Radar Scan Converter Using Triangular Interpolation
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Solution Overview
Problem
Current methods for transforming radar scan images from polar coordinates to Cartesian coordinates are time-consuming and processor-intensive, leading to hardware cost increases and display distortions when handling radar scan images and geographical data in a single display plane.
Innovation Solution
The method involves dividing the image into triangular areas, determining polar coordinates for vertices, calculating attributes for each vertex, and using interpolation to transform coordinates, with the graphics card performing the interpolation to reduce processor load and enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polar coordinates are transformed to Cartesian coordinates using traditional methods for each pixel, then transformation accuracy is improved, but processing time increases and real-time display becomes difficult
Solution Approach 1:
The patent divides the image into triangular areas with vertices defined by Cartesian coordinates. By segmenting the transformation task into triangular regions, the system can pre-calculate vertex coordinates and reuse them across multiple pixels, significantly reducing per-pixel processing time while maintaining transformation accuracy within each triangle.
Solution Approach 2:
The patent performs preliminary calculation of polar coordinates for triangle vertices and stores them in advance. This pre-computation allows the main processing stage to only perform simple interpolation operations for each pixel rather than full coordinate transformations, enabling real-time display while maintaining accuracy.
2Measurement precision
If complex coordinate transformation computations are executed for each pixel, then transformation precision is improved, but processor capacity consumption increases
Solution Approach 1:
By dividing the image into triangular areas, the patent reduces the computational complexity per pixel. Each triangle's vertices are transformed once with high precision, while interior pixels use simpler interpolation formulas, reducing overall processor capacity consumption while maintaining precision at critical boundary locations.
Solution Approach 2:
The patent uses interpolation to estimate coordinates for pixels within triangles based on the pre-calculated vertex coordinates. This copying approach reuses the precise vertex transformations multiple times for adjacent pixels, reducing total computation while maintaining acceptable precision through the continuous nature of interpolation.
3Adaptability or versatility
If look-up tables are recalculated for zoom operations, then display adaptability is improved, but processor availability for other functions decreases
Solution Approach 1:
The patent enables dynamic adaptation to zoom operations by allowing the triangular segmentation and vertex coordinate calculations to be updated when display parameters change. This dynamic approach maintains display adaptability while using efficient algorithms that minimize recalculation overhead, keeping more processor capacity available for other functions.
4Measurement precision
If traditional transformation methods are used, then coordinate accuracy is improved, but hardware costs increase due to need for powerful processors
Solution Approach 1:
By segmenting the transformation into triangular regions with pre-calculated vertices, the patent reduces the computational burden on the processor. This allows standard hardware to achieve the same coordinate accuracy that would require powerful processors with traditional per-pixel transformation methods, thereby reducing hardware costs.
Solution Approach 2:
The patent replaces complex mechanical computation (full coordinate transformation for each pixel) with a simpler mathematical approach using triangular interpolation. This substitution reduces the computational power needed while maintaining coordinate accuracy, allowing implementation on less expensive hardware platforms.
Data Source
AI summary
A method transforms an image from polar coordinates into Cartesian coordinates. Therefore, the target image in Cartesian coordinates is subdivided into triangles defined by the vertices given in Cartesian coordinates. The respective polar coordinates are coded into attributes and these attributes are attached to each vertex. The attributes of any coordinates within the triangle are calculated by performing a bilinear interpolation on a graphics card and the polar coordinates are calculated from these attributes. Finally, the characteristics of the coordinates of the image in polar coordinates are transferred to the corresponding coordinates of the target image given in Cartesian coordinates. A method also consistently displays single ore multiple radar scan images with additional geographical data in one display plane. These methods may be performed by a computer program and implemented in a radar scan converter.