Sensor Registration Bias Compensation via Extended Kalman Filter
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Solution Overview
Problem
Current sensor registration methods in ballistic missile defense systems are inadequate for accurately determining target position and velocity when there is uncertainty in sensor location, particularly due to GPS failures from jamming or spoofing, which can lead to tracking and guidance errors.
Innovation Solution
The Geo-Positional Sensor Level EStimation System (GPSLESS) employs an extended Kalman filter algorithm with real-time sensor registration estimation to compensate for positional sensor biases, using Jacobian matrix computations and state transition matrices to provide improved target state estimates and sensor positional bias correction, even in the absence of GPS data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS receivers are used to provide absolute positional reference, then measurement precision is improved, but reliability deteriorates due to susceptibility to jamming and spoofing
Solution Approach 1:
The patent introduces an intermediary approach by using sensor registration estimation as a mediator between the sensor measurements and the target state estimation. Instead of directly relying on GPS for absolute position, the system estimates and compensates for sensor position bias through the registration process, allowing the system to function reliably even when GPS is unavailable or compromised.
Solution Approach 2:
The patent replaces the mechanical/GPS-based positioning system with an estimation-based system. Rather than using external GPS signals to determine sensor position, the system uses a mathematical model that estimates sensor position bias through the measurement of target trajectories and known acceleration, substituting physical positioning infrastructure with computational estimation.
2Ease of operation
If sensor registration is performed assuming no positional bias, then ease of operation is improved, but measurement precision deteriorates due to uncorrected positional errors
Solution Approach 1:
The patent changes the parameter approach by transforming the sensor position from a fixed known value to an estimated parameter. The system introduces sensor position bias as an estimable parameter and uses the extended Kalman filter to dynamically estimate and compensate for this bias, allowing the system to maintain both operational simplicity and measurement precision.
Solution Approach 2:
The patent implements feedback by continuously estimating sensor position bias from measurement residuals and using this estimate to correct subsequent measurements. The extended Kalman filter provides feedback loops that adjust the sensor registration based on the discrepancy between expected and actual target positions, thereby maintaining precision without complicating the operational process.
Data Source
AI summary
A method for determining or compensating for the positional errors of a sensor tracking a target comprises the steps of operating the sensor to generate sensed information relating to the target and adding any sensor positional bias update information to produce updated sensed information. The target state is propagated to produce time updated state estimates. The Jacobian of the state dynamics and the state transition matrix for the extended Kalman filter algorithm are computed. The covariance of a state vector is time propagated using the state transition matrix.