Small-Signal Sonar Filtering for Noise and Sidelobe Control
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Solution Overview
Problem
Sonar systems face challenges due to absorption and spreading losses that result in system noise, compromising detection accuracy, particularly in multibeam systems where sidelobes and noise distribution patterns hinder target detection and introduce false positives, distorting signals near detection limits.
Innovation Solution
A sonar device with reception and processing circuitry that includes a target detector, first and second noise filters, and a selector, using different thresholds for noise filtering and a binary mask to enhance target detection by preserving genuine signals while reducing sidelobes and noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If noise filtering is applied to reduce system noise and sidelobes, then detection accuracy improves, but genuine weak target signals may be distorted or lost
Solution Approach 1:
The patent divides the detection space into multiple sectors and applies different noise filtering thresholds to each sector. Weak target signals in sectors with low sidelobe levels are preserved with lower thresholds, while stronger filtering is applied in sectors with high sidelobe levels, thus resolving the contradiction between noise reduction and signal preservation
Solution Approach 2:
Different filtering characteristics are applied to different spatial regions (sectors) based on their local noise and sidelobe characteristics. This allows optimal filtering performance in each local region without compromising genuine target signals, addressing the contradiction between filtering effectiveness and signal preservation
2Measurement precision
If detection threshold is lowered to identify smaller objects, then detection sensitivity improves, but false-positive noise targets increase
Solution Approach 1:
The detection space is segmented into multiple sectors, each with independently optimized detection thresholds. This allows lower thresholds (higher sensitivity) in clean sectors while maintaining higher thresholds (lower false-positive rate) in noisy sectors, resolving the contradiction between sensitivity and reliability
Solution Approach 2:
The patent dynamically adjusts detection thresholds based on local noise characteristics and sidelobe levels in different sectors. By changing the threshold parameter adaptively rather than using a fixed global threshold, the system achieves high sensitivity where appropriate while maintaining reliability where needed
3Object-generated harmful factors
If aggressive noise filtering is applied to eliminate sidelobes, then sidelobe suppression improves, but target signals near detection limits are distorted
Solution Approach 1:
The patent applies different filtering strengths to different sectors based on their sidelobe characteristics. Sectors with high sidelobe levels receive aggressive filtering, while sectors with low sidelobe levels use gentle filtering to preserve signal fidelity, thus resolving the contradiction between sidelobe suppression and signal preservation
Solution Approach 2:
The filtering operation is localized to specific sectors with different characteristics. Each sector receives filtering treatment appropriate to its local sidelobe environment, achieving effective sidelobe suppression where needed while maintaining signal fidelity where sidelobes are not problematic
Data Source
AI summary
A sonar device including reception circuitry and processing circuitry is disclosed. The reception circuitry acquires sonar data from one or more targets in an underwater environment. The processing circuitry includes a target detector, a first noise filter, a second noise filter, and a selector. The target detector generates a binary mask that indicates presence or absence of target based on the sonar data. The first and second noise filters receive the sonar data, perform first and second noise filtering on the sonar data to reduce noise in the sonar data respectively, and output first and second filtered sonar data respectively. The first noise filtering is based on a first threshold. The second noise filtering is based on a second threshold different from the first threshold. The selector selects one of the first filtered sonar data and the second filtered sonar data as output based on the binary mask.