Browsing by Subject "Kalman Filter"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Evaluating the Impact of Vegetation and Future Climate Change on Groundwater Recharge using a Land-Surface Model
    (2022-01) Anurag, Harsh
    Understanding groundwater recharge is critical for accurate assessment of our valuable groundwater resources. Unfortunately, it’s also one of the most difficult fluxes of hydrological cycle to quantify because it’s influenced by several interacting factors including climate, topography, soil, land-use and vegetation. This thesis uses an integrated land-surface model to understand various factors that impact recharge. Vegetation, through evapotranspiration (ET), controls the amount of water reaching the water table and becoming recharge. Thus, changes in vegetation growth can in turn impact groundwater recharge. Currently, vegetation representation in most recharge modeling studies is specified using climatological leaf area index (LAI) values. This kind of year-to-year repeating vegetation parameterization cannot capture seasonal and inter-annual vegetation responses to dynamic meteorological conditions and can thus neglect the corresponding impact on recharge. The first part of this thesis uses Community Land Model (CLM) to investigate the sensitivity of recharge to seasonal and interannual varying vegetation in Minnesota (USA) across different climate, hydrogeology, and ecoregions. We found that although year-to-year varying vegetation does not affect long-term climatological recharge estimates, it can drive disproportionately large variability in annual and seasonal recharge. Results also show that across the precipitation gradient, vegetation leaf-out in Minnesota is highly sensitive to springtime temperature anomalies, and this phenological response can trigger notable changes in ET and subsequently recharge. Along with characterizing recharge responses to vegetation dynamics, understanding and predicting recharge under future climate conditions is also critical, as climate change is imposing additional stresses on our water resources. In the second part of this thesis, we used Minnesota as a testbed to understand how recharge will respond to changing climate in upper-latitude, low-elevation temperate settings. We compared the simulated future recharge (2026-2055) under two emissions scenarios (RCP4.5 and RCP8.5) with baseline historical conditions (1976-2005) and found that despite consistent projections of higher precipitation, state-average recharge will mostly decline or remain about the same due to warming-induced ET increases. Results also demonstrate that in addition to precipitation and temperature change, moisture feedbacks on ET and the influence of hydrogeological properties and frozen ground dynamics on runoff is essential to consider when quantifying climate change impacts on recharge in temperate zones. The final part of this thesis focuses on snowfall-induced seasonally frozen ground changes and its impact on spring recharge. We conducted simulation experiments with varying snow inputs to test the hypothesis that a smaller snowpack will allow for higher partitioning to runoff versus recharge due to greater ground frost. Results show that smaller snowpacks did lead to lower spring recharge amounts relative to precipitation compared to larger snowpacks, but not due to greater partitioning to runoff as initially hypothesized. Instead, relative recharge decreased alongside relative runoff when snowfall was less, because more of the infiltrated water was lost to ET as the surface soil ice thawed earlier and meltwater infiltrated into the root zone earlier. Overall, the findings in this thesis enhances our understanding of processes controlling groundwater recharge in upper to mid-latitude, low-relief settings such as Minnesota. As demand of groundwater continues to increase, understanding this important process by which aquifers are replenished is imperative for effective and sustainable groundwater resource management.
  • Thumbnail Image
    Item
    Position Estimation Using Magnetic Fields
    (2018-11) Madson, Ryan
    This thesis develops position estimation systems based on magnetic fields and addresses a number of challenges related to making such systems accurate and robust for real-world applications. The thesis first addresses one-dimensional position estimation using the measurement of piston position inside a cylinder as a benchmark application. The piston is equipped with a permanent magnet and one or more magnetic sensors are embedded on a compact circuit board located on top of the cylinder. Due to large distances between the moving piston and the stationary sensor, the magnetic field as a function of piston position is highly nonlinear. This magnetic field is modeled either analytically or emperically and a nonlinear estimation algorithm, namely the truncated interval unscented Kalman Filter (TIUKF), is utilized for real-time estimation of the position of the piston. Piston position estimation can be useful on hydraulic actuators, pneumatic actuators, IC engines, and a number of other cylinder piston products. The developed estimation algorithm is implemented experimentally on a microprocessor. A compact sensor board containing sensors, the microprocessor, and other components is developed. The developed position estimation system is first evaluated experimentally on pneumatic actuators. The estimation system performs well and an estimation accuracy better that 1% is achieved on pneumatic actuators with stroke lengths of 5 cm and 10 cm. Next an auto-calibration system is developed in order to enable the sensor board to estimate position accurately when installed on new cylinders. Small misalignments and offsets in location can occur on each installation. The new auto-calibration method allows the position estimation system to perform robustly and accurately by identification of new parameters on each installation. This auto-calibration is done without requiring any additional external reference position sensors. A significant challenge to magnetic field based position estimation comes from disturbances due to unexpected ferromagnetic objects coming close to the sensors. A new disturbance estimation method based on modeling the magnetic disturbance as a dipole with unknown location, magnitude, and orientation is developed. A TIUKF is used to estimate all the parameters of this unknown dipole, in addition to estimating piston position from nonlinear magnetic field models. Experimental data from a pneumatic actuator is used to verify the performance of the developed estimator. Experimental results show that the developed estimator is significantly superior to a linear magnetic field model based disturbance estimator. It can reliably estimate piston position and the unknown dipole parameters in the presence of a variety of unknown disturbances. Next the estimation system is implemented for a large hydraulics actuator used on construction machines. The ferromagnetic material of the hydraulic cylinder leads to significant hysteresis, since this material is magnetized and demagnetized repeatedly with the motion of the magnet. A method to model and compensate for the hysteresis in the system is developed. In particular, a modified Preisach model and associated estimation algorithm developed is shown to provide excellent performance. An accuracy better than 2\% is achieved on the large-stroke hydraulic cylinder in spite of significant hysteresis. Finally, the one-dimensional position estimation tools are extended in an attempt to enable 3D position estimation of a magnet. The objective is to estimate magnet position in real-time from a moving sensor board in the neighborhood of the magnet. Applications for this 3D position estimation system include a breast cancer surgery application in which a small magnet can be used to mark tumor location. The significant challenges in the 3D position estimation application are handled by using an accelerometer and gyroscope in addition to magnetic sensors for orientation estimation, by using a particle filter for the estimation task, and by using a neural network for modeling the functional relationships between magnetic field and 3D position and orientation. While the developed system provides reasonable experimental performance, further work with more sensitive magnetic sensors and a better reference 3D position sensor for modeling are needed.
  • Thumbnail Image
    Item
    Wearable Inertial Sensors for Motion Analysis in Respiration, Diet Monitoring, and Vehicular Safety Applications
    (2021-07) Johnson, Gregory
    This thesis is concerned with the development and application of motion analysis algorithms based on signals from inertial measurement units (IMUs). In particular, the application areas discussed in the thesis are respiratory monitoring, dietary monitoring, and vehicular safety. Usage of IMUs for attitude and heading estimation has a rich legacy, but it is only in recent years that they have become low-cost commodity sensors found in nearly every smart phone and smart watch, making them particularly applicable sensors for everyday applications. Despite the existence of well-established orientation estimation techniques, motion analysis using inexpensive wearable sensor applications targeted to the general population requires special attention. All three application areas discussed in this thesis require a similar approach to the estimation of motion variables in that they depend on the partial or full orientation of the device relative to the human user and/or the user’s orientation relative to earth. However, the class of mobile-phone grade IMUs utilized here offer notoriously poor accuracy compared to much more expensive aerospace-grade IMUs. Inexpensive IMUs typically suffer from bias instability, which requires careful calibration or specialized algorithms. Further, full orientation estimation traditionally relies on the IMU’s magnetometer to sense the geomagnetic field. But, the geomagnetic field is relatively weak and can often be dwarfed by magnetic fields from ferromagnetic objects routinely encountered in indoor environments. Thus, applications targeted for use by the general population must utilize algorithms that can overcome these limitations in a robust manner. The first application area addressed is respiratory monitoring. The physical motions of the thoracoabdominal wall during respiration are important in many diseases, and differentiation of normal from abnormal respiratory kinematics can be used to monitor disease state. In this application, a novel wearable device is developed that allows for long-term, out-of-clinic monitoring and analysis of respiration under the assumption of static body position (non- ambulatory). In particular, the device measures respiratory accelerations at multiple points on the thoracoabdominal surface and estimates respiratory displacements along with a variety of clinically useful metrics. After careful removal of gravity from the acceleration measurement using a multiplicative Kalman Smoother, the algorithm double integrates and high pass filters the residual signal to obtain three-dimensional respiratory displacements. The accuracy is on the order of the accuracy of a reference optical motion tracking system, and this thesis presents an analysis of the factors contributing to displacement errors. From the displacements, a variety of additional temporal, phasic, and volumetric respiratory variables may be estimated. After developing methods and discussing experimental results from a single subject for estimating respiratory displacements and subsequently several respiratory variables from these displacements, we then present the results from an initial small cohort IRB-approved study using the device. In the study, subjects wore the respiratory monitor while faced with a variety of airway occlusions. Despite the ultra-low respiratory rates encountered, the system was able to detect thoracoabdominal asynchrony with limited accuracy. Real-life medical situations involving respiratory distress are likely to present higher respiratory rates and thus higher potential for more accurate estimates. The developed system offers a combination of capabilities unmatched by existing technology in terms of its portability and the suite of respiratory variables it is able to estimate. The second application area addressed in this thesis is the development of a novel Food Intake Monitoring (FIM) device. Typical methods for dietary tracking in obesity research such as the 24-hour food intake recall are well known to be inaccurate, and there is clear need for a device to automatically detect and capture eating events as an adjunct to these existing methods. In this thesis, a wrist-worn IMU and microcontroller are utilized to detect when a person is eating (under the assumption that the food is eaten primarily with the sensor-affixed arm), optimize the capture of the food being eaten using an on-board camera, and classify the obtained image as containing food or not. The detection, image capture, and classification modules are organized in a decision tree format, an approach which minimizes system power consumption while maximizing user privacy, as opposed to having a camera always on with constant wireless data being streamed. In the first iteration of the FIM, hand proximity to the mouth is decided based on two IMUs, one on the upper arm, and one on the lower arm. In the second iteration of the device, only a single IMU is utilized, and hand proximity is determined using the IMU’s magnetometer along with a magnet worn on the body near the collar bone. Once hand-mouth proximity has been detected, it is shown that a simple linear Support Vector Machine is able to accurately classify eating activities versus other hand-near-mouth activities, such as teeth brushing and shaving. After eating is detected, the system takes an image of the food in front of the user using an on- board camera. The timing of the image capture is based on estimation of the device orientation relative to gravity using a straightforward Kalman Filter, and a method is developed that predicts optimal image capture timing using the gyroscope. Finally, it is shown that images may be classified as containing food or not using a special Convolutional Neural Network (CNN) adapted to microcontroller deployment using integer quantization. The final health and safety application considered concerns vehicular safety and phone use while driving. Distracted driving due to phone or mobile device usage is one of the primary causes of vehicular accidents, and one approach to reducing such accidents is to automatically disable devices when the user is driving. In this thesis, IMU signals on a mobile phone or smart watch are utilized to determine whether or not the user is in the driver’s seat of a moving vehicle, under the assumption that the device is in a static position inside the vehicle and close to level road grade. First, the algorithm must estimate the orientation of the device relative to the vehicle. As in the other applications, fundamental limitations of mobile-phone grade IMUs prevent estimation of orientation using traditional methods. Instead, the algorithm uses motion signals obtained during braking to determine the forward direction of the vehicle, while estimation of the gravity direction fully constrains the phone orientation. Once the orientation is determined, the pitch and roll dynamics encountered during braking and turning the vehicle are used to determine which quadrant of the vehicle the device is in relative to the vehicle’s center of gravity. Successful identification of seat position is demonstrated first in simulation and then experimentally using data taken during real-world city driving conditions.