Coherence-Based Beamforming for Sonar Target Detection
Solution Overview
Problem
Conventional beamforming techniques in sonar systems suffer from wide main beam pattern lobes, significant secondary beam levels, poor target detection in non-isotropic noise environments, and vulnerability to countermeasures, leading to false or delayed target detection.
Innovation Solution
A beamforming method that evaluates the coherence of signals received by semiarrays using discrete Fourier transform and scalar products to determine point coherence values, which are then used to form a coherence index for improved target detection, automatically normalizing the acoustic panorama and implementing Constant False Alarm Rate processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional beamforming with fixed parameters is used, then the system is simple to implement, but the main beam lobe is overly wide and secondary lobes are significant
Solution Approach 1:
The patent applies dynamics by transitioning from fixed beamforming parameters to adaptive parameters that change based on the coherence index. The beamforming weights and thresholds are dynamically adjusted according to the calculated coherence values for different spatial locations, allowing the system to adapt to varying environmental conditions and signal characteristics, thereby improving angular resolution while maintaining implementation feasibility
Solution Approach 2:
The patent implements parameter changes by modifying the beamforming weights based on the coherence index. The coherence threshold and weighting factors are adjusted as parameters to optimize the main lobe width and secondary lobe suppression. This parameter adaptation enables the system to achieve narrower main lobes and reduced secondary lobes without requiring a complete redesign of the beamforming architecture
2Object-generated harmful factors
If array window weighting is applied to reduce secondary lobes, then secondary beam levels decrease, but the main lobe width increases by 20-30%
Solution Approach 1:
The patent applies local quality by using the coherence index to apply different weighting strategies to different spatial locations and beam directions. Instead of a uniform array window, the coherence-based weights are locally optimized for each spatial location, allowing aggressive secondary lobe suppression in some directions while maintaining narrow main lobe widths in others where coherence indicates reliable signal detection
Solution Approach 2:
The patent implements feedback by using the calculated coherence index to adjust beamforming parameters in a closed-loop manner. The coherence measurement provides feedback about signal quality and spatial coherence, which is then used to adjust the beamforming weights and thresholds, creating an adaptive system that automatically optimizes the trade-off between secondary lobe suppression and main lobe narrowness based on actual environmental conditions
3Measurement precision
If the transducer array size is increased to improve beamforming performance, then angular resolution and signal/noise ratio improve, but system cost and complexity increase
Solution Approach 1:
The patent applies self-service by using the coherence index calculation to extract additional spatial information from the existing transducer array outputs. Rather than adding more transducers, the system processes the existing signals through coherence analysis to achieve enhanced angular resolution and noise rejection, allowing the array to serve multiple functions (signal detection, spatial filtering, and coherence measurement) with the same hardware
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
This method significantly narrows the main lobe, eliminates secondary lobes, enhances signal/noise ratio, and improves angular resolution, enabling more accurate and robust target detection in varying environments with fewer transducers, reducing system costs and false alarms.
Implementation Method 1
a signal from the source reaches the transducers in the array at different times and with different phases, the time delays and phase differences depending on the direction from which the signal originates
Implementation Method 2
Beamforming constructively exploits the interference phenomena produced by combining the signals received by the transducers in the array, to modify receiving directivity
Implementation Method 3
A beamforming method that evaluates the coherence of signals received by semiarrays using discrete Fourier transform and scalar products
Data Source
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Figure 3
Figure 4~5
AI summary
A beamforming method, for analysing signals received by an array of transducers (6), includes: dividing the array of transducers (6) into a number of subgroups; and defining a given number M0 of combinations of the subgroups, each of the combinations of subgroups being such as to form the array as a whole. The method also includes, for each beam being formed : aligning signals received by the transducers (6) in each subgroup in the pointing direction of the beam, and combining aligned signals of the transducers in each subgroup to supply respective resulting signals; making coherence evaluations of the respective resulting signals of the subgroups in each of the M0 combinations; and determining a beamforming level of the beam being formed, as a function of the outcome of the coherence evaluations.