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For unmanned aircraft systems to gain full access to the National Airspace System (NAS), they must have the capability to detect and avoid other aircraft. This research focuses on the development of a detect-and-avoid (DAA) system for small unmanned aircraft systems. To safely avoid another aircraft, an unmanned aircraft must detect the intruder aircraft with ample time and distance. Two analytical methods for finding the minimum detection range needed are described. The first method, time-based geometric velocity vectors (TGVV), includes the bank-angle dynamics of the ownship while the second, geometric velocity vectors (GVV), assumes an instantaneous bank-angle maneuver. The solution using the first method must be found numerically, while the second has a closed-form analytical solution. These methods are compared to two existing methods. Results show the time-based geometric velocity vectors approach is precise, and the geometric velocity vectors approach is a good approximation under many conditions. The DAA problem requires the use of a robust target detection and tracking algorithm for tracking multiple maneuvering aircraft in the presence of noisy, cluttered, and missed measurements. Additionally these algorithms needs to be able to detect overtaking intruders, which has been resolved by using multiple radar sensors around the aircraft. To achieve these goals the formulation of a nonlinear extension to R-RANSAC has been performed, known as extended recursive-RANSAC (ER-RANSAC). The primary modifications needed for this ER-RANSAC implementation include the use of an EKF, nonlinear inlier functions, and the Gauss-Newton method for model hypothesis and generation. A fully functional DAA system includes target detection and tracking, collision detection, and collision avoidance. In this research we demonstrate the integration of each of the DAA-system subcomponents into fully functional simulation and hardware implementations using a ground-based radar setup. This integration resulted in various modifications of the radar DSP, collision detection, and collision avoidance algorithms, to improve the performance of the fully integrated DAA system. Using these subcomponents we present flight results of a complete ground-based radar DAA system, using actual radar hardware.

在無線感測器網路中，由於感測器電池的不可替換性，有效的能源管理是一項重要的研究議題。既然通訊及偵測都會消耗感測器的能量，減少多餘偵測範圍的重疊，及降低重覆資料(duplicate data)的影響，可有效節省能量，延長網路生命週期。於本研究中，我們提出VERA (Voronoi dEtection Range Adjustment)，利用分散式Voronoi diagram演算法劃分各感測器負責監控的區域，並利用基因演算法計算每個感測器最合適的偵測範圍以節省能量，延長網路生命週期。此外，我們亦考慮偵測能力的限制，在減少感測器偵測範圍重疊的同時，也避免某些區域的偵測能力低於門檻值。在實驗模擬的部份，我們利用模擬系統驗證所提出的方法是否能有效降低各感測器偵測範圍的重疊性，並因偵測範圍降低而導致duplicate data的減少和整個感測器網路總能量耗損的減少。末了，也將驗證本方法是否能延長無線感測器網路的生命週期和達到滿足偵測機率的最低保證。 / In the wireless sensor networks, the batteries are not replaceable, efficient power management thus becomes an important research issue. Since both communication and detection consume energy, if we can largely decrease the overlaps among detection ranges and reduce the duplicate data then we can save the energy effectively. This will thus prolong the network lifetime. In this research, we propose a Voronoi dEtection Range Adjustment (VERA) method that utilizes distributed Voronoi diagram to delimit the responsible area for each sensor, and utilize Genetic Algorithm to compute the most suitable detection range for each sensor. As we try to decrease the detection ranges, we still guarantee to meet the lower bound of the sensor detection probability. Simulations showed that our method can decrease the redundant overlaps among detection ranges, minimize energy consumption, and prolong the lifetime of the whole network effectively.

There are large gaps in our understanding how fish populations are affected by the anthropogenic noise and the alteration of habitat caused by the construction and operation of offshore wind farms. These issues are of great importance as the construction of offshore wind farms will increase all over the world in the near future. This thesis studies these effects with a focus on fish. The wind turbine foundations function as artificial reefs and are colonized by invertebrates, algae and fish. The epibenthic assemblages are influenced by factors such as hydrographical parameters, time of submergence, distance to natural hard bottom, material and texture (PAPER I, II). Once an epibenthic assemblage has been developed, fish utilize it for different ecosystem services such as food, shelter, and spawning and nursery area. Benthic and semi-pelagic species show a stronger response to the introduced foundation than pelagic species, as it is the bottom habitat that has mainly been altered (PAPER I, II). Pelagic species could be positively affected by the increased food availability - but it takes time and the effect is local. Construction noise like pile driving creates high levels of sound pressure and acoustic particle motion in the water and seabed. This noise induces behavioural reactions in cod (Gadus morhua) and sole (Solea solea). These reactions could occur up to tens of kilometres distance from the source (PAPER III). During power production, the wind turbines generate a broadband noise with a few dominating tones (PAPER IV, V), which are detectable by sound pressure sensitive fish at a distance of several kilometres even though intense shipping occurs in the area. Motion sensitive species will only detect the turbine noise at around a ten meter distance. Sound levels are only high enough to possibly cause a behavioural reaction within meters from a turbine (PAPER IV, V). / At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3, 4 and 5: Manuscripts.