Emitter Location Finding using Particle Swarm Optimization

Using several spatially separated receivers, nowadays positioning techniques, which are implemented to determine the location of the transmitter, are often required for several important disciplines such as military, security, medical, and commercial applications. In this study, localization is carried out by particle swarm optimization using time difference of arrival. In order to increase the positioning accuracy, time difference of arrival averaging based two new methods are proposed. Results are compared with classical algorithms and Cramer-Rao lower bound which is the theoretical limit of the estimation error

Artificial neural networks for location estimation and co-cannel interference suppression in cellular networks

This thesis reports on the application of artificial neural networks to two important problems encountered in cellular communications, namely, location estimation and co-channel interference suppression. The prediction of a mobile location using propagation path loss (signal strength) is a very difficult and complex task. Several techniques have been proposed recently mostly based on linearized, geometrical and maximum likelihood methods. An alternative approach based on artificial neural networks is proposed in this thesis which offers the advantages of increased flexibility to adapt to different environments and high speed parallel processing. Location estimation provides users of cellular telephones with information about their location. Some of the existing location estimation techniques such as those used in GPS satellite navigation systems require non-standard features, either from the cellular phone or the cellular network. However, it is possible to use the existing GSM technology for location estimation by taking advantage of the signals transmitted between the phone and the network. This thesis proposes the application of neural networks to predict the location coordinates from signal strength data. New multi-layered perceptron and radial basis function based neural networks are employed for the prediction of mobile locations using signal strength measurements in a simulated COST-231 metropolitan environment. In addition, initial preliminary results using limited available real signal-strength measurements in a metropolitan environment are also reported comparing the performance of the neural predictors with a conventional linear technique. The results indicate that the neural predictors can be trained to provide a near perfect mapping using signal strength measurements from two or more base stations. The second application of neural networks addressed in this thesis, is concerned with adaptive equalization, which is known to be an important technique for combating distortion and Inter-Symbol Interference (ISI) in digital communication channels. However, many communication systems are also impaired by what is known as co-channel interference (CCI). Many digital communications systems such as digital cellular radio (DCR) and dual polarized micro-wave radio, for example, employ frequency re-usage and often exhibit performance limitation due to co-channel interference. The degradation in performance due to CCI is more severe than due to ISI. Therefore, simple and effective interference suppression techniques are required to mitigate the interference for a high-quality signal reception. The current work briefly reviews the application of neural network based non-linear adaptive equalizers to the problem of combating co-channel interference, without a priori knowledge of the channel or co-channel orders. A realistic co-channel system is used as a case study to demonstrate the superior equalization capability of the functional-link neural network based Decision Feedback Equalizer (DFE) compared to other conventional linear and neural network based non-linear adaptive equalizers.This project was funded by Solectron (Scotland) Ltd

CSM-428: Techniques used for Location-based Services: A Survey

Not availabl

Navigation with Limited Prior Information Using Time Difference of Arrival Measurements from Signals of Opportunity

The Global Positioning System (GPS) provides world-wide availability to high-accuracy navigation and positioning information. However, the threats to GPS are increasing, and many limitations of GPS are being encountered. Simultaneously, systems previously considered as viable backups or supplements to GPS are being shut down. This creates the need for system alternatives. Navigation using signals of opportunity (SoOP) exploits any signal that is available in a given area, regardless of whether or not the original intent of the signal was for navigation. Common techniques to compute a position estimate using SoOP include received signal strength, angle of arrival, time of arrival, and time difference of arrival (TDOA). To estimate the position of a SoOP receiver, existing TDOA algorithms require one reference receiver and multiple transmitters, all with precisely known positions. This thesis considers modifications to an existing algorithm to produce a comparable position estimate without requiring precise a priori knowledge of the transmitters or reference receiver(s). Using Amplitude Modulation (AM) SoOP, the effect of erroneous a priori data on the existing algorithm are investigated. A proof-of-concept for three new estimation algorithms is presented in this research. Two of the estimators successfully demonstrate comparable performance to the existing algorithm. This is demonstrated in six different transmitter environments using four different receiver configurations

Automatic Dependent Surveillance Broadcast (ADS-B) Security Mitigation through Multilateration

Automatic Dependent Surveillance Broadcast (ADS-B) was mandated January 1st, 2020 to all commercial aircraft that fly over 10,000 ft. This radio frequency (RF) based technology is part of an international plan to phase out radar-based surveillance within the airspace. Unfortunately, due to a lack of encryption and other security measures, ADS-B transmission remains open for possible exploitation. This thesis will explore the use of Multilateration (MLAT) to validate location data provided within the ADS-B framework. MLAT uses the Time Difference of Arrival (TDOA) at multiple locations to determine the origin of a received signal. Additionally, as MLAT greatly depends on the topology of receiver location, multiple configurations will be examined and simulated within the confines of a real-world application. Finally, adversary spoofing scenarios were explored by injecting a stationary and moving adversary into the system. The adversary transmitted ADS-B location data from a different origin than the packets would indicate to create a fake aircraft in the airspace. The performance of the MLAT model was analyzed to determine its ability to flag the adversary’s data as suspicious