Time-Frequency Signal Isolation for Noise-Obscured Detection
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Solution Overview
Problem
Conventional High Probability of Intercept (HPOI) receivers struggle to detect signals of interest (SOIs) obscured by noise, particularly those using frequency hopping or spread spectrum technology, due to thermal noise and sensitivity to interferers, making it difficult to identify these signals from a distance.
Innovation Solution
A method involving two data transforms, including a Fast Fourier Transform (FFT) and a wavelet transform, followed by noise reduction algorithms and image processing techniques, to isolate and extract signals of interest from noisy data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional HPOI receivers scan the entire spectrum to detect signals of interest, then the probability of intercept improves, but thermal noise and sensitivity to interferers increase substantially
Solution Approach 1:
The spectrum is divided into multiple frequency bins through FFT transformation, allowing the receiver to segment the broad spectrum into manageable portions. This segmentation enables selective processing of specific frequency regions where signals of interest are more likely to be found, reducing the impact of thermal noise across the entire spectrum while maintaining high probability of intercept.
Solution Approach 2:
The patent transforms the one-dimensional frequency spectrum into a two-dimensional time-frequency representation using spectrograms. This dimensional transformation allows signals of interest to be distinguished from thermal noise based on their temporal and spectral characteristics, enabling detection even when signals are obscured by noise in the traditional frequency domain.
2Measurement precision
If receivers use narrow bandwidth to improve sensitivity and avoid detection, then signal detection capability improves, but the ability to capture opportunistic signals deteriorates
Solution Approach 1:
The receiver dynamically adjusts its analysis by creating spectrograms that display signal characteristics across both time and frequency dimensions. This dynamic approach allows the system to identify and focus on specific time-frequency regions where signals of interest appear, effectively combining the benefits of narrow bandwidth sensitivity with broad spectrum monitoring capability.
Solution Approach 2:
The patent employs detection algorithms that analyze the spectrogram and provide feedback about the presence and characteristics of signals of interest. This feedback mechanism allows the receiver to adaptively adjust its processing focus to regions of the spectrum where signals are detected, improving both sensitivity and capture rate by concentrating resources on promising frequency-time regions.
3Object-affected harmful factors
If HPOI receivers operate at high standoff to mitigate interference from patterns of life, then interference from cellular and WIFI communications reduces, but detection of modern frequency hopping signals deteriorates
Solution Approach 1:
By transforming the signal data into the time-frequency domain using spectrograms, the patent enables detection of frequency hopping signals that would be invisible in the traditional frequency domain. Frequency hopping signals exhibit characteristic patterns in the time-frequency representation that distinguish them from random noise and interference, allowing reliable detection even at high standoff distances where signal power is low.
Solution Approach 2:
The patent changes the analysis parameters from simple frequency domain inspection to time-frequency joint analysis. This parameter change allows the receiver to exploit the temporal structure of frequency hopping signals, detecting them based on their hopping patterns rather than their instantaneous frequency or power, thereby maintaining detection capability at high standoff distances.
Data Source
AI summary
A method for signal detection including the steps of: (1) iteratively acquiring sets of signal samples in a time domain N times; (2) iteratively applying a first transform to each iteratively captured samples in the time domain to convert each set of samples in the time domain to a corresponding subsequent set of samples in a frequency domain; (3) iteratively creating array rows using each of the sets of samples in the frequency domain wherein each subsequent array row has a length N; (4) appending each subsequent array row to the array until a second axis representing time reaches length N; (5) applying a second transform to the array to create a plurality of layers of spectral content; (6) determining a location of a signal of interest; and (7) applying a bounding box to the array to isolate the signal of interest.