Autocorrelation Signal Detection Without Prior Knowledge
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Solution Overview
Problem
Existing communication systems face challenges in rapidly detecting signals of interest due to noise and attenuation, requiring prior knowledge of signal characteristics such as amplitude and center frequency.
Innovation Solution
A detection system that uses a receiver and analog-to-digital converter to generate digital signal samples, identifies sample offsets, executes autocorrelation functions, computes and normalizes their outputs, and determines the presence of a signal of interest independently of signal amplitude and center frequency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional detection algorithms are used that rely on filters and energy detection, then signal detection can be performed, but prior knowledge of signal characteristics (amplitude, center frequency, bandwidth, detection threshold) is required
Solution Approach 1:
The detection algorithm performs self-calibration by automatically determining signal characteristics (amplitude, center frequency, bandwidth) from the received signal itself, eliminating the need for external configuration or prior knowledge. The system serves itself by extracting necessary parameters directly from the signal being detected.
Solution Approach 2:
The algorithm dynamically adjusts detection parameters based on the received signal characteristics. Instead of using fixed thresholds and frequency settings, the system adapts parameters like detection threshold, bandwidth, and center frequency automatically through signal analysis and self-calibration processes.
2Reliability
If signal processing algorithms use filters to isolate frequencies and detect energy, then noise can be filtered out, but the detection process becomes slower and requires known signal characteristics
Solution Approach 1:
The patent replaces traditional mechanical filter-based signal processing with a computational autocorrelation-based detection method. Instead of using physical filters to isolate frequencies, the system uses digital signal processing through autocorrelation functions to identify signal characteristics, enabling faster detection without requiring prior knowledge of signal parameters.
Solution Approach 2:
The algorithm performs preliminary autocorrelation analysis on the received signal to quickly identify potential signal presence and characteristics before committing to more intensive processing. This preliminary action enables rapid initial detection and filtering of noise without requiring full signal parameter knowledge in advance.
3Measurement precision
If detection algorithms require prior selection of detection threshold, bandwidth, and center frequency, then accurate signal detection can be achieved, but the system cannot detect signals with unknown or varying characteristics
Solution Approach 1:
The detection system transitions from static, pre-configured detection parameters to dynamic, adaptive parameters. The algorithm continuously analyzes the received signal to determine appropriate detection thresholds, bandwidth, and center frequency settings in real-time, allowing the system to adapt to different signal types and conditions without manual reconfiguration.
Solution Approach 2:
The autocorrelation-based detection algorithm serves multiple functions: it detects signal presence, determines signal characteristics (amplitude, frequency, bandwidth), and adapts detection parameters all within a single unified process. This multi-functional approach eliminates the need for separate configuration steps and enables detection of various signal types with unknown characteristics.
Data Source
AI summary
A detection system includes a receiver configured to generate a receiver signal representative of detected electromagnetic energy, and an analog-to-digital converter (ADC) configured to generate a plurality of signal samples based on the receiver signal. The detection system also includes a detection module configured to identify a plurality of sample offsets for the signal samples, and execute a plurality of autocorrelation functions on the signal samples to provide an output of each of the autocorrelation functions, wherein each autocorrelation function is executed on at least a portion of the signal samples identified by a sample offset of the plurality of sample offsets. The detection module is also configured to compute a sum of the autocorrelation function outputs, normalize the sum of the autocorrelation function outputs, and determine whether a signal of interest is present within the electromagnetic energy based on the normalized sum.