Underwater Target Detection via Spatio-Temporal Frequency Slicing
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Solution Overview
Problem
Current methods for underwater target detection in low signal-to-noise ratio (SNR) environments struggle to accurately integrate signals due to unknown target movement parameters, limiting the effectiveness of multi-frame accumulation techniques.
Innovation Solution
A method for detecting moving targets based on spatial slices of transformed spatio-temporal frequency space, involving signal segmentation, discrete Fourier transform, frequency domain beamforming, coordinate transformation, and segmented Radon transform to enhance detection performance in low SNR conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-frame data accumulation is used to improve SNR, then detection performance is improved, but target movement causes signal parameter changes that prevent accurate integration
Solution Approach 1:
The patent performs preliminary estimation of target movement parameters (azimuth rate and frequency rate) before conducting multi-frame accumulation. By predicting how signal parameters will change due to target motion and pre-compensating for these changes, the method enables accurate signal integration despite target movement, thus resolving the contradiction between improving detection performance through accumulation and maintaining integration accuracy.
2Measurement precision
If tracking algorithm is applied before detection to handle target movement, then signal integration accuracy is improved, but system complexity increases
Solution Approach 1:
The patent transforms the complex tracking-before-detection problem into a parameter estimation and compensation task. By focusing on estimating only two key parameters (azimuth rate and frequency rate) and applying analytical compensation formulas, the method achieves accurate signal integration with significantly reduced computational complexity compared to full tracking algorithms, thus resolving the contradiction between integration accuracy and system complexity.
3Reliability
If long time integration is performed to improve SNR, then detection performance in low SNR environment is improved, but target movement parameters change making accurate integration impossible
Solution Approach 1:
The patent performs preliminary estimation of target movement parameters and applies pre-compensation before long-time integration. This allows the system to maintain accurate signal integration over extended periods even as target movement parameters change, enabling long-time integration to improve SNR without losing integration accuracy, thus resolving the contradiction between detection performance and integration time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The proposed method improves detection performance by effectively handling target movement in low SNR environments, enabling accurate detection of targets with rapid azimuth changes and maintaining computational efficiency.
Implementation Method 1
performing N-point discrete Fourier transform (DFT) on the received signal on each array element
Implementation Method 2
performing segmented Radon transform on the spatial slice obtained in step 5 to detect the target
Data Source
AI summary
The present disclosure belongs to the field of underwater target detection, and in particular, to a method for detecting a moving target based on spatial slices of transformed spatio-temporal frequency space. The method includes: segmenting a target radiated acoustic signal received by an M-element horizontal line array in an underwater acoustic environment with a low signal-to-noise ratio (SNR); performing N-point discrete Fourier transform (DFT) on the received signal on each array element in each period of time; performing frequency domain beamforming on an array signal after each section of DFT, and performing stacking after compensating a phase difference between arrays brought by an azimuth of each primitive element; performing coordinate transformation on a frequency-azimuth-time three-dimensional (3D) matrix space obtained; taking a slice from the obtained frequency-azimuth-time 3D space subjected to the coordinate transformation; and performing segmented Radon transform on the spatial slice obtained to detect the target.