Print Email Facebook Twitter Coherent integration for imaging and detection using active sonar Title Coherent integration for imaging and detection using active sonar Author Demir, Kaan (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Leus, G.J.T. (mentor) Kovalenko, Vsevolod (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Circuits and Systems Date 2023-06-28 Abstract Existing sonar systems typically rely on a minimum signal strength of a single echo, which limits their performance in low signal-to-noise conditions. This thesis explores the concept of coherent integration for active sonar, with the aim of improving imaging and detection capabilities under low signal-to-noise conditions. The goal is to provide signal processing methods that achieve long-time coherent integration of the received echoes, thereby maximising the processing gain. Additionally, this research explores waveform design by comparing the performance of pseudo-random noise with chirps. Two applications are seen in this thesis: moving target detection, which involves static sonar sensors, and synthetic aperture imaging, where the sensors move while the imaging scene remains static. For moving target detection, a processing methods is proposed which achieves coherent integration for constant velocity targets in a computationally efficient manner, and improves the detection performance by implementing a clutter filtering stage. For the second application, a processing method for imaging from a moving sensor pair is proposed. The resulting point-spread function for a circular sensor trajectory is investigated, from which a set of design rules are established. Additionally, a least squares algorithm is applied, which shows that the resulting image can be improved in terms of resolution and sidelobe interference. Finally, the imaging and detection methods are tested and verified using an in-air demonstrator. Subject Signal ProcessingSonar To reference this document use: http://resolver.tudelft.nl/uuid:eaa5818c-6b0f-4380-9055-9fbf794576bf Embargo date 2023-12-28 Part of collection Student theses Document type master thesis Rights © 2023 Kaan Demir Files PDF Thesis_kd.pdf 19.27 MB Close viewer /islandora/object/uuid:eaa5818c-6b0f-4380-9055-9fbf794576bf/datastream/OBJ/view