Root-zone soil moisture (RZSM) is an important variable when forecasting plant growth, determining water availability during drought, and understanding evapotranspiration as a flux. However, current methods indirectly estimate RZSM using data assimilation, which requires time-series data to make model-based predictions. This is because direct measurement requires a lower frequency signal, typically P-band and below (<500MHz), to reach root zone depths and, in turn, necessitates a larger antenna to be deployed in space, which is often unfeasible. A new remote sensing technique known as Signals of Opportunity (SoOp) reutilizes transmitted communication signals to perform microwave remote sensing. This means that SoOp platforms need not include a transmitter, but rather rely on passive radar technology to make measurements. This thesis details the development of a UAS-based P-band SoOp receiver instrument. This platform will be used to progress the state-of-art in techniques for direct measurement of RZSM.

Measurements of root zone soil moisture play large roles in our understanding of the water cycle, weather, climate, land-heat exchanges, drought forecasting, and agriculture. Current measurements are made using a combination of ground-based sampling and active and passive microwave remote sensing. Signals of Opportunity (SoOp) has emerged as a promising method for sensing soil moisture, using satellite communication signals to make bi-static reflectometry measurements. The current combination of ground and satellite-based measurements for soil moisture results in a gap of useful spatial and temporal resolutions, as well as limited soil penetration depth. This thesis developed and constructed an Unmanned Aerial Vehicle (UAV) mountable, I-band SoOp instrument with calibration capabilities, along with supporting specular point mapping and mission planning software. This work advances the creation of a compact, mobile, root zone soil moisture (RZSM) remote sensing system.

<p>Multiple Signals of Opportunity (multi-SoOp) across different frequencies and polarizations</p> <p>offer a potential breakthrough for remote sensing of root-zone soil moisture (RZSM). Deeper penetration depths of existing communication transmissions in the frequency ranges of 137–138, 240–270, and 360–380 MHz enable the estimation of RZSM by complementing global navigation satellite system reflectometry (GNSS-R) in L-band. The small form factor of the multi-SoOp observatory allows for high spatiotemporal coverage of RSZM by a satellite constellation in a cost-effective manner. This study aims to develop models and tools to define mission requirements for various system parameters that affect observation accuracy and coverage, for the advancement of spaceborne multi-SoOp remote sensing. These parameters include frequency and polarization combinations, observation error, inter-frequency temporal coincidence, and configuration of the satellite constellation. We present the development of a retrieval algorithm and the sensitivity analysis of retrieval accuracy. The retrieval algorithm was evaluated using synthetic observations generated from multiyear time series of in-situ soil moisture (SM) and satellite-based vegetation data. The combined use of both high and low frequencies improves retrieval accuracy by limiting uncertainties from vegetation and surface SM and providing sensitivity to deeper layers. A bivariate model, derived from the sensitivity analysis, facilitates error prediction for future science missions. We introduce a framework for tradespace exploration of the multi-SoOp satellite constellation. A constellation design study indicates that a Walker constellation comprising 24 satellites with 3 orbital planes at 500 km and 50° inclination optimizes the coverage and mission cost under mission requirements. A tower-based field experiment validated the performance of a prototype antenna for multi-SoOp using the interference pattern technique. More field experiments with improved instruments are required to further advance the multi-SoOp technique.</p>

In modern combat aircraft, the most common localization method integrates a Global Navigation Satellite System (GNSS) with an Inertial Navigation System (INS). Although GNSS is the optimal choice for navigation, there are situations when the GNSS satellite signal is unavailable. This can happen due to various reasons such as jamming, physical obstacles, or technical failures. An alternative method to GNSS is utilizing Signals of Opportunity (SOP), which leverages signals not intended for navigation, such as those from cellular towers. These signals are transmitted from non-controllable sources, and challenges may arise due to the lack of guarantee regarding their quality and availability. Therefore, it is crucial that any estimation method utilizing SOP is robust to ensure accurate aircraft localization. This thesis investigates three different localization approaches to address this challenge. This study explores SOP sources with both known and unknown positions. For known signal source positions, an Extended Kalman Filter (EKF) based solution is utilized as a baseline to evaluate how well unknown signal sources can be used to estimate the aircraft's location. To address the challenge of unknown signal source positions, an EKF combined with a Simultaneous Localization and Mapping (SLAM) method, referred to as EKF SLAM, is used. In this case, the sources are introduced through two different approaches. The first approach, undelayed initialization, introduces the signal source directly when observed. The second approach, delayed initialization, involves inverse depth parameterization (IDP) and preprocessing of the signal source position before fully introducing it into the aircraft system. While both approaches outperform an unassisted INS approach, they do not achieve the same level of performance as when the source positions are known. Moreover, various factors, including the aircraft's trajectory, measurement noise, measurement frequency, and the initial covariance of new landmarks, influence the performance of the EKF SLAM approaches. Additionally, delayed initialization is strongly influenced by a threshold assessing landmark position estimate linearity, underscoring its sensitivity to accuracy. The concept behind delayed initialization aims to reduce the error of the signal source position before it is introduced to the system. This method has been proven to significantly reduce the signal source position error. However, its robustness is influenced by several factors, including the parallax angle, sudden changes in the aircraft's direction, and particularly the initial covariance of a landmark estimate. The accuracy of the aircraft's position is crucial, resulting in a trade-off between preprocessing and rapidly initializing a signal source position to the aircraft system. In contrast, undelayed initialization is less sensitive to trajectory changes, even though it introduces the signal sources with greater initial error. There is a significant difference in computational time when comparing known and unknown sources. As the number of sources increases, the computational time for unknown sources is more affected than for known sources. The delayed source initialization method increases computational time due to its preprocessing, especially as more sources are used. Conversely, initializing sources directly reduces the computational time, as no preprocessing is required. / I moderna stridsflygplan är den vanligaste lokaliseringsmetoden att integrera ett Global Navigation Satellite System (GNSS) med ett Inertial Navigation System (INS). Även om GNSS är det optimala valet för navigation finns det situationer när GNSS-satellitsignalen inte är tillgänglig. Detta kan inträffa på grund av olika orsaker som störningar, fysiska hinder eller tekniska fel. En alternativ metod till GNSS är att använda Signals of Opportunity (SOP), som utnyttjar signaler som inte är avsedda för navigation, till exempel de från mobilmaster. Dessa signaler kommer från okontrollerbara källor, vilket kan medföra utmaningar på grund av att deras kvalitet och tillgänglighet inte kan garanteras. Därför är det viktigt att varje lokaliseringsmetod som använder SOP är robust för att säkerställa en bra och korrekt flygplans positionering. Detta examensarbete undersöker tre olika lokaliseringsmetoder för att hantera denna utmaning. Denna studie utforskar SOP-källor med både kända och okända positioner. För kända positioner används en lösning baserad på ett Extended Kalman Filter (EKF) som en baslinje för att utvärdera hur väl okända signalkällor kan användas för att uppskatta flygplanets position. För att hantera utmaningen med okända signalkällors positioner används ett EKF kombinerad med en metod vid namn Simultaneous Localization and Mapping (SLAM), även kallad EKF SLAM. I detta fall introduceras källorna genom två olika tillvägagångssätt. Det första tillvägagångssättet, ofördröjd initialisering, introducerar signalkällan direkt när den observeras. Det andra tillvägagångssättet, fördröjd initialisering, involverar inverse depth parameterization (IDP) och förbearbetning av signalkällans position innan den introduceras i flygplanets lokaliseringssystem. Även om båda tillvägagångssätten presterar bättre än en oassisterad INS-metod uppnår de inte samma prestandanivå som när källornas position är kända. Dessutom påverkar olika faktorer prestandan hos EKF SLAM-metoderna, vilka främst är flygplanets flygbana, mätbrus, mätfrekvens och den initiala kovariansen av nya landmärken. Dessutom påverkas fördröjd initialisering starkt av en tröskel som bedömer linjäritet hos landmärkes positionen, vilket understryker dess känslighet för noggrannhet. Konceptet bakom fördröjd initialisering syftar till att minska felet i signalkällans position innan den introduceras i lokaliseringssystemet. Denna metod har visat sig kunna minska felet i signalkällans position avsevärt. Emellertid påverkas dess robusthet av flera faktorer, inklusive parallaxvinkeln, plötsliga förändringar i flygplanets riktning och särskilt den initiala kovariansen av uppskattningen av ett landmärkes position. Noggrannheten i flygplanets position är avgörande, vilket resulterar i en avvägning mellan förbearbetning och snabb initialisering av en signalkällas position till flygplanets lokaliseringssystem. Till skillnad från fördröjd initialisering är ofördröjd initialisering mindre känslig för förändringar i flygbanan, även om den introducerar signalkällorna med större initialt fel. Det finns en anmärkningsvärd skillnad i beräkningstid när man jämför kända och okända källors. När antalet källor ökar påverkas beräkningstiden för okända källor mer än för kända källor. Den fördröjda källinitialiseringsmetoden ökar beräkningstiden på grund av dess förbearbetning, särskilt när många källor används. Däremot minskar beräkningstiden när källor initialiseras direkt, eftersom ingen förbearbetning krävs.