Compressive Sensing for Wideband Signal Detection in Low SNR
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Solution Overview
Problem
Existing compressive sensing techniques for wideband signal detection suffer from missed detections and increased false alarm rates when signal SNR is low and dynamic range is moderate, failing to effectively track input signals in real-time.
Innovation Solution
The method involves compressively sensing the strongest narrowbanded signals, determining their center frequencies, and iteratively removing them from the input signal, while using frequency-shifted measurements to enhance detection accuracy and reduce false alarms, with additional narrowband receivers for signal analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing compressive sensing techniques are used for wideband signal detection, then sampling resources are saved, but detection accuracy deteriorates in low SNR environments with moderate dynamic range
Solution Approach 1:
The patent segments the wideband signal detection problem into multiple narrowband detection tasks. By dividing the wide frequency band into several narrowband segments and detecting signals in each segment separately, the system achieves better detection accuracy for low SNR signals while maintaining the efficiency benefits of compressive sensing.
2Productivity
If compressive sensing is applied to detect signals over very wide bandwidth, then real-time detection capability is improved, but false alarm rate increases in low SNR conditions
Solution Approach 1:
The patent employs dynamic threshold adjustment based on local signal characteristics in each narrowband segment. By adapting the detection threshold to the specific SNR conditions and signal properties of each segment, the system maintains high real-time detection capability while significantly reducing false alarm rates in low SNR environments.
3Measurement precision
If the strongest narrowbanded signal is removed iteratively from the input signal, then detection of weaker signals is improved, but processing complexity increases
Solution Approach 1:
The patent extracts and removes the strongest detected signal from the composite input signal before performing detection on the remaining signals. This extraction approach simplifies the detection process by eliminating dominant signals that would otherwise mask weaker signals, making the overall processing more efficient despite the iterative nature of the algorithm.
Data Source
AI summary
A method of and apparatus removing of a plurality of relatively narrow banded signals in a relatively wide banded input signal. The method involves and the apparatus provides for compressively sensing one relatively narrow banded signal in the relatively wide banded input signal and removing one relatively narrow banded signal from the relatively wide banded input signal before detecting and removing another relatively narrow banded signal in the relatively wide banded input signal, the step of and apparatus for compressing sensing occurring with respect to both (i) the input signal with the previously detected narrow banded signals removed therefrom and (ii) a frequency shifted version of (i).