Time Tag Jitter Removal via Clock Drift Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Accurate time correlation of data from multiple sensors is challenging due to clock drift and crossover distortion, which leads to time tag jitter and bias errors, especially in high-dynamic environments like the aircraft industry, where conventional methods require custom hardware and real-time operating systems.
Innovation Solution
A system and method that estimate clock drift and apply filtering techniques to remove time tag jitter and crossover distortion, allowing for precise time correlation of data streams without the need for custom hardware or real-time operating systems, using a data control system with a time tag module and filtering module to manage data from multiple sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional time tagging methods are used with single processor and real-time operating systems, then time correlation accuracy can be maintained, but device complexity and cost increase significantly
Solution Approach 1:
The patent replaces the mechanical/time-critical system (real-time operating system with custom hardware) with a software-based filtering solution. The time tag conditioning algorithm processes data packets using computational filtering to remove jitter and crossover distortion, eliminating the need for expensive real-time operating systems and custom hardware while maintaining sub-10-microsecond accuracy.
2Ease of operation
If sensor clocks are allowed to drift naturally, then ease of operation is improved, but time correlation accuracy deteriorates due to crossover distortion
Solution Approach 1:
The patent converts the harmful effect of clock drift and crossover distortion into a detectable pattern. By identifying when multiple sensors generate data at nearly the same time (crossover period), the system applies corrective filtering to remove the resulting time tag bias errors, thereby maintaining accuracy while allowing sensors to operate independently with drifting clocks.
Solution Approach 2:
The system continuously monitors time tag patterns from multiple sensors to detect crossover distortion conditions. When distortion is detected, the time tag conditioning algorithm applies corrective filtering based on the identified pattern, creating a feedback loop that maintains time correlation accuracy despite ongoing clock drift.
3Device complexity
If data packets are processed sequentially in a queue, then device complexity is reduced, but time tag accuracy worsens due to processing delays and jitter
Solution Approach 1:
The patent replaces the physical/time-critical constraint of sequential processing with a software-based time tag conditioning algorithm. The filtering process computationally removes the jitter and bias errors introduced by sequential queue processing, allowing simple sequential processing to achieve the same accuracy as complex real-time systems.
Data Source
AI summary
Techniques for removing time tag jitter and crossover distortion from a measurement system are described. A period or clock drift estimate is generated for each sensor, and the estimates are used to remove time tag jitter and detect and remove crossover distortion. This crossover distortion condition is common to measurement systems required to acquire and accurately time tag data from multiple sensors where the sensors produce data at an asynchronous periodic rate which may result in a distortion in time tag accuracy. These techniques are germane to aircraft and other systems that require precisely time correlated measurements from multiple sensors hosted by a data control system, and can obviate the use of real time operating systems and specialized hardware for this purpose.