Nonlinear Interference Classification for Noise Burst vs Sinusoid Detection
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Solution Overview
Problem
Existing systems face challenges in reliably discriminating between transient interference from noise bursts and sinusoidal signals, particularly in passive sensing systems like radio astronomy and microwave radiometry, where man-made interference is indistinguishable from natural phenomena, affecting the accuracy of data collection and weather forecasting.
Innovation Solution
An interference classifier is developed, comprising a buffer, scale factor calculator, normalizer, nonlinear transformer, and comparator, which performs a nonlinear transform on normalized signal samples to determine whether the signal contains a noise burst or sinusoidal interference by calculating a scale factor, normalizing the signal, applying a nonlinear transformation, and comparing the average level to a threshold, using a combination of functions such as Lorentz and parabolic transforms to approximate conditional probability ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a passive energy sensor integrates power over a time interval to determine microwave energy level, then the measurement captures all emissions (natural and man-made), but the sensor cannot discriminate between man-made interference and natural phenomena
Solution Approach 1:
The patent segments the signal analysis process into multiple stages: initial energy detection, transient identification, and classification. By dividing the measurement process and applying different analysis methods to different signal characteristics, the system can distinguish between natural emissions and man-made interference while maintaining overall energy measurement accuracy.
Solution Approach 2:
The patent employs dynamic signal processing that adapts to the characteristics of incoming signals. The system dynamically adjusts its analysis approach based on whether signals are transient or continuous, and uses time-varying statistical properties to differentiate between natural and artificial sources, enabling discrimination without sacrificing measurement precision.
2Reliability
If pulse-blanking techniques are used to excise intervals containing interference, then the reliability of interference detection improves, but the ability to reliably detect and reject contaminated data depends on accurate classification which is currently unavailable
Solution Approach 1:
The patent implements a feedback mechanism where the classification results from signal analysis feed back into the pulse-blanking process. The system continuously monitors signal characteristics, classifies transients as natural or artificial, and uses this classification feedback to control which intervals are excised, creating a closed-loop system that improves both reliability and accuracy of interference rejection.
Solution Approach 2:
The patent performs preliminary classification of transients before applying pulse-blanking. By analyzing signal characteristics and classifying transients as natural or artificial before the blanking decision is made, the system ensures that only appropriately classified intervals are rejected, improving the reliability of the interference rejection process.
3Difficulty of detecting and measuring
If high-level transients are detected to trigger interference rejection, then man-made interference can be identified, but high-level transients may also originate from natural phenomena causing false alarms
Solution Approach 1:
The patent changes from using a single parameter (signal level) for detection to using multiple parameters including temporal characteristics, spectral properties, and statistical measures. By analyzing changes in these parameters over time and comparing them against classification criteria, the system can distinguish between natural high-level transients and man-made interference, reducing false alarms while maintaining detection sensitivity.
Solution Approach 2:
The patent employs dynamic classification that considers the temporal evolution of signal characteristics rather than static threshold comparisons. The system dynamically assesses multiple features of transients and adapts its classification decision based on the specific characteristics observed, improving detection accuracy and reducing false alarms from natural phenomena.
4Productivity
If conventional energy detectors are used in cognitive radio networks, then spectrum sensing can be performed, but simple energy detectors cannot provide reliable detection of signal presence
Solution Approach 1:
The patent segments the spectrum sensing process into energy detection followed by transient classification. By dividing the detection task and adding a classification stage that analyzes specific signal characteristics, the system maintains the productivity of energy detection while significantly improving the reliability of signal presence detection through additional discriminative analysis.
Solution Approach 2:
The patent implements dynamic signal analysis that adapts to different spectral conditions. The system dynamically evaluates signal characteristics and uses adaptive classification criteria to determine signal presence, improving reliability over conventional static energy detection while maintaining efficient spectrum sensing productivity.
Data Source
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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.