Sonar Target Parameter Reliability via Discrete Track Segmentation
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Solution Overview
Problem
Existing sonar systems lack the ability to assess the reliability of calculated target parameters such as range, course, and velocity, leading to uncertainty in determining whether the best solution is correct, especially in scenarios where target movement or sound propagation conditions change.
Innovation Solution
The method calculates target tracks for multiple possible solutions, determining a quality measure for each, which provides a reliability indicator for the optimized solution by analyzing the distribution of these measures, allowing for a reliability degree to be derived and visualized, thereby assessing the trustworthiness of the calculated target parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple possible solutions are calculated with quality measures, then reliability assessment of target parameters is improved, but device complexity and computation time increase
Solution Approach 1:
The solution space is segmented into multiple discrete target tracks, each representing a possible target trajectory. By dividing the continuous parameter space into discrete segments (target tracks with different courses, ranges, and velocities), the system can evaluate reliability across multiple hypotheses without requiring exhaustive continuous analysis. This segmentation enables the calculation of quality measures for distinct scenarios while managing computational complexity through structured discretization.
2Reliability
If multiple possible solutions are calculated with quality measures, then reliability assessment of target parameters is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-defining a finite set of discrete target tracks based on measured bearing angles and assumed target parameters before full evaluation. These pre-segmented tracks serve as candidate solutions that narrow down the search space. By preparing this segmented framework in advance, the system reduces the computational burden during real-time processing, allowing reliability assessment across multiple solutions without proportional increases in processing time.
3Ease of operation
If only one optimized solution is output, then ease of operation is improved, but information about solution reliability is lost
Solution Approach 1:
The quality measure serves as an intermediary indicator that bridges the gap between the optimized solution and its reliability. Instead of presenting raw computational data or requiring operators to interpret complex solution sets, the system introduces quality measures as intermediate metrics that quantify the reliability of each target track. This intermediary layer maintains operational simplicity while conveying essential reliability information, allowing operators to assess confidence in the optimized solution without being overwhelmed by raw data.
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 approach enables the operator to estimate the reliability of the optimized solution, providing a graphical representation of the solution area that includes all possible solutions, thereby quickly determining the probability and reliability of the target parameters, even in uncertain conditions.
Implementation Method 1
sound waves of target noise emitted from this target are conventionally received by means of a sonar receiving installation
Data Source
AI summary
A method and an apparatus for passive determination of target parameters by directionally selective reception of sound waves emitted or transmitted from a target, by an arrangement (24) of underwater sound sensors of a sonar receiving installation from estimated bearing angles determined from estimated positions of the target, and bearing angles measured at the measurement point by the arrangement (24). A bearing angle difference between measured and estimated bearing angles is iteratively minimized and, when the minimum is reached, the target parameters are used for an optimized solution for outputting target position, course, range and/or velocity and they are updated during each processing cycle in a series of successive processing cycles. To estimate the reliability of this optimized solution, during each processing cycle a multiplicity of different target tracks Z(i,j) are calculated from possible solutions for the target parameters to be determined, specifically an assumed target course Cest, an assumed target range Rest and/or an assumed target velocity Vest. Associated bearing angles Best are assumed for each of the possible solutions, and the assumed bearing angles Best are used to calculate a quality measure.