GUI for Simulating Target Detection and Recognition
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Solution Overview
Problem
Current methods for simulating target detection and recognition are limited in their ability to simulate various scenarios, sensor types, and ATR quality without the need for actual data collection, which is costly and often conducted late in the development cycle.
Innovation Solution
A graphical user interface (GUI) is provided for simulating target detection and recognition using user-entered ground truth and collection attributes, with stochastic algorithms to simulate sensor behaviors and uncertainty attributes, allowing for the generation and display of detection data, including geolocation and object types, and the option to display false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world data collection is performed to obtain target detection data under various scenarios and conditions, then the reliability and accuracy of the data are improved, but the cost and time required increase significantly
Solution Approach 1:
The patent creates a simulation system that generates virtual copies of real-world target detection scenarios. The system models sensors, targets, and detection processes to produce synthetic data that replicates real-world conditions without requiring actual physical collection. This allows unlimited replication of scenarios including edge cases and rare events that would be time-consuming or costly to capture in reality.
Solution Approach 2:
The system performs preliminary simulation and data generation before actual field exercises are conducted. By pre-simulating various scenarios, collection conditions, and ATR quality levels, the system prepares training data in advance, eliminating the need to wait for favorable real-world conditions and reducing the overall development cycle time.
2Reliability
If real-world data collection is performed to obtain target detection data under various scenarios and conditions, then the reliability and accuracy of the data are improved, but the expense increases significantly
Solution Approach 1:
The simulation system creates virtual replicas of detection scenarios using computational models rather than physical resources. This replaces expensive field operations, aircraft, sensors, and targets with software-based simulations that can replicate any scenario without material costs, while maintaining data reliability through accurate modeling of detection physics and sensor behavior.
Solution Approach 2:
The system generates its own training data through internal simulation processes without requiring external resources such as actual targets, sensors, or field personnel. The simulation environment self-produces synthetic detection data that can be used for training and testing, eliminating dependency on expensive external data collection operations.
3Device complexity
If conventional ATR systems are used for target detection, then the system complexity is reduced, but the ability to simulate diverse scenarios and sensor types is limited
Solution Approach 1:
The simulation system is designed as a universal platform that can model multiple sensor types, detection algorithms, and scenario conditions within a single integrated framework. It provides multi-functional capability to simulate various collection conditions, ATR quality levels, and target scenarios without requiring separate specialized systems for each function, thereby achieving high versatility with controlled complexity.
Data Source
AI summary
A method is disclosed of providing a graphical user interface (GUI) for simulating target detection and recognition. The method includes providing a user entry area for receiving user entered ground truth attributes defining one or more respective simulated physical objects, and providing a user entry area for receiving user entered collection attributes defining one or more collections, each collection being a simulation of automated target recognition applied to simulated sensor output associated with simulated sensing of the one or more simulated physical objects. The method further includes simulating detection of the one or more simulated physical objects and recognition based on the ground truth attributes and the collection attributes, generating detection data about at least one detected object based on the simulated target detection, and displaying the detection data.