Pulse Train Period Estimation Using Segmented Autocorrelation
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Solution Overview
Problem
Conventional techniques face difficulties in accurately estimating the period of pulse train signals, especially when signals with multiple periods and large period differences are present, as shorter periods are easily distinguished from noise while longer periods are harder to discern.
Innovation Solution
An estimation device that aggregates pulse train signals into unit times, calculates time shift amounts for autocorrelation functions, detects periods by exceeding a threshold, converts detected periods to the original signal scale, and excludes identified periods from the input signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional PRI transform with autocorrelation function is used, then shorter period signals are easily distinguished from noise, but longer period signals become difficult to distinguish from noise when multiple periods with large period difference are present
Solution Approach 1:
The pulse train signal is divided into multiple unit time segments, and autocorrelation calculations are performed separately for each segment. This segmentation allows the system to process different period characteristics independently, improving the ability to distinguish both short and long period signals from noise.
Solution Approach 2:
The patent transforms the one-dimensional autocorrelation value into a two-dimensional distribution by calculating autocorrelation values across multiple unit time segments. This dimensional expansion creates a more robust statistical basis for distinguishing true periodic signals from noise, particularly for long period signals that would otherwise be indistinguishable.
2Measurement precision
If autocorrelation function is applied to pulse train signal, then period estimation can be performed, but signals with large period differences cannot be accurately estimated due to noise interference
Solution Approach 1:
By dividing the signal into unit time segments and performing separate autocorrelation calculations, the system can handle multiple period characteristics simultaneously. Each segment's autocorrelation results are then integrated to provide comprehensive period estimation for signals with large period differences.
Solution Approach 2:
The patent introduces an intermediary process that aggregates autocorrelation results from multiple unit time segments. This intermediary aggregation step serves as a mediator that combines information from different time segments, enabling accurate estimation of multiple periods even when they differ significantly in length.
3Measurement precision
If time-shifted autocorrelation is used for period estimation, then signal matching can be calculated, but longer period signals produce smaller autocorrelation values that are harder to distinguish from noise
Solution Approach 1:
Segmenting the signal into unit time segments allows the system to accumulate autocorrelation information across multiple segments. This segmentation approach prevents the loss of long period signal information by ensuring that autocorrelation values are calculated and preserved from each segment, then combined to enhance the overall signal detection capability.
Solution Approach 2:
The patent merges autocorrelation results from multiple unit time segments into a comprehensive period estimation. By combining the autocorrelation values from different segments, the system enhances the detection capability for long period signals, preventing information loss that would occur in single-segment analysis.
Data Source
AI summary
An aggregation unit (15a) aggregates an input pulse train signal including a time-series pulse corresponding to a predetermined observation time into pulses for respective unit times. A calculation unit (15b) calculates a time shift amount of an autocorrelation function using the aggregated pulse train signal. A detection unit (15c) calculates an autocorrelation value and a threshold with respect to each of time shift amounts selected in ascending order from the calculated time shift amount and detects the time shift amount as a period of the aggregated pulse train signal when the autocorrelation value exceeds the threshold. A conversion unit (15d) converts the detected period to a period of the input pulse train signal using the unit time. An exclusion unit (15e) excludes the pulse train signal having the converted period from the input pulse train signal.