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A wireless indoor positioning system using white LED lights is proposed. The time difference of arrival technique is employed and the phase differences between the received signals are determined to develop a positioning algorithm which can estimate the receiver location with a mean localisation error as low as 1 mm in a room of dimensions 5 × 5 × 3 m. Through simulations, it is identified that the optimum receiver height where localisation error gets minimised is between 2.5 and 3 m from the ceiling which corresponds well with the typical dimensions of a room.

In Underwater Wireless Sensor Networks, ensuring precise localization of sensor nodes is crucial. Existing localization algorithms heavily rely on pre-deployed and known anchor nodes to compute and determine the positions of other nodes. However, anchor nodes may be compromised or misled in practical scenarios, resulting in localization inaccuracies. To address this challenge, a proficient iterative localization algorithm is proposed in Garg and Varna (IEEE Trans Inf Forensics Secur 7: 717–730, 2012), which selects known nodes as reference anchors to locate unknown nodes and analyzes their performance under non-collaborative attacks. Therefore, a novel localization algorithm is introduced to counter collaborative attacks, utilizing time difference of arrival ranging mapping and selective minimum gradient principles from the AdaDelta Gradient Descent (AGD) algorithm to iteratively eliminate deceptive information transmitted by interfering nodes, thereby enhancing localization accuracy. Validations demonstrate its efficacy in combating collaborative network attacks under certain conditions and significantly improving localization performance. Simulation experiments confirm its robustness even under network attacks, ensuring the reliable operation of UWSNs.

A single Radio-Frequency Interference (RFI) is a disturbance source of modern wireless systems depending on Global Navigation Satellite Systems (GNSS) and Satellite Communication (SatCom). In particular, significant applications such as aeronautics and satellite communication can be severely affected by intentional and unintentional interference, which are unmitigated. The matter requires finding a radical and effective solution to overcome this problem. The methods used for overcoming the RFI include interference detection, interference classification, interference geolocation, tracking and interference mitigation. RFI source geolocation and tracking methodology gained universal attention from numerous researchers, specialists, and scientists. In the last decade, various conventional techniques and algorithms have been adopted in geolocation and target tracking in civil and military operations. Previous conventional techniques did not address the challenges and demand for novel algorithms. Hence there is a necessity for focussing on the issues associated with this. This survey introduces a review of various conventional geolocation techniques, current orientations, and state-of-the-art techniques and highlights some approaches and algorithms employed in wireless and satellite systems for geolocation and target tracking that may be extremely beneficial. In addition, a comparison between different conventional geolocation techniques has been revealed, and the comparisons between various approaches and algorithms of geolocation and target tracking have been addressed, including H∞ and Kalman Filtering versions that have been implemented and investigated by authors.

Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accuracy and low-power indoor positioning systems that can provide accuracy of less than 5 m and enable battery operation for mobility and long-term use. We propose and implement an intelligent, highly accurate and low-power indoor positioning system for smart homes leveraging Gaussian Process Regression (GPR) model using information-theoretic gain based on reduction in differential entropy. The system is based on Time Difference of Arrival (TDOA) and uses ultra-low-power radio transceivers working at 434 MHz. The system has been deployed and tested using indoor measurements for two-dimensional (2D) positioning. In addition, the proposed system provides dual functionality with the same wireless links used for receiving telemetry data, with configurable data rates of up to 600 Kbauds. The implemented system integrates the time difference pulses obtained from the differential circuitry to determine the radio frequency (RF) transmitter node positions. The implemented system provides a high positioning accuracy of 0.68 m and 1.08 m for outdoor and indoor localization, respectively, when using GPR machine learning models, and provides telemetry data reception of 250 Kbauds. The system enables low-power battery operation with consumption of <200 mW power with ultra-low-power CC1101 radio transceivers and additional circuits with a differential amplifier. The proposed system provides low-cost, low-power and high-accuracy indoor localization and is an essential element of public well-being in future smart homes.

During the launch mission of a launch vehicle (LV), localization of the LV is very important for ensuring flight safety. Usually, active localization systems such as the ground tracking radar systems and onboard navigation systems of the LV are responsible for acquiring the relevant location information for localization. However, if, in addition to the existing localization methods, passive localization using ground telemetry stations can be implemented, the reliability of the flight safety control for the LV can be improved. Time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA) are typical localization techniques used for an emitting target. In this paper, we examine passive TDOA/AOA localization that uses two ground telemetry stations, and propose a technique to improve its localization accuracy, based on second-order polynomial regression and the removal of the estimated multipath delays in the TDOA/AOA results. By comparing the results of our proposed technique with the measured data from an on-board GPS used in the 3rd Korea Space Launch Vehicle-1 (KSLV-1) mission, our study confirms that the localization accuracy has been improved.

Humans have always wanted to determine positions in an unknown environment. At the beginning, methods were simple. They were based on the observation of characteristic points—in the case of shipping, additional observations of the coastline. Then came navigation based on astronomical methods (astronavigation). At the beginning of the XX-th century, a new way of determining the current location was developed. It used radio wave signals. First came radio beacons, then ground-based systems. Currently, satellite systems are being used. At present, the most popular one is the Global Positioning System (GPS). This system is fully controlled by the Department of Defense, and only the U.S. forces and their closest allies have been guaranteed the accuracy offered by the system. Armies of other countries can only use the civilian version. This situation has engendered the need for an independent radiolocation system. This article describes the construction and operation of such a technology demonstrator that was developed at Gdansk University of Technology. The main advantage of the system is the ability to manage it without the chain organization of the reference stations, which currently work with each other asynchronously. This article demonstrates the functionality of such a system. It also presents results and analysis of its effectiveness.

A method is proposed for position estimation from non line of sight time difference of arrivals (TDOA) measurements. A general measurement model for TDOA accounting for non line of sight conditions is developed; then, several simplifying working assumptions regarding this model are discussed to allow the efficient implementation of a particle filter localization algorithm. This algorithm is tested and compared with an extended Kalman filter procedure, both in simulation, generating artificial measures, and with real data.

In localization estimation systems, it is well known that the sensor-emitter geometry can seriously impact the accuracy of the location estimate. In this paper, time-difference-of-arrival (TDOA) localization is applied to locate the emitter using unmanned aerial vehicle (UAV) swarms equipped with TDOA-based sensors. Different from existing studies where the variance of measurement noises is assumed to be independent and changeless, we consider a more realistic model where the variance is sensor-emitter distance-dependent. First, the measurements model and variance model based on signal-to-noise ratio (SNR) are considered. Then the Cramer–Rao low bound (CRLB) is calculated and the optimal configuration is analyzed via the distance rule and angle rule. The sensor management problem of optimizing UAVs trajectories is studied by generating a sequence of waypoints based on CRLB. Simulation results show that path optimization enhances the localization accuracy and stability.

Determining the position of the sensor node is the ultimate challenging one in wireless sensor networks (WSNs), which may perhaps lead a large localization error. To tackle this tricky, a novel Semi Definite Programming (SDP)—Multiplier method is proposed using time difference of arrival (TDOA) measurement in asynchronous networks. The SDP relaxation method is derived to prevaricate the optimal maximum likelihood (ML) convergence problem that will result in Lagrangian problem. Consequently, this problem can be optimally solved in a stress-free method which provides the value of multipliers. In this Relax and cut scheme, Lagrangian multiplier added in the objective function which gives the minimum value of the function that fulfills the relaxed constraints and reduce the execution time. Also to monitor the mobile target and to improve the energy of sensors, an optimization framework of selective approach algorithm has been used. The simulation result demonstrates that the proposed algorithm provides a better position estimation with less localization error and energy consumption.

The conventional global positioning system (GPS) can often fail to provide position determination for a mobile user in indoor and urban environments. To cope with GPS failure in such environments, a new navigation system which utilizes a terrestrial digital multimedia broadcasting (T-DMB) signal to obtain the mobile user's position is presented. Since the T-DMB transmitters in Korea construct a single frequency network (SFN), which forces the transmitters to be synchronized, the mobile user can measure a time difference of arrival (TDOA) for all audible T-DMB transmitter pairs. The time difference between T-DMB transmitters is converted to a distance difference by multiplying the time difference by the speed of light. Using these measurements and a TDOA positioning method, the mobile user position can be estimated. An experiment with a T-DMB receiver and a data acquisition (DAQ) board is performed in Seoul to analyze the error characteristic of TDOA measurements. It is certified that the measurement error is bounded under 300 m and can be used to determine the mobile user's position with a small standard deviation.