Target Position Estimation Pattern for Sensor Surveillance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current sensor surveillance systems require manual skill and time to redetect targets, as they lack efficient methods to optimize resource allocation and account for imperfect detection and target movements, especially in multi-target scenarios, leading to inefficient scanning efforts.
Innovation Solution
A method and system that detect and track targets, determining their characteristics and creating patterns of possible geographic positions based on predetermined parameters such as category, terrain, and surveillance levels, to accurately estimate target locations and optimize sensor resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual control methods are used to plan sensor scanning, then the system can detect targets with human expertise, but the process requires significant time and skilled operators
Solution Approach 1:
The system performs self-planning by automatically generating sensor scanning plans based on detected target characteristics and escape route predictions, eliminating the need for continuous manual intervention while maintaining expert-level detection capabilities
Solution Approach 2:
The system pre-calculates potential escape routes and predicts target positions in advance, allowing sensor scanning plans to be prepared proactively rather than reactively, reducing both planning time and improving detection efficiency
2Reliability
If manual planning is used to predict escape routes, then targets can be tracked along potential paths, but the process is time consuming and requires specialized skills
Solution Approach 1:
The system replaces manual expert analysis with automated computational algorithms that calculate escape routes and predict target positions, eliminating the need for specialized human skills while maintaining reliable tracking capabilities
Solution Approach 2:
The system continuously updates escape route predictions based on actual target movement feedback, refining its models to improve tracking reliability while maintaining automated operation without specialized human intervention
3Reliability
If sensors scan all areas to ensure target redetection, then no target is missed, but the scanning efficiency decreases and resources are wasted
Solution Approach 1:
The system concentrates sensor scanning resources in specific high-probability areas where targets are most likely to be found, rather than uniformly scanning all areas, thereby maintaining reliable redetection capability while significantly improving scanning efficiency
Solution Approach 2:
The system performs partial scanning by focusing only on the most critical areas predicted to contain targets, rather than exhaustively scanning all possible areas, achieving adequate redetection with optimized resource utilization
4Area of stationary object
If multiple sensors are deployed to cover all areas, then target detection coverage is improved, but the system complexity and resource requirements increase
Solution Approach 1:
The system dynamically assigns and reassigns sensor scanning tasks based on real-time target predictions and escape routes, allowing a smaller number of sensors to effectively cover larger areas through coordinated, adaptive movement rather than static deployment
Data Source
AI summary
The invention concerns a system (100) and a method for estimating the geographic position of a target (1). The method comprises the following steps: detecting a target (1); determining the characteristics of the target (1), which characteristics at least comprise a geographic position (3) and a category of the target; tracking the detected target (1) until at least one certain predetermined criteria is not fulfilled, wherein said criteria is associated to the level of certainty for determining the geographic position (3) of the target (1). The method further comprises determining a first point in time t1 when the predetermined criteria was last fulfilled, wherein, for a second point in time t2 the following step is performed: creating a pattern (2) defining at least one possible geographic position (3) of the target (1), said pattern (2) extends at least partially around the geographic position (3) of the target (1) at t1, wherein the dimension of said pattern (2) is determined based on at least one predetermined parameter.