Angle-Only Tracking Filter for Rapidly Accelerating Targets
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Solution Overview
Problem
Conventional angle-only tracking filters perform poorly in estimating the position, velocity, and acceleration of rapidly accelerating targets, as they fail to accurately account for non-uniform accelerations.
Innovation Solution
The proposed solution involves using sample angle measurements and ownship kinematics, along with nth order kinematics equations, to generate target kinematics state vectors, including position, velocity, and acceleration, through a system comprising a target angle discriminant unit, ownship navigation filter, model analyzer, and target kinematics generator, which includes first-order, second-order, and third-order filter units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional angle-only tracking filters are used, then the system is simple to operate, but the measurement precision of target position, velocity, and acceleration deteriorates for rapidly accelerating targets
Solution Approach 1:
The tracking filter is segmented into multiple independent filter units, each dedicated to estimating a specific kinematic parameter (position, velocity, acceleration). This segmentation allows each unit to specialize in handling non-uniform acceleration effects for its specific parameter, improving overall measurement precision without requiring a single complex filter to handle all parameters simultaneously.
Solution Approach 2:
The patent transitions from conventional two-dimensional angle-only measurements to three-dimensional Cartesian space estimation by introducing depth (range) estimation. The filter generates range, range rate, and range acceleration estimates in addition to position, velocity, and acceleration, effectively adding a dimensional transformation that enables accurate tracking of rapidly accelerating targets in 3D space.
2Reliability
If conventional MSC filters are used, then the filter structure is simple, but the reliability of tracking rapidly accelerating targets deteriorates
Solution Approach 1:
The filter system dynamically adapts to the target's motion characteristics by processing angle measurements through multiple filter units that can handle non-uniform acceleration. Each filter unit dynamically updates its estimates based on current measurements and previous states, allowing the system to reliably track targets with varying acceleration patterns rather than assuming constant or linear motion.
Solution Approach 2:
The patent introduces an intermediary coordinate transformation process that converts angle-only measurements into Cartesian space estimates. This intermediary transformation layer, combined with the multiple filter units, acts as a mediator that bridges the gap between simple angle measurements and reliable 3D kinematic estimation, improving tracking reliability without requiring direct complex modeling of target dynamics.
3Measurement precision
If nth order kinematics equations are incorporated, then the measurement precision for rapidly moving targets improves, but the computational complexity increases
Solution Approach 1:
The computational workload of nth order kinematics equations is segmented across multiple specialized filter units. Each unit processes a specific aspect of the kinematics (position, velocity, acceleration) using appropriate order equations, distributing the computational burden rather than requiring a single high-order filter to handle all calculations simultaneously, thus reducing peak computational power requirements.
Solution Approach 2:
The system changes the parameters being estimated by focusing on Cartesian space coordinates (range, position, velocity, acceleration) rather than traditional spherical coordinates. This parameter transformation allows the use of nth order kinematics equations to be applied more efficiently through the coordinate transformation approach, improving measurement precision for rapidly moving targets while managing computational complexity through the structured parameter changes.
Data Source
AI summary
An angle-only tracking filter includes: a target angle discriminant unit configured to receive sensor signal outputs and form angle only observations of a target relative to an ownship; an ownship navigation filter configured to receive and filter ownship inertial navigation measurements; a model analyzer configured to receive and analyze the ownship inertial navigation measurements and select the order of target kinematics to be determined; and a target kinematics generator coupled to the angle discriminant unit, the navigation filter unit, and the model analyzation unit, including: a first-order filter unit configured to generate a target position from the target angle measurements and the ownship inertial navigation information; a second-order filter unit configured to generate a target velocity from the target angle measurements and the ownship inertial navigation information; and a third-order filter unit configured to generate a target acceleration from the target angle measurements and the ownship inertial navigation information.