Modelling and Simulation of Pseudolite-based Navigation: A GPS-independent Radio Navigation System

The use of global positioning system (GPS) for precision guidance of weapons is being questioned due to its vulnerability of jamming and spoofing for non-military code users. In this paper a novel approach is proposed for guidance of weapons where use of GPS or other civilian Satellite-based navigation system is threatened. The proposed approach is modelled and simulated using SIMULINK for realistic trajectories and scenario. The results of simulation are validated with the actual GPS data

An Assessment of Indoor Geolocation Systems

Currently there is a need to design, develop, and deploy autonomous and portable indoor geolocation systems to fulfil the needs of military, civilian, governmental and commercial customers where GPS and GLONASS signals are not available due to the limitations of both GPS and GLONASS signal structure designs. The goal of this dissertation is (1) to introduce geolocation systems; (2) to classify the state of the art geolocation systems; (3) to identify the issues with the state of the art indoor geolocation systems; and (4) to propose and assess four WPI indoor geolocation systems. It is assessed that the current GPS and GLONASS signal structures are inadequate to overcome two main design concerns; namely, (1) the near-far effect and (2) the multipath effect. We propose four WPI indoor geolocation systems as an alternative solution to near-far and multipath effects. The WPI indoor geolocation systems are (1) a DSSS/CDMA indoor geolocation system, (2) a DSSS/CDMA/FDMA indoor geolocation system, (3) a DSSS/OFDM/CDMA/FDMA indoor geolocation system, and (4) an OFDM/FDMA indoor geolocation system. Each system is researched, discussed, and analyzed based on its principle of operation, its transmitter, the indoor channel, and its receiver design and issues associated with obtaining an observable to achieve indoor navigation. Our assessment of these systems concludes the following. First, a DSSS/CDMA indoor geolocation system is inadequate to neither overcome the near-far effect not mitigate cross-channel interference due to the multipath. Second, a DSSS/CDMA/FDMA indoor geolocation system is a potential candidate for indoor positioning, with data rate up to 3.2 KBPS, pseudorange error, less than to 2 m and phase error less than 5 mm. Third, a DSSS/OFDM/CDMA/FDMA indoor geolocation system is a potential candidate to achieve similar or better navigation accuracy than a DSSS/CDMA indoor geolocation system and data rate up to 5 MBPS. Fourth, an OFDM/FDMA indoor geolocation system is another potential candidate with a totally different signal structure than the pervious three WPI indoor geolocation systems, but with similar pseudorange error performance

Carrier-phase multipath in satellite-based positioning

[no abstract

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Physical Layer Challenges and Solutions in Seamless Positioning via GNSS, Cellular and WLAN Systems

As different positioning applications have started to be a common part of our lives, positioning methods have to cope with increasing demands. Global Navigation Satellite System (GNSS) can offer accurate location estimate outdoors, but achieving seamless large-scale indoor localization remains still a challenging topic. The requirements for simple and cost-effective indoor positioning system have led to the utilization of wireless systems already available, such as cellular networks and Wireless Local Area Network (WLAN). One common approach with the advantage of a large-scale standard-independent implementation is based on the Received Signal Strength (RSS) measurements.This thesis addresses both GNSS and non-GNSS positioning algorithms and aims to offer a compact overview of the wireless localization issues, concentrating on some of the major challenges and solutions in GNSS and RSS-based positioning. The GNSS-related challenges addressed here refer to the channel modelling part for indoor GNSS and to the acquisition part in High Sensitivity (HS)-GNSS. The RSSrelated challenges addressed here refer to the data collection and calibration, channel effects such as path loss and shadowing, and three-dimensional indoor positioning estimation.This thesis presents a measurement-based analysis of indoor channel models for GNSS signals and of path loss and shadowing models for WLAN and cellular signals. Novel low-complexity acquisition algorithms are developed for HS-GNSS. In addition, a solution to transmitter topology evaluation and database reduction solutions for large-scale mobile-centric RSS-based positioning are proposed. This thesis also studies the effect of RSS offsets in the calibration phase and various floor estimators, and offers an extensive comparison of different RSS-based positioning algorithms

A comprehensive proposal for securing terrestrial radionavigation systems

The security of terrestrial radionavigation systems (TRNS) has not yet been addressed in the literature. This proposal builds on what is known about securing global navigation satellite systems (GNSS) to address this gap, re-evaluating proposals for GNSS security in light of the distinctive properties of TRNS. TRNS of the type envisioned in this paper are currently in their infancy, unburdened by considerations of backwards compatibility: security for TRNS is a clean slate. This thesis argues that waveform- or signal-level security measures are irrelevant for TRNS, preventing neither spoofing nor unauthorized use of the service. Thus, only security measures which modify navigation message bits merit consideration. This thesis proposes orthogonal mechanisms combining navigation message encryption (NME) and navigation message authentication (NMA), constructed from standard cryptography primitives and specialized to TRNS: message encryption allows providers to offer tiered access to navigation parameters on a bit-by-bit basis, and message authentication disperses the bits of a message authentication code across all data packets, posing an additional challenge to spoofers. This cryptographic proposal, however, is still vulnerable to certain types of replay threats. This thesis addresses this gap by augmenting TRNS with autonomous signal-situational-awareness (SSA) capability, allowing TRNS operators to detect spoofing and meaconing attacks. Two signal authentication techniques for SSA are developed to detect a weak spoofing signal in the presence of static and dynamic multipath. This thesis also proposes enhancements to these signal authentication techniques. These enhancements exploit the synergy from combining information across multiple epochs, or over multiple monitoring beacons, to further lower the spoofer detection threshold. Both techniques with their enhancements are shown to be effective in simulations of the varied operating environments that a generic TRNS will encounter. With both proposed cryptographic NME+NMA scheme and autonomous SSA in place, TRNS gains a defensive capability that GNSS cannot easily match: a comprehensive defense against most man-in-the-middle attacks on position, navigation and timing services.Aerospace Engineerin

Positioning algorithms for RFID-based multi-sensor indoor/outdoor positioning techniques

Position information has been very important. People need this information almost everywhere all the time. However, it is a challenging task to provide precise positions indoor/outdoor seamlessly. Outdoor positioning has been widely studied and accurate positions can usually be achieved by well developed GPS techniques. However, these techniques are difficult to be used indoor since GPS signals are too weak to be received. The alternative techniques, such as inertial sensors and radio-based pseudolites, can be used for indoor positioning but have limitations. For example, the inertial sensors suffer from drifting problems caused by the accumulating errors of measured acceleration and velocity and the radio-based techniques are prone to the obstructions and multipath effects of the transmitted signals. It is therefore necessary to develop improved methods for minimising the limitations of the current indoor positioning techniques and providing an adequately precise solution of the indoor positioning and seamless indoor/outdoor positioning. The main objectives of this research are to investigate and develop algorithms for the low-cost and portable indoor personal positioning system using Radio Frequency Identification (RFID) based multi-sensor techniques, such as integrating with Micro-Electro-Mechanical Systems (MEMS) Inertial Navigation System (INS) and/or GPS. A RFID probabilistic Cell of Origin (CoO) algorithm is developed, which is superior to the conventional CoO positioning algorithm in its positioning accuracy and continuity. Integration algorithms are also developed for RFID-based multi-sensor positioning techniques, which can provide metre-level positioning accuracy for dynamic personal positioning indoors. In addition, indoor/outdoor seamless positioning algorithms are investigated based on the iterated Reduced Sigma Point Kalman Filter (RSPKF) for RFID/MEMS INS/low-cost GPS integrated technique, which can provide metre-level positioning accuracy for personal positioning. 3-D GIS assisted personal positioning algorithms are also developed, including the map matching algorithm based on the probabilistic maps for personal positioning and the Site Specific (SISP) propagation model for efficiently generating the RFID signal strength distributions in location fingerprinting algorithms. Both static and dynamic indoor positioning experiments have been conducted using the RFID and RFID/MEMS INS integrated techniques. Metre-level positioning accuracy is achieved (e.g. 3.5m in rooms and 1.5m in stairways for static position, 4m for dynamic positioning and 1.7m using the GIS assisted positioning algorithms). Various indoor/outdoor experiments have been conducted using the RFID/MEMS INS/low-cost GPS integrated technique. It indicates that the techniques selected in this study, integrated with the low-cost GPS, can be used to provide continuous indoor/outdoor positions in approximately 4m accuracy with the iterated RSPKF. The results from the above experiments have demonstrated the improvements of integrating multiple sensors with RFID and utilizing the 3-D GIS data for personal positioning. The algorithms developed can be used in a portable RFID based multi-sensor positioning system to achieve metre-level accuracy in the indoor/outdoor environments. The proposed system has potential applications, such as tracking miners underground, monitoring athletes, locating first responders, guiding the disabled and providing other general location based services (LBS)

Indoor collaborative positioning based on a multi-sensor and multi-user system

With recent developments in the Global Satellite Navigation Systems (GNSS), the applications and services of positioning and navigation have developed rapidly worldwide. Location-based services (LBS) have become a big application which provide position related services to the mass market. As LBS applications become more popular, positioning services and capacity are demanded to cover all types of environment with improved accuracy and reliability. While GNSS can provide promising positioning and navigation solutions in open outdoor environments, it does not work well when inside buildings, in tunnels or under canopy. Positioning in such difficult environments have been known as the indoor positioning problem. Although the problem has been looked into for more than a decade, there currently no solution that can compare to the performance of GNSS in outdoor environments. This thesis introduces a collaborative indoor positioning solution based on particle filtering which integrates multiple sensors, e.g. inertial sensors, Wi-Fi signals, map information etc., and multiple local users which provide peer-to-peer (P2P) relative ranging measurements. This solution addresses three current problems of indoor positioning. First of all is the positioning accuracy, which is limited by the availability of sensors and the quality of their signals in the environment. The collaborative positioning solution integrates a number of sensors and users to provide better measurements and restrict measurement error from growing. Secondly, the reliability of the positioning solutions, which is also affected by the signal quality. The unpredictable behaviour of positioning signals and data could lead to many uncertainties in the final positioning result. A successful positioning system should be able to deal with changes in the signal and provide reliable positioning results using different data processing strategies. Thirdly, the continuity and robustness of positioning solutions. While the indoor environment can be very different from one another, hence applicable signals are also different, the positioning solution should take into account the uniqueness of different situations and provide continuous positioning result regardless of the changing datWith recent developments in the Global Satellite Navigation Systems (GNSS), the applications and services of positioning and navigation have developed rapidly worldwide. Location based services (LBS) have become a big application which provide position related services to the mass market. As LBS applications become more popular, positioning services and capacity are demanded to cover all types of environment with improved accuracy and reliability. While GNSS can provide promising positioning and navigation solutions in open outdoor environments, it does not work well when inside buildings, in tunnels or under canopy. Positioning in such difficult environments have been known as the indoor positioning problem. Although the problem has been looked into for more than a decade, there currently no solution that can compare to the performance of GNSS in outdoor environments. This thesis introduces a collaborative indoor positioning solution based on particle filtering which integrates multiple sensors, e.g. inertial sensors, Wi-Fi signals, map information etc., and multiple local users which provide peer-to-peer (P2P) relative ranging measurements. This solution addresses three current problems of indoor positioning. First of all is the positioning accuracy, which is limited by the availability of sensors and the quality of their signals in the environment. The collaborative positioning solution integrates a number of sensors and users to provide better measurements and restrict measurement error from growing. Secondly, the reliability of the positioning solutions, which is also affected by the signal quality. The unpredictable behaviour of positioning signals and data could lead to many uncertainties in the final positioning result. A successful positioning system should be able to deal with changes in the signal and provide reliable positioning results using different data processing strategies. Thirdly, the continuity and robustness of positioning solutions. While the indoor environment can be very different from one another, hence applicable signals are also different, the positioning solution should take into account the uniqueness of different situations and provide continuous positioning result regardless of the changing data. The collaborative positioning aspect is examined from three aspects, the network geometry, the network size and the P2P ranging measurement accuracy. Both theoretical and experimental results indicate that a collaborative network with a low dilution of precision (DOP) value could achieve better positioning accuracy. While increasing sensors and users will reduce DOP, it will also increase computation load which is already a disadvantage of particle filters. The most effective collaborative positioning network size is thus identified and applied. While the positioning system measurement error is constrained by the accuracy of the P2P ranging constraint, the work in this thesis shows that even low accuracy measurements can provide effective constraint as long as the system is able to identify the different qualities of the measurements. The proposed collaborative positioning algorithm constrains both inertial measurements and Wi-Fi fingerprinting to enhance the stability and accuracy of positioning result, achieving metre-level accuracy. The application of collaborative constraints also eliminate the requirement for indoor map matching which had been a very useful tool in particle filters for indoor positioning purposes. The wall constraint can be replaced flexibly and easily with relative constraint. Simulations and indoor trials are carried out to evaluate the algorithms. Results indicate that metre-level positioning accuracy could be achieved and collaborative positioning also gives the system more flexibility to adapt to different situations when Wi-Fi or collaborative ranging is unavailable. The collaborative positioning aspect is examined from three aspects, the network geometry, the network size and the P2P ranging measurement accuracy. Both theoretical and experimental results indicate that a collaborative network with a low dilution of precision (DOP) value could achieve better positioning accuracy. While increasing sensors and users will reduce DOP, it will also increase computation load which is already a disadvantage of particle filters. The most effective collaborative positioning network size is thus identified and applied. While the positioning system measurement error is constrained by the accuracy of the P2P ranging constraint, the work in this thesis shows that even low accuracy measurements can provide effective constraint as long as the system is able to identify the different qualities of the measurements. The proposed collaborative positioning algorithm constrains both inertial measurements and Wi-Fi fingerprinting to enhance the stability and accuracy of positioning result, achieving metre-level accuracy. The application of collaborative constraints also eliminate the requirement for indoor map matching which had been a very useful tool in particle filters for indoor positioning purposes. The wall constraint can be replaced flexibly and easily with relative constraint. Simulations and indoor trials are carried out to evaluate the algorithms. Results indicate that metre-level positioning accuracy could be achieved and collaborative positioning also gives the system more flexibility to adapt to different situations when Wi-Fi or collaborative ranging is unavailable

Indoor collaborative positioning based on a multi-sensor and multi-user system

With recent developments in the Global Satellite Navigation Systems (GNSS), the applications and services of positioning and navigation have developed rapidly worldwide. Location-based services (LBS) have become a big application which provide position related services to the mass market. As LBS applications become more popular, positioning services and capacity are demanded to cover all types of environment with improved accuracy and reliability. While GNSS can provide promising positioning and navigation solutions in open outdoor environments, it does not work well when inside buildings, in tunnels or under canopy. Positioning in such difficult environments have been known as the indoor positioning problem. Although the problem has been looked into for more than a decade, there currently no solution that can compare to the performance of GNSS in outdoor environments. This thesis introduces a collaborative indoor positioning solution based on particle filtering which integrates multiple sensors, e.g. inertial sensors, Wi-Fi signals, map information etc., and multiple local users which provide peer-to-peer (P2P) relative ranging measurements. This solution addresses three current problems of indoor positioning. First of all is the positioning accuracy, which is limited by the availability of sensors and the quality of their signals in the environment. The collaborative positioning solution integrates a number of sensors and users to provide better measurements and restrict measurement error from growing. Secondly, the reliability of the positioning solutions, which is also affected by the signal quality. The unpredictable behaviour of positioning signals and data could lead to many uncertainties in the final positioning result. A successful positioning system should be able to deal with changes in the signal and provide reliable positioning results using different data processing strategies. Thirdly, the continuity and robustness of positioning solutions. While the indoor environment can be very different from one another, hence applicable signals are also different, the positioning solution should take into account the uniqueness of different situations and provide continuous positioning result regardless of the changing datWith recent developments in the Global Satellite Navigation Systems (GNSS), the applications and services of positioning and navigation have developed rapidly worldwide. Location based services (LBS) have become a big application which provide position related services to the mass market. As LBS applications become more popular, positioning services and capacity are demanded to cover all types of environment with improved accuracy and reliability. While GNSS can provide promising positioning and navigation solutions in open outdoor environments, it does not work well when inside buildings, in tunnels or under canopy. Positioning in such difficult environments have been known as the indoor positioning problem. Although the problem has been looked into for more than a decade, there currently no solution that can compare to the performance of GNSS in outdoor environments. This thesis introduces a collaborative indoor positioning solution based on particle filtering which integrates multiple sensors, e.g. inertial sensors, Wi-Fi signals, map information etc., and multiple local users which provide peer-to-peer (P2P) relative ranging measurements. This solution addresses three current problems of indoor positioning. First of all is the positioning accuracy, which is limited by the availability of sensors and the quality of their signals in the environment. The collaborative positioning solution integrates a number of sensors and users to provide better measurements and restrict measurement error from growing. Secondly, the reliability of the positioning solutions, which is also affected by the signal quality. The unpredictable behaviour of positioning signals and data could lead to many uncertainties in the final positioning result. A successful positioning system should be able to deal with changes in the signal and provide reliable positioning results using different data processing strategies. Thirdly, the continuity and robustness of positioning solutions. While the indoor environment can be very different from one another, hence applicable signals are also different, the positioning solution should take into account the uniqueness of different situations and provide continuous positioning result regardless of the changing data. The collaborative positioning aspect is examined from three aspects, the network geometry, the network size and the P2P ranging measurement accuracy. Both theoretical and experimental results indicate that a collaborative network with a low dilution of precision (DOP) value could achieve better positioning accuracy. While increasing sensors and users will reduce DOP, it will also increase computation load which is already a disadvantage of particle filters. The most effective collaborative positioning network size is thus identified and applied. While the positioning system measurement error is constrained by the accuracy of the P2P ranging constraint, the work in this thesis shows that even low accuracy measurements can provide effective constraint as long as the system is able to identify the different qualities of the measurements. The proposed collaborative positioning algorithm constrains both inertial measurements and Wi-Fi fingerprinting to enhance the stability and accuracy of positioning result, achieving metre-level accuracy. The application of collaborative constraints also eliminate the requirement for indoor map matching which had been a very useful tool in particle filters for indoor positioning purposes. The wall constraint can be replaced flexibly and easily with relative constraint. Simulations and indoor trials are carried out to evaluate the algorithms. Results indicate that metre-level positioning accuracy could be achieved and collaborative positioning also gives the system more flexibility to adapt to different situations when Wi-Fi or collaborative ranging is unavailable. The collaborative positioning aspect is examined from three aspects, the network geometry, the network size and the P2P ranging measurement accuracy. Both theoretical and experimental results indicate that a collaborative network with a low dilution of precision (DOP) value could achieve better positioning accuracy. While increasing sensors and users will reduce DOP, it will also increase computation load which is already a disadvantage of particle filters. The most effective collaborative positioning network size is thus identified and applied. While the positioning system measurement error is constrained by the accuracy of the P2P ranging constraint, the work in this thesis shows that even low accuracy measurements can provide effective constraint as long as the system is able to identify the different qualities of the measurements. The proposed collaborative positioning algorithm constrains both inertial measurements and Wi-Fi fingerprinting to enhance the stability and accuracy of positioning result, achieving metre-level accuracy. The application of collaborative constraints also eliminate the requirement for indoor map matching which had been a very useful tool in particle filters for indoor positioning purposes. The wall constraint can be replaced flexibly and easily with relative constraint. Simulations and indoor trials are carried out to evaluate the algorithms. Results indicate that metre-level positioning accuracy could be achieved and collaborative positioning also gives the system more flexibility to adapt to different situations when Wi-Fi or collaborative ranging is unavailable

Simultaneous Positioning and Communications: Hybrid Radio Architecture, Estimation Techniques, and Experimental Validation

abstract: Limited spectral access motivates technologies that adapt to diminishing resources and increasingly cluttered environments. A joint positioning-communications system is designed and implemented on \acf{COTS} hardware. This system enables simultaneous positioning of, and communications between, nodes in a distributed network of base-stations and unmanned aerial systems (UASs). This technology offers extreme ranging precision ($<$ 5 cm) with minimal bandwidth (10 MHz), a secure communications link to protect against cyberattacks, a small form factor that enables integration into numerous platforms, and minimal resource consumption which supports high-density networks. The positioning and communications tasks are performed simultaneously with a single, co-use waveform, which efficiently utilizes limited resources and supports higher user densities. The positioning task uses a cooperative, point-to-point synchronization protocol to estimate the relative position and orientation of all users within the network. The communications task distributes positioning information between users and secures the positioning task against cyberattacks. This high-performance system is enabled by advanced time-of-arrival estimation techniques and a modern phase-accurate distributed coherence synchronization algorithm. This technology may be installed in ground-stations, ground vehicles, unmanned aerial systems, and airborne vehicles, enabling a highly-mobile, re-configurable network with numerous applications.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201