Software Receiver Signal Slice Correlation for Unknown Frequency
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Solution Overview
Problem
Existing approaches fail to efficiently receive electromagnetic signals when the transmitter frequency is unknown in advance, requiring significant computational resources, storage, and power, especially in scenarios with limited form factor, power source, and computational bandwidth, such as with ingestible event markers.
Innovation Solution
A software receiver samples electromagnetic signals in 'slices' and correlates them with sine and cosine reference signals, generating two-tuples that can be stored and manipulated to determine the signal frequency without requiring prodigious storage or computational resources, allowing for frequency detection even when the signal frequency is not known in advance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signal reception methods are used with unknown frequency, then the signal can be received, but significant computational resources and storage are required
Solution Approach 1:
The received signal is divided into multiple slices or segments in the time domain. Each slice is processed independently to generate a two-tuple (real and imaginary components), and these tuples are accumulated to produce the final frequency spectrum. This segmentation reduces the computational burden on any single processing stage while maintaining overall detection accuracy.
Solution Approach 2:
The patent extracts only the essential information from each signal slice - specifically the real and imaginary components represented as two-tuples - rather than processing the entire raw signal. This extraction approach minimizes storage requirements and computational resources while preserving the information needed for frequency detection through subsequent accumulation and rotation operations.
2Measurement precision
If the receiver stores and processes all received data, then accurate frequency detection is possible, but storage requirements become prohibitive
Solution Approach 1:
The patent extracts only the essential information from each signal slice - specifically the real and imaginary components represented as two-tuples - rather than processing the entire raw signal. This extraction approach minimizes storage requirements and computational resources while preserving the information needed for frequency detection through subsequent accumulation and rotation operations.
Solution Approach 2:
The patent transforms the signal processing approach by changing from storing raw time-domain samples to storing compressed frequency-domain two-tuples. This parameter transformation allows for efficient accumulation and rotation operations that enable frequency detection with minimal storage, as the two-tuple representation captures the essential spectral information in a compact form.
3Object-affected harmful factors
If a narrow bandpass filter is used, then noise is reduced, but the signal may be filtered out if frequency is not known exactly
Solution Approach 1:
The patent implements a dynamic frequency analysis approach where the signal is processed into multiple slices and the resulting two-tuples are rotated through different frequency offsets. This dynamic rotation allows the system to adaptively search across a range of frequencies without requiring a predetermined narrow filter, effectively combining noise reduction with frequency range coverage by analyzing multiple frequency hypotheses.
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
This method enables efficient detection of electromagnetic signals with reduced storage and computational demands, allowing for real-time analysis and re-analysis of stored data, even after the signal has ended, using minimal resources.
Implementation Method 1
The samples of each slice are correlated with values in a pair of reference signals, such as sine and cosine, at the reference frequency
Data Source
AI summary
In a software receiver, a received electromagnetic signal is sampled in “slices”, each having a duration of some multiple of a reference frequency. The samples of each slice are correlated with values in a pair of reference signals, such as sine and cosine, at the reference frequency. This yields a two-tuple for each slice, which two-tuples may be stored. The stored two-tuples can be simply added to arrive at a correlation value of narrower bandwidth than that of any slice taken alone. The stored two-tuples can be taken in sequence, each rotated by some predetermined angle relative to its predecessor in sequence, and the rotated two-tuples summed to arrive at a correlation value with respect to a frequency that is offset from the reference frequency to an extent that relates to the predetermined angle. In this way, the receiver is able to proceed despite the transmitted frequency not being known exactly in advance and does not require prodigious storage or computational resources.