Remote Unit Data Registration via Kalman Filter Alignment
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Solution Overview
Problem
In network-centric military operations, the alignment of sensor and navigation data from multiple platforms is challenging, leading to errors in correlating and fusing data, which affects the accuracy of track information and decision-making processes.
Innovation Solution
A system and method that utilize a Kalman filter to register and align positional data from multiple sources by estimating and correcting errors, weighing data based on accuracy probabilities, and propagating corrected data to ensure alignment with absolute geographic and time standards, enhancing data accuracy and correlation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensor and navigation data from multiple platforms are exchanged and processed in network-centric operations, then the ability to create a common track picture and enable command and decision processing is improved, but errors in correlating and fusing data increase due to misalignment between remote and local tracks
Solution Approach 1:
The system employs a feedback mechanism where the local track is used as a reference to estimate and correct remote unit errors. The processor continuously compares remote tracks with the local track, calculates registration errors, and applies corrections iteratively to maintain accurate alignment across the network
Solution Approach 2:
The local track serves as an intermediary reference frame that mediates between multiple remote units. By estimating remote unit errors relative to this common local reference, the system enables coherent fusion of data from diverse platforms without requiring direct pairwise alignment between all units
2Measurement precision
If remote unit sensor and navigation data is aligned with local unit data through data registration, then the accuracy of track correlation is improved, but the complexity of processing and estimating registration errors increases
Solution Approach 1:
The system extracts and isolates the registration errors as a separate estimable parameter. By identifying specific error components (position offsets, velocity biases, timing errors) in the remote unit data relative to the local reference, the complexity is managed through targeted error estimation rather than comprehensive reprocessing
Solution Approach 2:
The system transforms the alignment problem into a parameter estimation problem. By changing the approach from direct geometric alignment to estimating temporal and spatial parameters (position offsets, velocity biases, clock synchronization errors), the solution becomes computationally more manageable and scalable
Data Source
AI summary
A system and method is provided for registering outputs from a plurality of remote positional data sources, the system having: a plurality of positional data sources disposed on a plurality of units providing a plurality of types of positional data relative to at least one commonly tracked object held as a local positional data source; a processor disposed on a unit configured to process the positional data from each positional data source and apply a filter to the positional data; and the processor configured to weigh the positional data based on a probability of that a positional datum in the positional data is accurate and using weighted positional data to identify an absolute location of the commonly tracked object.