Mobile devices with wireless networking capabilities are used in a wide range of environments. Geolocation information increases the value of the data generated by a device and is vital in the development of a wide range of applications from autonomous vehicles to the Internet of things. Systems that generate signals specifically for geolocation have become widely adopted but, due to fundamental constraints, lack coverage and accuracy in complex urban and indoor environments. In addition to this, the reliance on a single signal source is not desirable in many applications that value the integrity of the geolocation estimate. A direction of research aiming to improve geolocation in indoor and urban environments measures signals of opportunity in order to generate a more robust estimate. While this approach improves signal availability, the unpredictable nature of these variable and uncontrolled signals leads to poor geolocation estimates, which are typically not suitable for use in many applications. This project aims to improve on the accuracy, resilience and integrity of a geolocation estimate obtained from signal of opportunity measurements in indoor and urban environments while reducing hardware requirements. This has been achieved by efficiently coupling signals of opportunity within the radio environment with other system signals, such as those from an inertial measurement unit. Research has been carried out to optimise the coupling of these data sources resulting in techniques to allow the identification and removal of key error drivers from both the radio environment and other system sensors. This thesis proposes a specifically designed extended Kalman filter to improve on the signal coupling. The filter aims to optimise the accuracy of radio environment measurements while also providing the ability to identify signal error sources in urban and indoor environments, leading to both greater accuracy and resilience of the geo-location estimate. Further, the proposed extended Kalman filter may use the radio environment as a source of geolocation data. The ability of the filter to recognise and mitigate leading radio environment error sources such as multipath and interference allowed the design of filters to obtain detailed and accurate signal strength and time of arrival information. The thesis also presents a thorough set of simulation and modelling experiments to investigate and optimise the efficiency of the proposed solutions in a range of environments. Validation testing confirmed that in the urban and indoor environments, the average error of geo-location estimates has been reduced from 10 m to 3 m without improvement to the hardware surrounding infrastructure. The improvements presented in this thesis allow networked devices to improve the value of their data by incorporating the context that comes from increased geolocation accuracy and resilience. In turn, this allows the development of a wide range of new location based applications for mobile devises in indoor and urban environments.

Measurements of soil moisture are a crucial component for understanding the global water and carbon cycle, weather forecasting, climate models, drought prediction, and agriculture production. Active and passive microwave radar instruments are currently in use for remote sensing of soil moisture. Signals of Opportunity (SoOp) based remote sensing has recently emerged as a complementary method for soil moisture remote sensing. SoOp reuses general digital communication signals allowing the reuse of allocated wireless communication signal bands for science measurements. This thesis developed a tower based SoOp instrument implementing frequencies in the P-Band and S-Band. Two field campaigns were conducted using this new instrument during the summers of 2017 and 2018 at Purdue's Agronomy Center for Research and Education.

GPS is a widely accepted means of navigation, whether it is for civilian or military means. With the implementation of GPS on smart projectiles, these weapons have been able to achieve remarkable accuracy. Even though the improvements in accuracy are impressive, GPS signals are susceptible to jamming and spoofing by a sufficiently motivated enemy. The work reported here examines the viability of constructing a navigation solution using ground based signals of opportunity that provide range and range rate information. Using a generalized sensor model encompassing the key error terms, a variety of physical devices are included in the analysis. For a typical indirect fire trajectory, navigation solutions are computed as a function of the number and density of signal sources, terrain type, and sensor errors. Systematic studies were performed using these parameters in order to better understand the merits and demerits of this type of system to create a useful navigation solution. Based on these studies, results indicate that navigation solutions can be computed with the same accuracy as current GPS systems with a moderate number of signal sources. Generally, more accurate solutions are obtained when the projectile is directly over the signal sources and there is variation of signal source location in all three axes.

Navigation is an invisible utility that is often taken for granted with considerable societal and economic impacts. Not only is navigation essential to our modern life, but the more it advances, the more possibilities are created. Navigation is at the heart of three emerging fields: autonomous vehicles, location-based services, and intelligent transportation systems. Global navigation satellite systems (GNSS) are insufficient for reliable anytime, anywhere navigation, particularly indoors, in deep urban canyons, and in environments under malicious attacks (e.g., jamming and spoofing). The conventional approach to overcome the limitations of GNSS-based navigation is to couple GNSS receivers with dead reckoning sensors. A new paradigm, termed opportunistic navigation (OpNav), is emerging. OpNav is analogous to how living creatures naturally navigate: by learning their environment. OpNav aims to exploit the plenitude of ambient radio frequency signals of opportunity (SOPs) in the environment. OpNav radio receivers, which may be handheld or vehicle-mounted, continuously search for opportune signals from which to draw position and timing information, employing on-the-fly signal characterization as necessary. In collaborative opportunistic navigation (COpNav), multiple receivers share information to construct and continuously refine a global signal landscape. For the sake of motivation, consider the following problem. A number of receivers with no a priori knowledge about their own states are dropped in an environment comprising multiple unknown terrestrial SOPs. The receivers draw pseudorange observations from the SOPs. The receivers' objective is to build a high-fidelity signal landscape map of the environment within which they localize themselves in space and time. We then ask: (i) Under what conditions is the environment fully observable? (ii) In cases where the environment is not fully observable, what are the observable states? (iii) How would receiver-controlled maneuvers affect observability? (iv) What is the degree of observability of the various states in the environment? (v) What motion planning strategy should the receivers employ for optimal information gathering? (vi) How effective are receding horizon strategies over greedy for receiver trajectory optimization, and what are their limitations? (vii) What level of collaboration between the receivers achieves a minimal price of anarchy? This dissertation addresses these fundamental questions and validates the theoretical conclusions numerically and experimentally. / text

<div>Surface soil moisture is one of the few direct hydrological variables which can be measured. It plays a crucial part in the water cycle, agriculture, drought development, runoff generation, and many other phenomena. Satellite observations from active and passive microwave radiometers are best suited for the retrieval of soil moisture. The relationship between soil dielectric constant and water content is direct and is used to determine the surface soil moisture levels. Active microwave remote sensing techniques measure the energy reflected from target surfaces (ocean, soil, biomass) after transmitting a pulse of microwave energy, whereas passive microwave sensors measure the self-emissions of the target surfaces. The passive missions by ESA's SMOS and NASA's SMAP have demonstrated this technology for remote sensing on a global scale. Global Navigation Satellite System-Reflectometry (GNSS-R) is an alternative approach to the remote sensing of soil moisture, as demonstrated through several airborne and ground-based experiments. The new technique of Signals of Opportunity (SoOp) uses a bistatic radar configuration in which the non-cooperative transmitter already transmits signals designed for communication or navigation. The receiver reuses the reflected energy from the target surface (ocean, soil, biomass), thereby making the digital communication and navigation signal spectrum useful to the remote sensing science community. Several airborne and ground-based experiments have been conducted on the use of digital communication signals, a range of frequencies from P-band to Ku-band, for measurement of ocean surface roughness, wind speed, and soil moisture. </div><div> </div><div>This thesis presents the preliminary results obtained for reflectivity retrievals for the 2017 and 2018 S-band tower-based SoOp field experiment conducted at Purdue's Agronomy Center for Research and Education (ACRE). XM signals were recorded by a sky-facing antenna and an Earth-facing antenna mounted atop a tower. The line-of-sight (direct) signal is captured by the sky-facing antenna and reflected signal from the soil captured by the Earth-facing antenna was used for the ambiguity function of XM transmission. A link budget was used to determine the received signal to noise ratio (SNR). The cross-correlation between the direct and the reflected XM signals was used to estimate reflectivity values. The reflectivity retrievals were compared with the in-situ soil moisture content at 5 cm depth obtained by the HydraProbes. The reflectivity values were also verified by a Signals of Opportunity (SoOp) Coherent Bistatic (SCoBi) simulated model.</div>

This thesis considers the problem of using signals of opportunity (SOO) with known direction of arrival (DOA) for aircraft positioning. SOO is a collective name for a wide range of signals not intended for navigation but which can be intercepted by the radar warning system on an aircraft. These signals can for example aid an unassisted inertial navigation system (INS) in areas where the global navigation satellite system (GNSS) is inaccessible. Challenges arise as the signals are transmitted from non-controllable sources without any guarantee of quality and availability. Hence, it is important that any estimation method utilising SOO is robust and statistically consistent in case of time-varying signals of different quality, missed detections and unreliable signals such as outliers. The problem is studied using SOO sources with either known or unknown locations. An extended Kalman filter (EKF) based solution is proposed for the first case which is shown to significantly improve the localisation performance compared to an unassisted INS in common scenarios. Yet, a number of factors affect this performance, including the measurement noise variance, the signal rate and the availability of known source locations. An outlier rejection mechanism is developed which is shown to increase the robustness of the suggested method. A numerical evaluation indicates that statistical consistency can be maintained in many situations even with the above-mentioned challenges. An EKF based simultaneous localisation and mapping (SLAM) solution is proposed for the case with unknown SOO source locations. The flight trajectory and initialisation process of new SOO sources are critical in this case. A method based on nonlinear least squares is proposed for the initialisation process, where new SOO sources are only allowed to be initialised in the filter once a set of requirements are fulfilled. This method has shown to increase the robustness during initialisation, when the outlier rejection is not applicable. When combining known and unknown SOO source locations, a more stable localisation solution is obtained compared to when all locations are unknown. Applicability of the proposed solution is verified by a numerical evaluation. The computational time increases cubically with the number of sources in the state and quadratically with the number of measurements. The time is substantially increased during landmark initialisation.

This dissertation proposes a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The proposed methodology has been built upon the literature review, analyses, and findings from a number of published studies throughout the dissertation research. Namely, a Sig- nals of Opportunity Coherent Bistatic scattering model (SCoBi) has been first developed at MSU and then its simulator has been open-sourced. Simulated GNSS-Reflectometry (GNSS-R) analyses have been conducted by using SCoBi. Significant findings have been noted such that (1) Although the dominance of either the coherent reflections or incoher- ent scattering over land is a debate, we demonstrated that coherent reflections are stronger for flat and smooth surfaces covered by low-to-moderate vegetation canopy; (2) The influ- ence of several land geophysical parameters such as SM, vegetation water content (VWC), and surface roughness on the bistatic reflectivity was quantified, the dynamic ranges of reflectivity changes due to SM and VWC are much higher than the changes due to the surface roughness. Such findings of these analyses, combined with a comprehensive lit- erature survey, have led to the present inversion algorithm: Physics- and learning-based retrieval of soil moisture information from space-borne GNSS-R measurements that are taken by NASA’s CYGNSS mission. The study is the first work that proposes a machine learning-based, non-parametric, and non-linear regression algorithm for CYGNSS-based soil moisture estimation. The results over point-scale soil moisture observations demon- strate promising performance for applicability to large scales. Potential future work will be extension of the methodology to global scales by training the model with larger and diverse data sets.

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Accurate measurement of soil moisture (SM) has a significant impact on agricultural production, hydrological modeling, forestry, horticulture, waste management, and other environmental fields. Particularly in precision agriculture (PA), high spatiotemporal resolution information about surface SM is crucial. However, the use of invasive SM probes and other sensors is expensive and requires extensive manpower. Moreover, these intrusive techniques provide point measurements and are unsuitable for large agricultural fields. As an alternative, this dissertation explores the remote sensing of surface SM by utilizing the surface reflectivity estimated from global navigation satellite systems reflectometry (GNSS-R) data acquired through smartphones and off-the-shelf, cost-effective U-blox global navigation satellite systems (GNSS) receivers. To estimate surface reflectivity, the GNSS receivers are attached underneath a small unmanned aircraft system (UAS), which flies over agricultural fields. Additionally, this dissertation investigates a fully custom UAS-based dual-polarized L-band microwave radiometric measurement technique over agricultural areas to estimate surface brightness temperature (����). The radiometer measures surface emissivity as ����, allowing for the estimation of surface SM while considering the detection and removal of radio frequency interference (RFI) from the radiometric measurements. This radiometer processes the data in near real-time onboard the UAS, collecting raw in-phase and quadratic (I&Q) signals across the study field. This feature mitigates the RFI onboard and significantly reduces post-processing time. In summary, this study highlights the utilization of smartphones and semi-custom GNSS receivers in conjunction with UAS-based GNSS-R techniques and UAS-based L-band microwave radiometry for the estimation of surface reflectivity and ����. The radiometric measurement of surface emissivity is related to surface reflectivity through the relationship (Emissivity = 1 -Reflectivity).

The potential for high spatio-temporal resolution microwave measurements has urged the adoption of the signals of opportunity (SoOp) passive radar technique for use in remote sensing. Recent trends in particular target highly complex remote sensing problems such as root-zone soil moisture and snow water equivalent. This dissertation explores the continued open-sourcing of the SoOp coherent bistatic scattering model (SCoBi) and its use in soil moisture sensing applications. Starting from ground-based applications, the feasibility of root-zone soil moisture remote sensing is assessed using available SoOp resources below L-band. A modularized, spaceborne model is then developed to simulate land-surface scattering and delay-Doppler maps over the available spectrum of SoOp resources. The simulation tools are intended to provide insights for future spaceborne modeling pursuits.