Telemetric Time Series Resampling for Bandwidth Reduction
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Solution Overview
Problem
Current methods for downlinking telemetric time series data from remote systems, such as spacecraft, are limited by bandwidth, leading to restricted sampling rates and high CPU usage due to lossless compression techniques, which fail to efficiently reduce data volume while maintaining high fidelity.
Innovation Solution
A resampling method that reduces data volume by selecting specific sample points through linear interpolation and dividing time series data into subsequences until variations are within a predetermined error threshold, allowing for transmission with reduced bandwidth and CPU usage without encoding or decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lossless compression techniques are used to reduce data volume, then bandwidth usage is reduced, but CPU power consumption increases and compression rates are limited
Solution Approach 1:
The patent changes the fundamental parameter of data representation by resampling time series data at variable intervals based on event significance, rather than using fixed-interval sampling followed by compression. This transforms the data at the source to reduce volume before transmission, eliminating the need for CPU-intensive compression algorithms while achieving comparable or better bandwidth efficiency
Solution Approach 2:
The system performs preliminary resampling and filtering of time series data on the spacecraft before transmission. By pre-processing the data to remove redundant information and retain only significant events, the system reduces the data volume that needs to be transmitted and stored, thereby reducing both bandwidth requirements and the CPU power needed for compression
2Measurement precision
If lossless compression techniques are used to maintain precision, then measurement fidelity is preserved, but achievable sampling rate is limited
Solution Approach 1:
The patent applies different sampling densities to different portions of the time series data based on local characteristics. High-sampling regions are applied only to segments containing significant events or rapid changes, while low-sampling regions are applied to stable, unchanging parameters. This local differentiation maintains measurement precision where needed while increasing overall sampling rate and reducing data volume
Solution Approach 2:
Instead of applying uniform high-rate sampling to all parameters (excessive action), the system applies high-rate sampling only when and where it is necessary to capture significant events (partial action). This selective approach maintains measurement precision for critical events while reducing the overall sampling burden and data volume
3Measurement precision
If high sampling rate is used to capture short-lived events, then monitoring fidelity is improved, but bandwidth requirements increase
Solution Approach 1:
The patent implements dynamic sampling where the sampling interval adjusts based on the activity level of the monitored parameter. When significant events or rapid changes are detected, the system automatically increases sampling rate to capture the event fidelity. When parameters are stable, sampling rate decreases. This dynamic adaptation maintains monitoring fidelity for short-lived events while significantly reducing the overall bandwidth required compared to uniform high-rate sampling
Data Source
AI summary
A method for resampling time series data includes determining a first time series of data points; determining a variation of the data points within a selected sequence, wherein the variation is determined by determining linearly interpolated data points for the data points of the selected sequence and by determining a maximum absolute difference. If a determined variation is greater than a predetermined error value, subsequences are selected by repeatedly dividing the first time series into adjacent subsequences until the first time series comprises a set of subsequences, wherein a determined variation for each of the subsequences is smaller than the predetermined error value. A resampled time series is created by adding the data points of the first time series that correspond to a first or last data point of the selected subsequences to the resampled time series, wherein every data point is added only once to the resampled time series.