Vector analysis of unmanned aircraft sea surface imaging characterization based on ISAR

Utilization of airborne Inverse-Synthetic-Aperture-Radar (ISAR) for detection of moving targets on the sea surface is studied in this paper. In order to systematically analyze the characterization of radar imaging in the presence of both motion and observation uncertainty of a target, this study incorporates the Bayes-PRM multi-query algorithm for fusing ISAR multi-sensing information. The algorithm isochronously samples the physical quantities, such as UAV position, altitude, pitch angle, and velocity as a sequence of multivariate groups, and converts the time-series data of the trajectories into distributional features in graph theory. The coupled edge weights and Alternating Direction Method of Multipliers (ADMM) are introduced through the coupling framework combined with the ambient graph. With ADMM, the quadratic penalty term is used to achieve a simple linear function, and subproblems involving amplitude, linear velocity, and yaw angle can be embedded in a sequential solution scheme. The quality of the primal and dual solutions is then improved in an iterative manner to achieve vector analysis of the UAV. The potential maneuvering region of the target is fitted to a Gaussian-Wiener stochastic movement model, which in turn yields the detection expectation through point set coverage. By analyzing the tracking simulation results and diffraction theory, the experimental results are transformed into a function of the UAV multi-vector, and the angular linearization model of the multi-vector under the radar image is developed, which solves the optimal elevation angle and the optimal path for the maximum scanning radius of the airborne radar. The nominal trajectory of the UAV is effectively obtained, which confirms the improvement of the credibility of ISAR in target detecting. Through the proposed model, the results of object detection accuracy were improved.