Coherent Cooperative Radar Networks With Split Data Evaluation
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Solution Overview
Problem
Existing coherent cooperative radar sensor networks face inefficiencies in computational load distribution and require significant computing power due to centralized data evaluation, leading to increased resource demands and potential information loss.
Innovation Solution
The method partitions sensor data into coherent and non-coherent types, distributing them to separate evaluation units based on expected computational effort, allowing parallel processing and reducing the need for centralized computing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized data evaluation is used in coherent cooperative radar sensor networks, then comprehensive target detection and analysis can be achieved, but computational load and resource demands increase significantly
Solution Approach 1:
The patent segments the sensor data into coherent and non-coherent components, which are then evaluated separately in different evaluation units. This segmentation allows the computational load to be distributed across multiple units rather than concentrated in a single centralized processor, thereby reducing the power requirement for each individual unit while maintaining comprehensive target detection capability through the combination of both evaluation paths
2Loss of information
If all sensor data are transmitted to a central evaluation unit, then complete information is available for processing, but data transmission rates and network resource demands increase
Solution Approach 1:
The patent extracts and separates coherent data from non-coherent data at the source sensors or intermediate processing units. By taking out only the necessary coherent components for centralized processing while handling non-coherent data locally, the system reduces the volume of data that needs to be transmitted across the network while ensuring that complete target information is still available through the combination of both data types evaluated in their respective units
3Power
If computational tasks are distributed across multiple evaluation units, then resource demands per unit decrease, but system complexity increases
Solution Approach 1:
The patent implements local quality by assigning specific evaluation functions to specific units: one evaluation unit is dedicated to processing coherent data while another handles non-coherent data. Each unit is optimized for its specific task, which reduces the computational power requirement per unit. The system manages complexity through clear functional differentiation and defined interfaces between units, rather than requiring every unit to handle all types of data processing
Data Source
AI summary
A method for controlling a coherent cooperative radar sensor network including a plurality of radar sensors. At least two sensors operate coherently. The sensor data are partitioned according to the type of evaluation into data to be evaluated coherently and data to be evaluated non-coherently. The data to be evaluated coherently and the data to be evaluated non-coherently are transmitted to respectively different evaluation units, which then each carry out an evaluation. The individual evaluations are combined to form an overall evaluation.