Radar Fill Level Measurement Curve Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Radar-based fill level measurement devices face challenges in storing and transmitting evaluation curves due to limited storage capacity and low data transmission rates, leading to considerable data loss when curves are heavily compressed, making subsequent diagnosis impossible.
Innovation Solution
A method for compressing evaluation curves using linear prediction to generate an approximated curve and error curve, where only the estimation coefficients and error curve are transmitted, allowing for efficient decompression without data loss, and further reducing data volume through iterative calculation and entropy coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If heavy compression is applied to evaluation curves to reduce data volume, then storage capacity and transmission efficiency are improved, but data loss occurs making subsequent diagnosis impossible
Solution Approach 1:
The patent extracts only the essential diagnostic information from the evaluation curve by identifying and transmitting only the position and amplitude of local maxima (peaks). This selective extraction allows significant data reduction while preserving all information needed for diagnosis, as the compressed data contains precisely the critical features required for identifying measurement issues.
Solution Approach 2:
The evaluation curve is segmented into discrete, independent features (local maxima) rather than transmitting the continuous curve data. Each local maximum is identified and transmitted as a separate data point containing position and amplitude information, enabling efficient compression while maintaining diagnostic capability.
2Quantity of substance
If linear prediction is used to compress evaluation curves, then data volume is reduced, but complex calculation processes are required
Solution Approach 1:
Instead of applying complex linear prediction algorithms, the patent extracts only the essential features (local maxima positions and amplitudes) directly from the evaluation curve. This extraction approach achieves compression without requiring complex calculations, as it simply identifies and records the positions and values of peak points.
Solution Approach 2:
Rather than using linear prediction to approximate the curve and transmit prediction coefficients, the patent inverts the approach by directly identifying and transmitting the actual critical features (local maxima) from the original data. This reversal eliminates the need for complex prediction calculations while achieving the same compression goal.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient compression and decompression of evaluation curves without data loss, reducing the overall data volume to be transmitted, and allowing for accurate diagnosis by minimizing the data volume of estimation coefficients and error curves, achieving a compression factor of up to 2.
Implementation Method 1
radar-based fill level measurement
Implementation Method 2
receive signal after reflection of the radar signal
Data Source
AI summary
The present disclosure relates to a method for compressing an evaluation curve, which is recorded during a radar-based fill level measurement of a filling material located in a container, and to a corresponding fill level measurement device for carrying out the method. Corresponding to the compression method according to the present disclosure, the present disclosure comprises a corresponding method for decompressing the compressed evaluation curve. The compression method is characterized in that the compression occurs using linear prediction, by corresponding estimation coefficients and an error curve being created. This makes use of the finding according to the present disclosure that evaluation curves can be compressed for diagnostic purposes, in particular in the case of FMCW-based fill level measurement, efficiently and without data loss using the model of linear prediction.