoT) devices, showcasing the versatility of RIS-aided localization across various scenarios. Also, critical design aspects are examined, including propagation regions, hardware requirements, deployment strategies, necessary measurements, multiplexing, and signalling systems.

In this paper, we propose an effective time-of-arrival (TOA)-based localization method with adaptive bias computation in indoor environments. The goal of the localization is to estimate an accurate target's location in wireless localization system. However, in indoor environments, non-line-of-sight (NLOS) errors block the signal propagation between target device and base station. The NLOS errors have significant effects on ranging between two devices for wireless localization. In TOA-based localization, finding the target's location inside the overlapped area in the TOA-circles is difficult. We present an effective localization method using compensated distance with adaptive bias computation. The proposed method is possible for the target's location to estimate an accurate location in the overlapped area using the measured distances with subtracted adaptive bias. Through localization experiments in indoor environments, estimation error is reduced comparing to the conventional localization methods.

For improved localization around the indoor/outdoor transit area of buildings, this paper proposes an efficient algorithm combining the measurements from the WLAN (Wireless Local Area Network) and the GPS (Global Positioning System) for. The proposed hybrid localization algorithm considers both multipath errors and NLOS (Non-Line-of-Sight) errors, which occur in most wireless localization systems. To detect and isolate multipath errors occurring in GPS measurements, the propose algorithm utilizes conventional multipath test statistics. To convert WLAN signal strength measurements to range estimates in the presence of NLOS errors, a simple and effective calibration algorithm is designed to compute conversion parameters. By selecting and combining the reliable GPS and WLAN measurements, the proposed hybrid localization algorithm provides more accurate location estimates. An experiment result demonstrates the performance of the proposed algorithm.

Radiofrequency positioning problem within indoor space becomes significant with respect to the specific area for which the radiolocation is required. The area of interest may be an apartment, shop or storage area of any shape and size. The specificity of the space defines various requirements on the radiolocation system and the need for software that would facilitate the planning of such a system is obvious. Using well-known empirical and deterministic models for propagation of electromagnetic waves in enclosed spaces and precise analysis of specific closed areas, localization based on the RSS (Received Signal Strength) method should give good positioning results in any enclosed space. Software solution that was developed in this work allows you to determine the optimal position of the receiver on the walls of any space that is selected as an area in which implementation of a system of RSS indoor localization is required. The optimal position of the receiver provides precise and reliable localization within the indoor enclosure. The software can be used to optimize the indoor localization of people or objects, with the requirement that special radio frequency tags or radio frequency transmitters are mounted on them. The software supports the ability to import an arbitrary layout of indoor space over which optimization will be carried out from AutoCAD or a CAD tool.

It is hard to obtain a general error model for the range-based wireless indoor positioning system due to the complicated hybrid line-of-sight/non-line-of-sight (LOS/NLOS) environment. The performance of the conventional Gaussian-based nonlinear filters is degraded in the indoor scenario. In this paper, we employ a dynamic Gaussian model (DGM) to describe the indoor ranging error. A general Gaussian approximated model is constructed first to fit the potential distribution. The instantaneous LOS or NLOS error at a typical time is considered as the drift from this general distribution dynamically. The relationship between the instantaneous error of the DGM and the estimation accuracy of nonlinear filters is analyzed. Based on our analysis, we propose a measurement adaptation method to further reduce the error according to the DGM. Then, the nonlinear filters based on the Gaussian model, which are simple and accurate, can be applied. A biased extended Kalman filter (EKF) and an adaptive Gaussian particle filter (PF) integrated with the measurement adaptation method are designed. The real indoor experiment demonstrates that the estimation accuracy of our algorithms is greatly improved without imposing complexity and that our algorithms are suitable for the dynamic indoor environment.

A wireless electromagnetic localization method for tracking a medical micro-device in the gastrointestinal tract has been proposed and a prototype for the localization system has been developed. In the electromagnetic localization system, several excitation coils were mounted above the abdomen of human body to generate a varying electromagnetic field one by one. Then a wireless magnetic sensor was used to measure the magnetic field strength at the location of the micro-device. Thus the position and orientation of the micro-device could be obtained by solving the inverse problem of magnetic fields. In order to improve the localization precision, a global error correction technique based on the Hardy's Multi-Quadric scattered data interpolation method was investigated to correct the static errors. In the calibration experiment, dependencies between true locations of the micro-device and the corresponding tracked locations within the operational space were established. Based on these data sets, the Hardy's Multi-Quadric correction function was derived. The experimental results show that the localization error can be greatly reduced by using the Hardy's Multi-Quadric correction method. The advantage of the approach is that it allows for smooth continuous mapping from the tracked space to the true space within the entire tracked space.

Planning Systems Incorporated (PSI) has been working with the National Institute of Justice, Center for Society Law and Justice (CSLJ) at the University of New Orleans, and law enforcement agencies in five highly varied United States locations over the past three years to evaluate the use of an automated, wireless acoustic gun fire detection and localization system. Considerable progress has been made in improving the sensor design, system engineering, software applications, deployment practices and manufacturing capabilities since last year's presentation. Two military variants have been developed as well. This paper will provide an updated status of SECURES technology and its utilization.

Ultra wideband (UWB) technology has been adopted in many wireless communication and localization systems in indoor and outdoor areas. In this paper, based on the application in outdoor near-ground environments, the main path loss models are discussed according to the analysis of the propagation environment and received UWB signal. The path loss calculation model by combining the multi-waves with the threshold is proposed. With the outdoor experimental measurements and data processing, the path loss exponents are determined and the path loss characteristics are discussed.