Novel Neuron-like Procedure of Weak Signal Detection against the Non-Stationary Noise Background with Application to Underwater Sound

The well-known method of detecting a useful signal in the presence of noise during underwater remote sensing, based on the matched filtering of the received signal with the test signal, provides the maximum signal-to-noise ratio (SNR) at the receiver output. To do this, a correlation-type criterion function (CF) is constructed for the received and test signals. In the case of large volumes of processed data, this method requires the use of large computing resources. The search for a data processing method with lower computational costs, as well as the effective application of artificial neural networks to array signal processing, motivates the authors to propose an alternative approach to the CF construction based on the McCulloch–Pitts neuron model. Such a neuron-like CF is based on a specific nonlinear transformation of the input and test signals and uses only logical operations, which require much less computational resources. The ratio of the output signal amplitude to the input noise level is indeed the maximum with matched filtering. Studies have shown that it is not this parameter that should be considered, but statistical characteristics, on the basis of which the thresholds for detecting a signal in the presence of noise are determined. Such characteristics include the probability density distributions of correlation and neuron-like CFs in the presence and absence of noise. In this case, the signal detection thresholds will be lower for the neuron-like CF than for the conventional correlation CF. The aim of this research is to increase the accuracy of the selection of a useful signal against the intense noise background when using a processor based on the neuron-like CF and to determine the conditions when the input SNR, at which signal detection is possible, is lower compared to the correlation CF. The comparative results of stochastic modeling show the effectiveness of using a new neuron-like approach to reduce the detection threshold when a chirp signal is received against a background of unsteady Gaussian noise. The advantages of the neuron-like method become significant when the statistical distribution of the additive noise does not change, but its variance increases or decreases. In order to confirm the presence of non-stationarity in real noises, experimental data obtained from the remote sounding of bottom sediments in the Black Sea are presented. The results obtained are considered to be applicable in a wide range of practical situations related to remote sensing in non-stationary environments, long-range sonar and sea bottom exploration.