Real-Time Detection of Electric Field Signal of a Moving Object Using Adjustable Frequency Bands and Statistical Discriminant for Underwater Defense

The electric field method is robust in detecting underwater moving target objects in acoustically noisy conditions, such as a shallow water environment. However, it requires an enhanced signal processing algorithm to cope with the time-variant sea noise which distorts the target response and decreases data quality and detection performance. We propose a novel signal processing algorithm with superior performance to solve this problem. The proposed signal processing algorithm decomposes a measured signal into several frequency coefficients. It then extracts the target response using the coefficients in a frequency range chosen by a real-time statistical discriminant depending on the noise. We verify the effectiveness of the proposed signal processing algorithm through a field experiment, which can be a challenging task for conventional algorithms. We expect that this study will contribute to several fields of anomaly detection, especially in offshore defense and surveillance.