Kalman Filter Noise Quantification via Indirect Measurements
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Solution Overview
Problem
Accurate remote tracking of fixed objects from a moving platform is compromised due to unanticipated noise from platform vibrations and flexing, which existing Kalman filtering methods struggle to address effectively, especially when the inertial reference is remote from the sensors, leading to reduced tracking accuracy and increased costs for equipment consolidation or noise boosting.
Innovation Solution
The method involves deriving inertial reference parameters to quantify sensor noise remotely, using indirect navigation noise measurements that account for structural dynamics and error from remote motion measurements, and combining these with sensor noise through statistical analysis to improve Kalman filter accuracy without requiring additional equipment or complex structural models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If indirect noise measurements are used to account for structural dynamics, then measurement accuracy is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary computational model that translates remote inertial reference measurements into sensor-specific noise estimates. This model acts as a mediator between the inertial reference system and the sensor, accounting for structural dynamics without requiring direct physical modification or additional hardware at the sensor location.
Solution Approach 2:
The patent replaces physical mechanical solutions (such as consolidating sensors and inertial references or adding noise boosting equipment) with a computational approach. By using indirect noise measurements and statistical analysis, the system substitutes hardware modifications with software-based noise characterization and compensation.
2Measurement precision
If equipment consolidation or noise boosting is implemented, then tracking accuracy is improved, but implementation cost increases
Solution Approach 1:
The patent creates a computational copy or model of the noise characteristics at the sensor location based on measurements from the remote inertial reference. Instead of physically moving or consolidating equipment, the system replicates the noise information through mathematical modeling and statistical analysis, making it available for Kalman filtering without hardware consolidation.
3Device complexity
If remote motion measurements are used, then device complexity is reduced, but measurement precision deteriorates due to structural dynamics
Solution Approach 1:
The patent transforms the remote inertial reference measurements into sensor-specific noise parameters through computational processing. By changing the parameters from raw motion data to characterized noise estimates that account for structural dynamics, the system maintains measurement precision while keeping the device configuration simple and distributed.
Data Source
AI summary
Accurate remote tracking of fixed objects from a moving platform requires overcoming platform noise. Such tracking becomes difficult when the only inertial reference (such as a central aircraft inertial navigation system) is remote from the sensor, which experiences non-measured angular movements due to airframe vibrations and flexing. In such a scenario, Kalman filtering cannot converge on a true value because all noise sources are not known. Current naïve approaches arbitrarily boost noise with fixed additive or multiplicative factors. However, such approaches slow filter response and; thus, often fail to give timely results. Embodiments of the present disclosure derive inertial reference parameters to quantify noise of the sensor that is remote from the inertial reference. Advantageously, disclosed embodiments enable use of remote sensors with an existing inertial reference, rather than consolidating sensors and the inertial reference at a single location or providing inertial references at each sensor.