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.

The performance of wearable Photoplethysmogram (PPG) sensors is highly influenced by noise. This thesis describes the methods and results of designing a filtering systemfor PPG in context of Atrial Fibrillation (AF) detection. The developed work is an adaptive filtering system combined with a robust heart rate detection mechanism for validation of the proposed method. Additionally, the heart rate estimation can potentially be used to detect AF episodes using machine learning. Research has been done regarding an optimal reference signal for the adaptive filtering structure. Accelerometer data, being commonly used as reference signal for the noise did not showgood correlationwith the motion induced artefacts in the signal. Therefore, a reference for the signal component is generated from the PPG itself, which is achieved by applying a narrow bandpass filter. Here the center frequency is determined from an autocorrelation of the signal in a sliding-window. The optimal settings for the sliding window in AF context were found to be 2 seconds with 80% overlap. Furthermore, a comparison is made between NLMS, RLS and Kalman adaptive algorithms, in which RLS showed the best overall performance. The validation of the filtering structure is based on peak detection from the enhanced signal compared with the ECG reference peaks. The results indicates that the system significantly improves the heart rate error in signal disturbed by noise and during AF episodes.