Active Reduced State Estimator for Dynamic Flight Regimes
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Solution Overview
Problem
Current Optimal Reduced State Estimation (ORSE) systems initialize parameters with maximum biases that can become stale over time, leading to oversized uncertainties, particularly when drag forces vary with speed and altitude, causing overestimation of accelerations in flight regimes.
Innovation Solution
A method for actively estimating the state of a system by using radar systems and computer processors to initialize state variables with known bounded values, apply measurements to an estimating filter with mean square optimization, and update these values based on regime changes detected by analyzing flight regime transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current ORSE systems initialize parameters with maximum biases, then the estimation covers all possible maneuvers, but the uncertainties become oversized and stale over time
Solution Approach 1:
The patent applies dynamics by transitioning from static maximum bias initialization to dynamic bound updating. The system continuously monitors acceleration measurements and updates the bounds based on actual observed acceleration levels, allowing the estimation parameters to adapt to changing flight conditions rather than remaining fixed at conservative maximum values throughout the track duration.
Solution Approach 2:
The patent implements feedback by using actual acceleration measurements to refine the bounds. The system compares observed acceleration with the current bounds and adjusts the bounds accordingly, creating a closed-loop process that reduces oversized uncertainties while maintaining reliability. This feedback mechanism allows the system to learn from actual track behavior and update its parameter estimates dynamically.
2Adaptability or versatility
If maximum bias is used for drag forces, then all possible drag scenarios are covered, but the drag overestimation occurs for much of the trajectory
Solution Approach 1:
The patent applies dynamics by transitioning from static maximum bias initialization to dynamic bound updating. The system continuously monitors acceleration measurements and updates the bounds based on actual observed acceleration levels, allowing the estimation parameters to adapt to changing flight conditions rather than remaining fixed at conservative maximum values throughout the track duration.
Solution Approach 2:
The patent changes the parameter from fixed maximum bias to time-varying bounds. Instead of using a constant conservative estimate for drag, the system adjusts the drag bound parameters dynamically based on observed flight behavior, speed, and altitude changes, allowing more accurate drag force estimation while maintaining adaptability to different flight regimes.
3Device complexity
If initial bounds are used throughout the track, then computation is simplified, but the bounds become stale and inaccurate for current track state
Solution Approach 1:
The patent applies dynamics by transitioning from static maximum bias initialization to dynamic bound updating. The system continuously monitors acceleration measurements and updates the bounds based on actual observed acceleration levels, allowing the estimation parameters to adapt to changing flight conditions rather than remaining fixed at conservative maximum values throughout the track duration.
Solution Approach 2:
The patent ensures continuity of useful action by maintaining continuous bound updating throughout the track duration. Rather than using initial bounds that become stale, the system continuously refines the bounds based on ongoing measurements, ensuring that the bounds remain accurate and relevant for the current track state at all times.
Data Source
AI summary
An active state estimation system comprises radar systems for obtaining sensor measurements, data storage devices for storing the sensor measurements, and computer processors in communication with the data storage devices. A memory stores program instructions which cause the computer processors to initialize a system having state variables and also having unknown, multidimensional, arbitrarily time-varying parameters, but which are subject to known bounded values. Sensor measurements for the object being tracked are then received, and applied to an estimating filter that explicitly uses a mean square optimization criterion that separately accounts for measurement errors and said bounding values, to produce estimates of the true state of the system. The system also determines whether a regime change has occurred based on the estimates of the true state of the system, and if so, determines updated known bounded values that are used to update the boundaries used by the system.