Target State Estimation Using Kalman Filter and Linearized Parameters
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Solution Overview
Problem
Existing target state estimation methods, such as Bearing Only Target Motion Analysis (BO-TMA), face challenges in handling non-linear motion of targets, leading to increased calculation complexity and instability in state estimation.
Innovation Solution
A target state estimation device that includes a calculated bearing value acquisition unit, an estimated state value calculation unit, an accuracy calculation unit, and a smoothed value calculation unit, utilizing a Kalman filter process to estimate and smooth target states, even in non-uniform linear motion scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative calculation is used to handle non-linear bearing-target relationship, then target state estimation accuracy is improved, but calculation amount increases
Solution Approach 1:
The patent transforms the non-linear bearing-target relationship into a linear relationship by changing the parameter representation. Instead of directly using bearing angles in iterative calculations, the invention introduces auxiliary parameters (such as range rates and relative position components) that linearize the observation equations, enabling direct calculation without iteration while maintaining estimation accuracy.
Solution Approach 2:
The patent replaces the iterative mechanical calculation process with a direct algebraic solution. By substituting the iterative numerical method with a closed-form solution based on linearized equations, the system eliminates the need for repeated calculations while preserving the essential estimation function.
2Measurement precision
If iterative calculation is used to handle non-linear bearing-target relationship, then target state estimation is achieved, but solution stability deteriorates
Solution Approach 1:
The patent changes the parameterization of the problem from bearing angles directly to a set of linearized parameters that include range, range rates, and relative position components. This parameter transformation stabilizes the solution by avoiding the numerical instability inherent in iterative methods for non-linear systems.
Solution Approach 2:
The patent introduces intermediate parameters as mediators between the bearing measurements and the target state. These intermediate parameters (such as relative position components and range rates) serve as a stable bridge that linearizes the relationship and prevents solution instability.
3Device complexity
If pseudo linear problem approach is used, then calculation complexity is reduced, but adaptability to non-uniform linear motion deteriorates
Solution Approach 1:
The patent makes the estimation system dynamic by incorporating time-varying parameters and multiple time-window processing. Instead of assuming uniform linear motion, the system adapts to non-uniform motion by processing bearing data from multiple time windows and using dynamic parameter updates, thereby maintaining both simplicity and adaptability.
Solution Approach 2:
The patent segments the motion analysis into multiple time windows, each processed independently with the pseudo-linear approach. By dividing the overall estimation problem into smaller temporal segments, the system can handle non-uniform motion through piecewise linear approximation while maintaining calculation simplicity within each segment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and responsive target state estimation regardless of the target's motion type, enhancing estimation accuracy and reducing calculation complexity.
Implementation Method 1
a smoothed value calculation unit to predict the state of the target by performing a Kalman filter process on the estimated state value output from the estimated state value calculation unit
Data Source
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AI summary
A target state estimation device includes: a calculated bearing value acquisition unit (11) that acquires a calculated bearing value indicating a bearing of a target, every time a period corresponding to a time window elapses; an estimated state value calculation unit (14) that estimates a state of the target using a calculated bearing value acquired by the calculated bearing value acquisition unit (11), and outputs an estimated state value indicating a result of the state estimation; an accuracy calculation unit (15) that calculates an accuracy of the estimated state value output from the estimated state value calculation unit (14); and a smoothed value calculation unit (16) that predicts the state of the target by performing a Kalman filter process on the estimated state value output from the estimated state value calculation unit (14), using the accuracy calculated by the accuracy calculation unit (15), and calculates a smoothed value indicating the state of the target, using a predicted state value indicating a result of the state prediction.