Primitive Path Prediction for Adaptive Multi-Sensor Target Tracking
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Solution Overview
Problem
Existing robotic systems face challenges in efficiently navigating complex environments and identifying high-value targets (HVTs) due to limited adaptive sensor management and data fusion capabilities, leading to suboptimal situational awareness and tracking performance.
Innovation Solution
A system utilizing a Bayesian Program Learning (BPL) Reasoning Engine for adaptive sensor management and data fusion, combining multi-sensor data streams to enhance detection, tracking, classification, and identification (DTCI) of HVTs, with a sensor controller managing sensor nodes to optimize sensing parameters and a nomination interface for engagement recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor management and data fusion methods are used, then system complexity is reduced, but detection precision and tracking performance deteriorate
Solution Approach 1:
The patent segments the complex sensing system into modular functional components including sensor nodes with individual processing units, hierarchical data fusion layers (local to global), and separate primitive generation modules. This modular architecture improves detection precision through specialized processing while managing system complexity through standardized interfaces and independent operation of each segment.
Solution Approach 2:
The system implements dynamic sensor management where sensor parameters, data fusion weights, and processing resources are continuously adjusted based on real-time environmental conditions, target priorities, and system state. This dynamic adaptation enhances detection precision in varying conditions while the system only increases complexity when and where needed, rather than maintaining fixed high-complexity architecture throughout.
2Loss of information
If comprehensive multi-sensor data fusion is implemented, then situational awareness is improved, but processing time and computational load increase
Solution Approach 1:
The data fusion process is segmented into hierarchical levels: local fusion at individual sensor nodes processing raw data into basic features, intermediate fusion combining multiple sensor outputs into refined target parameters, and global fusion integrating all information into comprehensive situational awareness. This segmentation reduces processing time at each level while preserving information through progressive integration.
Solution Approach 2:
The system performs preliminary processing and feature extraction at the sensor node level before data transmission, pre-filtering and organizing raw sensor data into standardized formats. This preliminary action reduces the computational burden on central fusion systems and decreases overall processing time while maintaining complete situational awareness through distributed intelligence.
3Measurement precision
If adaptive sensor management is used, then detection performance is improved, but control complexity increases
Solution Approach 1:
Sensor nodes autonomously manage their own operation by self-adjusting sensing parameters, selecting appropriate data processing strategies, and dynamically allocating resources based on local environmental conditions and detected target priorities. This self-service capability improves detection performance through adaptive optimization while reducing overall control complexity by eliminating the need for centralized micromanagement of each sensor.
Solution Approach 2:
The system implements multi-level feedback mechanisms where sensor performance data and environmental conditions continuously inform adaptive adjustments to sensing parameters and data fusion strategies. This feedback-driven adaptation improves detection performance while the modular feedback architecture manages control complexity through localized decision-making at each system level.
Data Source
AI summary
Technology is described for a method for recognition of a target type using primitive patterns. The method can include detecting an activity signature of a target that is moving, using a sensor node. Another operation may be sampling the activity signature of the target to provide sub-samples. The sub-samples may be compared to primitives from a data store of primitives, using machine learning. In addition, the primitives are selected that are similar to the sub-samples and are joinable together to form an activity signature model. The activity signature model can be compared with activity signature templates for targets to determine the target type being captured by the sensor node.