Browsing by Subject "Unmanned Aircraft Systems"

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    Nursery Production Method Performance Evaluation Assessed With The Normalized Difference Vegetation Index Derived From An Unmanned Aircraft System Mounted Single-Imager Sensor
    (2020-03) Bahe, Michael
    Trees provide many benefits to urban areas including enhanced human health, pollution mitigation, and reductions in residential energy consumption. The goal of urban forest managers is to develop mature trees with large crowns to maximize these benefits. Urban trees have the highest mortality rate during the initial years post planting, known as the establishment period. In an era of planting trees to reach quotas, the looming fact is many perish during establishment limiting goal achievement. Nursery production methods (NPM) are a controllable factor in practice that may have an impact on establishment success. In this study, urban trees planted in situ from four common NPM’s (balled and burlapped, smooth plastic containers, spring planted bareroot, and gravelbed bareroot) were monitored for three years post planting using the normalized difference vegetation index (NDVI). This data was derived from high-resolution imagery collected with an unmanned aircraft system (UAS). First, the single-imager multispectral sensor selected for this project was evaluated for effectiveness in determining tree health. This was done in a controlled growth chamber environment. Results showed the single-imager sensor derived NDVI values were effective indicators of tree stress within species groups. Second, a novel technique to isolate tree crowns for spectral data analysis with UAS derived imagery was utilized to compare the health of newly planted trees in situ from the four NPM’s. Analysis of the effect NPM’s had on tree health during the establishment period showed minimal differences between the study groups thus providing evidence that each is a viable option for practitioners in urban areas.
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    Reliable Air Data Solutions For Small Unmanned Aircraft Systems
    (2020-07) Sun, Kerry
    This dissertation examines the problem of increasing Air Data System (ADS) reliability for small Unmanned Aerial Systems (UAS). A reliable ADS is required for the safe operation of aircraft; traditionally, a hardware redundant ADS design has been used. However, hardware redundancy is not feasible in small UAS since reliable ADS are expensive, and many small UAS have more stringent size, weight, and power constraints. The impracticality and limitations of hardware redundancy have motivated research in the last decade to identify alternatives to traditional ADS. In particular, estimating air data quantities, often denoted as Synthetic Air Data System (SADS), has become a viable strategy of interest. This dissertation examines the use of SADS to increase ADS reliability for small UAS. The key challenges associated with increasing ADS reliability in UAS are examined. First, calibrating low-cost air data sensors for small UAS is not well addressed in the open literature. Most existing calibration techniques do not work well with small UAS operating at low airspeeds, especially when the effects of wind cannot be ignored. This dissertation develops a method for calibrating a 5-hole probe sensor applied on small UAS using only using data from an IMU and GNSS. Second, many SADS use the aerodynamic parameters to help estimate air data quantities, but the accurate aerodynamic model is often unavailable. This dissertation proposes a model-free SADS which allows estimating angle of attack and sideslip without the need for an aerodynamic model. The performance and observability of this SADS are tested using both simulation and flight data. In addition, the problem of ADS integrity is addressed by systematically designing and analyzing the performance to ensure that it satisfies probabilistic continuity and integrity certification requirements. An ADS Fault Detection and Isolation (FDI) algorithm to detect and mitigate the effect of realistic Pitot tube failure modes is designed. The approach used is the Integrity Monitoring framework, which has been used successfully with GNSS-based precision landing systems for commercial aircraft. The FDI algorithm is validated with a flight data set in which a Pitot tube failed due to water blockage.