Nonlinear Interference Classification for Noise Burst Discrimination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in reliably discriminating between transient interference originating from noise bursts and sinusoidal signals, particularly in passive sensing systems, which affects the accuracy of frequency analysis and weather forecasting, as they cannot distinguish between man-made and natural interference.
Innovation Solution
A method and apparatus that utilize a buffer, scale factor calculator, normalizer, nonlinear transformer, and comparator to classify signals by normalizing samples, applying a nonlinear transform, and comparing the average level to a threshold, employing a combination of functions for normalized signal samples to differentiate between noise bursts and sinusoidal interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a passive energy sensor integrates power over a time interval to detect natural emissions, then the measurement sensitivity is improved, but the ability to discriminate between natural phenomena and man-made interference deteriorates
Solution Approach 1:
The sensor output signal is divided into multiple sample points over the integration period. Each sample point is individually processed through the nonlinear transform and compared against thresholds, allowing discrimination of interference while maintaining overall measurement sensitivity through selective rejection of contaminated samples.
Solution Approach 2:
A nonlinear transform function serves as an intermediary between the raw sensor signal and the final measurement result. This transform modifies the signal characteristics such that man-made interference produces distinguishable output patterns compared to natural phenomena, enabling reliable discrimination while preserving sensitivity to natural emissions.
2Reliability
If pulse-blanking techniques replace corrupted samples with zeros to reject interference, then the rejection of man-made interference is improved, but the measurement accuracy deteriorates due to loss of valid data
Solution Approach 1:
The system continuously monitors the nonlinear transform output and uses feedback from threshold comparisons to identify and excise only those specific sample intervals containing interference. This selective approach maintains measurement accuracy by preserving valid data while achieving reliable interference rejection through adaptive sample-by-sample evaluation.
3Ease of operation
If the signal level is used to detect interference, then the detection simplicity is improved, but the discrimination accuracy deteriorates because high-level transients may originate from natural phenomena
Solution Approach 1:
Instead of using the raw signal level for detection, the system applies a nonlinear transform that changes the parameter being measured. The transform converts both natural and artificial signals into a common domain where their statistical properties differ, enabling accurate discrimination based on transformed signal characteristics rather than simple amplitude thresholds.
Data Source
AI summary
An interference classifier is disclosed for determining the type of interference present in a signal. The interference classifier 601 comprises a buffer 602 operable to receive and store data comprising samples of a signal; a scale factor calculator 603 operable to use the signal samples to calculate a scale factor in dependence upon the levels of the signal samples; a normaliser 604 operable to calculate normalised signal samples by using the scale factor to normalise the signal samples; a nonlinear transformer 605 operable to perform a nonlinear transform on the normalised signal samples to calculate transformed signal samples; an averaging circuit 606 operable to calculate an average of the transformed signal samples; and a comparator 607 operable to compare the calculated average of the transformed signal samples to a predetermined threshold level in order to determine the type of interference present in the received signal. The interference classifier 601 disclosed herein performs an advantageous type of nonlinear transform that provides an improvement in detection probability over known kurtosis-based interference classifiers. Applications of the interference classifier 601 include automotive radar systems, radio astronomy, microwave radiometry, weather forecasting and cognitive radio networks.