Modelling and Analysis of Emitter Geolocation using Satellite Tool Kit

This paper considers geolocation of a stationary radio frequency emitter which is being steered by multiple antennas installed on a geostationary satellite using received signal strength metric. The difference in the signal strengths is measured by the antennas and subsequently plotted as lines of position on the surface of the earth. Intersection of these two or more lines of position indicates the location of the terrestrial radio frequency transmitters. This problem is appropriately modelled using a satellite tool kit that simulates the space environment involving satellites, antennas, emitters, etc in a realistic and integrated manner. Accuracy and size of the geolocation area depend on the distance between emitters and the receiver and also on the contour widths geometry. Results of geolocation accuracy are compared by installing the radio frequency emitter at increasing latitudes and at varying contour widths. It is observed that the emitters placed at lower latitudes and having smaller contour widths provided higher accuracy in geolocation that validates the proposed formulation

Geolocation of RF Emitters Using a Low-Cost UAV-Based Approach

The proliferation of unmanned aerial vehicles (UAVs) in both military and civilian settings has prompted great interest in finding new and innovative ways to utilize these tools. One such application is to locate ground-based radio emitters from a UAV platform. The goal of this research is to study the feasibility of a low-cost (on the order of $1000) UAV geolocation platform. To accomplish this goal, a series of both real-world flight testing and computer simulated scenarios were conducted. Simulations for different sensor uncertainties and approach path scenarios such as loiter and button hook patterns were investigated. Results showed that a high uncertainty sensor of ±10 degrees was able to reliably geolocate the target provided it could fly sufficiently close to the emitter location. For the physical testing, a commercial-off-the-shelf Doppler direction finding unit was chosen as the method of performing the geolocation. Ground testing proved promising, locating the emitter to within 20 meters. However, flight testing showed poor results and was unable to locate the target. Areas of future work that could improve upon these results include investigating how altitude and antenna orientation variations caused by the movement of the aircraft affect the performance of the direction finding unit

Nonlinear Least Squares 3-D Geolocation Solutions using Time Differences of Arrival

This thesis uses a geometric approach to derive and solve nonlinear least squares minimization problems to geolocate a signal source in three dimensions using time differences of arrival at multiple sensor locations. There is no restriction on the maximum number of sensors used. Residual errors reach the numerical limits of machine precision. Symmetric sensor orientations are found that prevent closed form solutions of source locations lying within the null space. Maximum uncertainties in relative sensor positions and time difference of arrivals, required to locate a source within a maximum specified error, are found from these results. Examples illustrate potential requirements specification applications. The maximum machine epsilon and the maximum number of iterations to reach the least squares solution without loss of source location accuracy are estimated. Improvements in accuracy of least squares solutions over closed form solutions are measured

Radio Frequency Emitter Geolocation Using Cubesats

The ability to locate an RF transmitter is a topic of growing interest for civilian and military users alike. Geolocation can provide critical information for the intelligence community, search and rescue operators, and the warfighter. The technology required for geolocation has steadily improved over the past several decades, allowing better performance at longer baseline distances between transmitter and receiver. The expansion of geolocation missions from aircraft to spacecraft has necessitated research into how emerging geolocation methods perform as baseline distances are increased beyond what was previously considered. The CubeSat architecture is a relatively new satellite form which could enable small-scale, low-cost solutions to USAF geolocation needs. This research proposes to use CubeSats as a vehicle to perform geolocation missions in the space domain. The CubeSat form factor considered is a 6-unit architecture that allows for 6000 cm3 of space for hardware. There are a number of methods which have been developed for geolocation applications. This research compares four methods with various sensor configurations and signal properties. The four methods\u27 performance are assessed by simulating and modeling the environment, signals, and geolocation algorithms using MATLAB. The simulations created and run in this research show that the angle of arrival method outperforms the instantaneous received frequency method, especially at higher SNR values. These two methods are possible for single and dual satellite architectures. When three or more satellites are available, the direct position determination method outperforms the three other considered methods

An Analysis of Radio-Frequency Geolocation Techniques for Satellite Systems Design

This research 1) evaluates the effectiveness of CubeSat radio-frequency geolocation and 2) analyzes the sensitivity of different RF algorithms to system parameters. A MATLAB simulation is developed to assess geolocation accuracy for variable system designs and techniques (AOA, TDOA, T/FDOA). An unconstrained maximum likelihood estimator (MLE) and three different digital elevation models (DEM) are utilized as the surface of the Earth constraint to improve geolocation accuracy. The results presented show the effectiveness of the MLE and DEM techniques, the sensitivity of AOA, TDOA, and T/FDOA algorithms, and the system level performance of a CubeSat geolocation cluster in a 500km circular orbit

Development of a Model and Localization Algorithm for Received Signal Strength-Based Geolocation

Location-Based Services (LBS), also called geolocation, have become increasingly popular in the past decades. They have several uses ranging from assisting emergency personnel, military reconnaissance and applications in social media. In geolocation a group of sensors estimate the location of transmitters using position and Radio Frequency (RF) information. A review of the literature revealed that a majority of the Received Signal Strength (RSS) techniques used made erroneous assumptions about the distribution or ignored effects of multiple transmitters, noise and multiple antennas. Further, the corresponding algorithms are often mathematically complex and computationally expensive. To address the issues this dissertation focused on RSS models which account for external factors effects and algorithms that are more efficient and accurate

Estimating Single and Multiple Target Locations Using K-Means Clustering with Radio Tomographic Imaging in Wireless Sensor Networks

Geolocation involves using data from a sensor network to assess and estimate the location of a moving or stationary target. Received Signal Strength (RSS), Angle of Arrival (AoA), and/or Time Difference of Arrival (TDoA) measurements can be used to estimate target location in sensor networks. Radio Tomographic Imaging (RTI) is an emerging Device-Free Localization (DFL) concept that utilizes the RSS values of a Wireless Sensor Network (WSN) to geolocate stationary or moving target(s). The WSN is set up around the Area of Interest (AoI) and the target of interest, which can be a person or object. The target inside the AoI creates a shadowing loss between each link being obstructed by the target. This research focuses on position estimation of single and multiple targets inside a RTI network. This research applies K-means clustering to localize one or more targets. K-means clustering is an algorithm that has been used in data mining applications such as machine learning applications, pattern recognition, hyper-spectral imagery, artificial intelligence, crowd analysis, and Multiple Target Tracking (MTT)

Investigations of 5G localization with positioning reference signals

TDOA is an user-assisted or network-assisted technique, in which the user equipment calculates the time of arrival of precise positioning reference signals conveyed by mobile base stations and provides information about the measured time of arrival estimates in the direction of the position server. Using multilateration grounded on the TDOA measurements of the PRS received from at least three base stations and known location of these base stations, the location server determines the position of the user equipment. Different types of factors are responsible for the positioning accuracy in TDOA method, such as the sample rate, the bandwidth, network deployment, the properties of PRS, signal propagation condition, etc. About 50 meters positioning is good for the 4G/LTE users, whereas 5G requires an accuracy less than a meter for outdoor and indoor users. Noteworthy improvements in positioning accuracy can be achievable with the help of redesigning the PRS in 5G technology. The accuracy for the localization has been studied for different sampling rates along with different algorithms. High accuracy TDOA with 5G positioning reference signal (PRS) for sample rate and bandwidth hasn’t been taken into consideration yet. The key goal of the thesis is to compare and assess the impact of different sampling rates and different bandwidths of PRS on the 5G positioning accuracy. By performing analysis with variable bandwidths of PRS in resource blocks and comparing all the analyses with different bandwidths of PRS in resource blocks, it is undeniable that there is a meaningful decrease in the RMSE and significant growth in the SNR. The higher bandwidth of PRS in resource blocks brings higher SNR while the RMSE of positioning errors also decreases with higher bandwidth. Also, the number of PRS in resource blocks provides lower SNR with higher RMSE values. The analysis with different bandwidths of PRS in resource blocks reveals keeping the RMSE value lower than a meter each time with different statistics is a positivity of the research. The positioning accuracy also analyzed with different sample sizes. With an increased sample size, a decrease in the root mean square error and a crucial increase in the SNR was observed. From this thesis investigation, it is inevitable to accomplish that two different analyses (sample size and bandwidth) done in a different way with the targeted output. A bandwidth of 38.4 MHz and sample size N = 700 required to achieve below 1m accuracy with SNR of 47.04 dB

A Low-Cost Visible Light Positioning System for Indoor Positioning

Currently, a high percentage of the world’s population lives in urban areas, and this proportion will increase in the coming decades. In this context, indoor positioning systems (IPSs) have been a topic of great interest for researchers. On the other hand, Visible Light Communication (VLC) systems have advantages over RF technologies; for instance, they do not need satellite signals or the absence of electromagnetic interference to achieve positioning. Nowadays, in the context of Indoor Positioning (IPS), Visible Light Positioning (VLP) systems have become a strong alternative to RF-based systems, allowing the reduction in costs and time to market. This paper shows a low cost VLP solution for indoor systems. This includes multiple programmable beacons and a receiver which can be plugged to a smartphone running a specific app. The position information will be quickly and securely available through the interchange between the receiver and any configurable LED-beacon which is strategically disposed in an area. The implementation is simple, inexpensive, and no direct communication with any data server is required.This research was funded by INDRA-Adecco Foundation Chair on Accessible Technology, Comunidad de Madrid and the FSE/FEDER Program under grant SINFOTON2-CM (S2018/NMT-4326) and the UNIVERSIDAD CARLOS III DE MADRID under grant 2020/00038/001