Latency Compensation for Multi-Sensor Data Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Complex dynamic systems, such as automatic landing systems in aircraft, face latency issues due to differences in sensor outputs from multiple sensors, which can bias model results and introduce unwanted perturbations.
Innovation Solution
A computer-based method and system for latency compensation that receives data from multiple sensors, constructs a model of latency effects, and uses this model to calculate and apply latency compensation adjustments, ensuring accurate data fusion and reducing latency-induced errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from multiple sensors are combined to model dynamic systems, then measurement precision is improved, but latency differences between sensors introduce errors that worsen reliability
Solution Approach 1:
The system performs preliminary latency compensation by calculating compensation values based on latency characteristics before fusing sensor data. This advance correction prevents latency-induced errors from corrupting the model results, thereby maintaining both measurement precision and reliability.
Solution Approach 2:
The system uses feedback mechanisms where the combining filter outputs are used to update latency compensation calculations. This closed-loop approach continuously refines the compensation values based on actual system behavior, improving reliability while preserving the precision benefits of multi-sensor fusion.
2Reliability
If latency compensation is applied to sensor data, then reliability of model results is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system changes parameters by introducing latency compensation values that adjust sensor data based on their respective latency characteristics. This parameter transformation approach improves reliability without requiring fundamental architectural changes, thus limiting the increase in device complexity to manageable levels.
Solution Approach 2:
The patent introduces an intermediary latency compensation mechanism that mediates between raw sensor data and the combining filter. This intermediary layer handles the complexity of latency correction in a modular way, improving reliability while containing device complexity through structured data flow management.
3Measurement precision
If sensor data processing is enhanced with latency compensation, then accuracy of dynamic system modeling is improved, but loss of time occurs due to additional computational steps
Solution Approach 1:
The system performs latency compensation calculations in advance, before the actual data fusion process. This preliminary action ensures that compensation values are ready when needed, improving modeling accuracy without adding significant processing time during critical operations.
Solution Approach 2:
The system optimizes processing time by changing parameters such as using efficient mathematical formulations for latency compensation. These parameter optimizations maintain high modeling accuracy while minimizing the time penalty associated with additional computational steps.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Systems and methods for latency compensation are disclosed. In one embodiment, a computer-based system for latency compensation in a dynamic system (100) comprises a processor and logic instructions stored in a tangible computer-readable medium coupled to the processor (222) which, when executed by the processor (222), configure the processor (222) to receive at least first parameter data from a first sensor (120a) and second parameter data from a second sensor (120b), direct the at least first parameter data and the second parameter data into a combining filter (140), receive additional parameter data about the dynamic system (100) from at least one additional sensor (122), construct a model of latency effects (126) on the first parameter data and the second parameter data, and use the model of latency effects (126) to compensate for latency-based differences in the first parameter data and the second parameter data.