Radar Adaptive Scan Correlation for Clutter Suppression
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Solution Overview
Problem
Conventional radar devices performing scan correlation processing face limitations in effectively handling objects with different behaviors within the same range, leading to suboptimal results due to fixed processing modes that either suppress sea clutter poorly or fail to recognize moving objects, requiring operator intervention to adjust settings as environmental conditions change.
Innovation Solution
A radar apparatus that includes a behavior data generation means to detect and store changes in sensed image data, allowing for adaptive filtering and selection processes based on presence and instability ratios, enabling optimal scan correlation processing by recognizing characteristics such as sea clutter, fixed, or moving objects, and adjusting coefficients accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If fixed scan correlation processing modes are used, then sea clutter suppression is improved, but moving object recognition deteriorates
Solution Approach 1:
The patent implements dynamic processing mode selection by automatically detecting object behavior characteristics (movement, stability) and adapting the scan correlation processing mode accordingly. The system transitions from fixed processing modes to dynamic adaptation based on real-time environmental conditions, allowing optimal suppression of sea clutter while preserving moving object detection capability
Solution Approach 2:
The system changes processing parameters (filtering strength, correlation coefficients) based on detected object characteristics. By analyzing behavior data and determining appropriate processing modes, the patent adjusts parameters dynamically to resolve the contradiction between clutter suppression and object recognition
2Adaptability or versatility
If operator intervention is required to adjust settings, then processing adaptability is improved, but operational complexity increases
Solution Approach 1:
The patent implements self-service through automatic behavior detection and processing mode determination. The system autonomously analyzes sensed image data, detects object characteristics, and selects appropriate processing modes without operator intervention, thereby maintaining high adaptability while eliminating the need for manual setting adjustments
Solution Approach 2:
The system uses feedback loops where behavior detection results inform processing mode selection. By continuously monitoring object characteristics and adjusting processing accordingly, the patent achieves automatic adaptation that eliminates operational complexity while maintaining versatility
3Device complexity
If uniform processing is applied to entire range, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent applies local quality by performing behavior detection and processing mode determination for different regions independently. Instead of uniform processing, the system analyzes local object characteristics and applies appropriate processing modes to specific areas, thereby improving recognition accuracy without significantly increasing overall system complexity
Data Source
AI summary
A radar device is provided that can reliably display sensed image data of an object, regardless of the state of the object (echo) within a sensed range and of the surrounding environment. A behavior data detector 11 generates current level detection data by detecting a level behavior of sensed image data X(n) handled by a W data generator 7 from sensed data x(n) that is output from a sweep memory 4. Previous behavior data constituted by level detection data of several past scans is stored in a behavior data memory 12, and the behavior detector 11 updates the previous behavior data with the current level detection data and outputs the result to the W data generator 7. Detecting characteristics of the sensed image data of corresponding pixels from the behavior data, the W data generator 7 selects filter calculation data W(n), the current sensed image data X(n) or specific filter calculation data Z(n) and outputs the selected result as written image data Y(n).