RF Trigger Detection via Spectral Analysis
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Solution Overview
Problem
Existing methods for detecting trigger events in Radio Frequency (RF) systems, such as changes due to fading or interference, are inadequate as they rely on predictable criteria like RF level and frequency, and require demodulation, which is technology-specific and inefficient.
Innovation Solution
A method and apparatus that analyze the energy content of RF signals over time by generating data corresponding to frequency changes, comparing it with predetermined spectra, and generating a trigger signal when a match is detected, allowing for early adaptation to new technologies without requiring demodulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If demodulation is used to provide triggers, then trigger events can be detected, but processing overhead and time penalty increase
Solution Approach 1:
The patent extracts only the essential spectral features needed for trigger detection without performing full demodulation. By analyzing frequency content and spectral patterns directly from the RF signal, the system obtains sufficient trigger information while avoiding the time-consuming demodulation process.
Solution Approach 2:
Instead of complete demodulation, the patent applies partial action by performing spectral analysis only on the portions of the signal that contain trigger-relevant information. The Fast Fourier Transform is applied selectively to detect spectral patterns, providing adequate trigger detection with reduced processing effort.
2Measurement precision
If demodulation is used to provide triggers, then trigger events can be detected, but processing overhead increases
Solution Approach 1:
The patent extracts only the essential spectral features needed for trigger detection without performing full demodulation. By analyzing frequency content and spectral patterns directly from the RF signal, the system obtains sufficient trigger information while avoiding the complex demodulation process.
Solution Approach 2:
The patent replaces the mechanical demodulation process with a spectral analysis approach using Fast Fourier Transform. This substitution simplifies the processing by working directly with frequency domain representations of the signal, avoiding the complex time-domain demodulation operations.
3Ease of operation
If RF level and frequency criteria are used for triggering, then some signals can be identified, but modulation type differences cannot be detected
Solution Approach 1:
The patent transitions from analyzing single-dimensional parameters (RF level and frequency) to analyzing the spectral dimension by examining frequency content distribution across multiple frequency bins. This dimensional expansion enables detection of modulation type differences through spectral patterns while maintaining operational simplicity.
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 effective detection of trigger events based on time-varying spectrum variations, providing a technology-independent solution that can adapt to new technologies and detect sporadic or unpredictable events, improving RF system performance and adaptability.
Implementation Method 1
converting a number of the samples of the input signal to the frequency domain. Converting the samples of the input signal to the frequency domain may comprise: performing Fourier Transforms on the number of the samples of the input signal. The Fourier Transform may be a Fast Fourier Transform (FFT).
Data Source
AI summary
A method of detecting a trigger event includes receiving an input signal for analysis. The received input signal is used to generate first data corresponding to sequential sets of spectral output data sets, at least some of the spectral output data sets corresponding to sample points in time acquired from the input signal as the input signal changes with time. At least part of the first data is then compared with second data, the second data corresponding to a predetermined sequential set of signature spectral data sets corresponding to a time-varying spectrum associated with a trigger event. A trigger signal is then generated in response to a change in a state of match between the at least part of first and second data.