Browsing International Telemetering Conference Proceedings, Volume 53 (2017) by Authors
HIGH-PRECISION MOTION ESTIMATION SYSTEMS FOR UUV NAVIGATIONLee, Hua; Radzicki, Vincent; UCSB, Dept Electrical & Comp. Eng. (International Foundation for Telemetering, 2017-10)This paper is the summary of a sequence of research tasks in the area of 3D bearing-angle estimation for UUV homing and docking exercises. The main focus is to simplify the concept as well as computation efficiency of the homing and docking tasks, by elevating the estimation modality from the conventional twin-receiver configuration to the 2D circular arrays. The objective is to utilize the multi-element receiver array for the entire navigation procedure, including bearing-angle estimation, optimal path planning, and high-precision docking.
NEAR-FIELD HOMING AND GUIDANCE PLANNING FOR AUTONOMOUS NAVIGATION SYSTEMSLee, Hua; Radzicki, Vincent R.; Rhajagopal, Abhejit; UCSB, Dept Electrical & Comp. Eng. (International Foundation for Telemetering, 2017-10)Advances in planning and controls algorithms for Unmanned Autonomous Vehicles (UAVs) have led to a substantial increase in a wide variety of applications. An important task for UAVs is au-tomated high-precision homing-and-docking. This requires the UAV to autonomously estimate its relative bearing to the home docking station and plan its optimal approach accordingly. This paper presents the design of homing and navigation system for UAVs that can operate in near-field scenarios. The system incorporates a dual-transmitter/receiver design and through a modified angle of arrival and motion estimation routine, the UAV can determine its relative bearing to the homing station while simultaneously planning the optimal approach. The approach planning algorithm will be described, along with theoretical analysis and simulated results documenting its performance in comparison to other techniques.
TRACKING INFORMATION IN SAR IMAGE FORMATION AND CLASSIFICATION ALGORITHMSRajagopal, Abhejit; Radzicki, Vincent; Chandrasekaran, Shivkumar; Lee, Hua; UCSB, Dept Electrical & Comp. Eng. (International Foundation for Telemetering, 2017-10)Traditional target detection pipelines involve two sequential steps: the formation of a range-profile or likely-image, and the classification of likely targets within that image. Although it has been shown that target tracking in the RaDAR image-domain can be unnecessarily noisy, with more accurate and efficient implementations involving a direct analysis of the measured wavefield, image formation remains a desirable output in many applications due to its highly descriptive and interpretable nature. In this paper, we outline a mechanism for formalizing and accelerating this procedure in application-specific use cases. Enabled by recent advances in deep learning, we present a pipeline for automatically selecting an “optimal” filtered back-projection model, forming a likelyimage, and performing target recognition and classification. The architecture allows practitioners to track and optimize the flow of information throughout the pipeline, enabling applications that utilize only intermediate outputs of the algorithm.