Sonar Target Simulator Using Real-World Acoustic Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current sonar targeting simulators fail to realistically simulate real-world sea conditions, making it easy for operators to distinguish between synthetic and actual data, thereby reducing the training value.
Innovation Solution
A sonar target simulator (STS) that uses real-world collected data to generate target signatures and environmental models, combining them to create realistic simulated scenarios within a gaming area, incorporating background signatures and motion analysis to enhance training accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If modeled background noise data is used to simulate sea conditions, then the simulator can operate with simplified data, but the realism of the simulated environment deteriorates making it easy for operators to identify fake noise
Solution Approach 1:
The patent applies the copying principle by recording actual sea environment data including real background noise, target signatures, and clutter characteristics during real ocean operations. These recorded real-world copies are then stored in databases and reused to generate simulated scenarios, ensuring the training environment accurately replicates actual operational conditions rather than using simplified models
Solution Approach 2:
The patent implements preliminary action by conducting real-world data collection campaigns before the simulator is deployed for training. During these preliminary operations, actual sea state parameters, acoustic signatures, and environmental conditions are recorded and processed into training databases in advance, so that when training scenarios are generated, they already contain authentic environmental characteristics
2Device complexity
If predetermined simulations with fixed target locations are used, then the simulator setup is simplified, but the training value deteriorates as operators can memorize target positions
Solution Approach 1:
The patent applies dynamics by implementing random target placement algorithms that generate variable target positions, velocities, and trajectories for each training scenario. The simulator dynamically creates new simulation parameters including target motion profiles and environmental conditions, ensuring that no two training sessions have identical configurations, thus preventing memorization while maintaining manageable system complexity
Solution Approach 2:
The patent implements parameter changes by systematically varying multiple simulation parameters including target depth, speed, bearing, environmental temperature, salinity, and background noise levels. These parameter variations are controlled through configurable ranges and distributions, allowing the simulator to generate diverse training scenarios without requiring complex reconfiguration of the underlying system architecture
3Ease of manufacture
If narrowband techniques are used to simulate target signatures, then the simulation approach is simple and widely utilized, but the realism deteriorates as simulated signals appear obvious compared to real-world data
Solution Approach 1:
The patent applies copying by recording actual acoustic signatures of targets (such as submarines, surface ships, and underwater vehicles) during real-world operations. These recorded signatures including broadband noise characteristics, modulation patterns, and frequency content are stored in databases and applied to simulated targets, replacing simplified narrowband models with authentic acoustic fingerprints
Solution Approach 2:
The patent implements composite materials by combining multiple recorded acoustic signature components including propeller noise, machinery vibrations, cavitation effects, and flow noise into composite target signatures. These composite signatures replicate the complex acoustic characteristics of real vehicles better than single-frequency narrowband signals, while still using computationally efficient recorded data rather than complex physical models
Data Source
AI summary
A sonar target simulator (“STS”) for training a sonar operator is disclosed. The STS is configured to create a plurality of simulated scenarios within a gaming area having a plurality of environments. The STS includes one or more memory units storing real-world collected data, one or more processing units, and a computer-readable medium. The real-world collected data includes background signatures related to the plurality of simulated environments. The computer-readable medium has encoded thereon computer-executable instructions to cause the one or more processing units to generate a target signature from real-world collected data, generate an environmental model from the real-world data, and combine the target signature with the environmental model to create a simulated scenario, of the plurality of simulated scenarios, for use in the gaming area. In this example, the environmental model corresponds to an environment of the plurality of environments.