Dual EKF Sensor Fusion for EOIR RF Tracking
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Solution Overview
Problem
Conventional sensor fusion methods for EO/IR and RF sensors in projectile guidance and tracking rely on static mixing coefficients, which fail to account for dynamic engagement geometry, leading to reduced measurement accuracy due to factors like slant range, altitude, and line of sight variations.
Innovation Solution
A dual adaptive mixing system using the residual vectors of extended Kalman filters to dynamically compute mixing coefficients in real-time, ensuring optimal sensor weighting based on actual event conditions, such as slant range, altitude, and line of sight, thereby enhancing tracking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static mixing coefficients are used in sensor fusion, then the system structure is simple and easy to implement, but the measurement accuracy deteriorates due to inability to account for dynamic engagement geometry
Solution Approach 1:
The patent applies dynamics by transitioning from static mixing coefficients to dynamic mixing coefficients that adapt in real-time based on engagement geometry. The mixing coefficients are continuously updated using the residual vectors from EKFs, which reflect the actual performance of each sensor under current conditions (slant range, altitude, LOS rate). This dynamic adaptation resolves the contradiction by maintaining system simplicity while significantly improving measurement accuracy through condition-dependent sensor weighting.
Solution Approach 2:
The patent changes the parameter of mixing coefficients from fixed constants to variable parameters that depend on engagement geometry. By using the residual vectors from EKFs as indicators of sensor performance under specific conditions, the system dynamically adjusts the weighting of each sensor's measurements. This parameter change enables the system to optimize measurement accuracy across varying engagement scenarios while maintaining computational efficiency.
2Measurement precision
If dynamic mixing coefficients are computed using residual vectors of EKFs, then the measurement accuracy is improved by accounting for actual engagement conditions, but the device complexity increases
Solution Approach 1:
The patent implements feedback by using the residual vectors from EKFs to dynamically adjust mixing coefficients. The residual vectors provide real-time feedback on how well each sensor is performing under current engagement conditions (slant range, altitude, LOS rate). This feedback mechanism enables the system to automatically optimize sensor weighting without requiring complex external control systems, thus improving measurement accuracy while managing complexity through self-adjustment.
Solution Approach 2:
The system performs self-service by using its own residual vectors to determine optimal mixing coefficients. Each EKF's residual vector automatically indicates the performance of its associated sensor, and this information is directly used to adjust the mixing coefficients. This self-service approach eliminates the need for external performance monitoring systems or complex decision-making algorithms, improving accuracy while keeping the system relatively simple.
3Productivity
If static gain scheduling is used for sensor mixing, then the computational load is low and processing is fast, but the tracking accuracy deteriorates under varying engagement geometry
Solution Approach 1:
The patent replaces static gain scheduling with dynamic mixing coefficients that adapt to varying engagement geometry in real-time. The mixing coefficients are continuously updated based on residual vectors from EKFs, which reflect current sensor performance under specific conditions (slant range, altitude, LOS rate). This dynamic approach maintains processing speed while significantly improving tracking accuracy across different engagement scenarios.
Solution Approach 2:
The patent changes the mixing parameters from fixed values in gain scheduling to dynamically computed values based on residual vectors. This parameter change allows the system to adapt to varying engagement geometry without requiring pre-programmed gain schedules for every possible condition. The computational load remains manageable because the mixing coefficients are derived directly from existing EKF residuals rather than requiring separate optimization calculations.
Data Source
AI summary
The system and method for EO/IR and RF sensor fusion and tracking using a dual extended Kalman filter (EKF) system provides a dynamic mixing scheme leveraging the strength of each individual sensor to adaptively combine both sensors' measurements and dynamically mix them based on the actual relative geometries between the sensors and objects of interest. In some cases the objects are adversarial targets and other times they are assets.