Passive Optical System Using Virtual Twins for Long Range Trajectory Tracking
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Solution Overview
Problem
Passive ranging techniques face challenges in accurately determining the trajectory of targets at long ranges due to limited accuracy of bearing and position data, which is probabilistic and prone to errors, and are deceived by similar shapes of targets, requiring more computational complexity and time, limiting their applicability in military and commercial applications.
Innovation Solution
A passive optical system using virtual twins to create virtual baselines for real-time target motion estimation, independent of specific target shapes, by launching a stream of virtual twins and employing stochastic filtering to iteratively improve target state vectors and predict trajectories over long ranges, even when non-linear, without emitting mechanical or electromagnetic waves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If passive ranging techniques are used to determine target trajectory at long range, then measurement cost and power consumption are reduced, but measurement precision and reliability deteriorate due to limited accuracy of bearing and position data
Solution Approach 1:
The patent creates a virtual twin copy of the ownship platform that replicates its position, orientation, and motion characteristics. This virtual copy serves as a computational baseline to enhance the accuracy of target position and trajectory measurements without requiring additional physical sensors or increasing power consumption, thus resolving the contradiction between low power usage and measurement precision.
2Measurement precision
If virtual twins are launched and stochastic filtering is applied to iteratively improve target state vectors, then measurement precision and trajectory prediction accuracy are improved, but device complexity and computational requirements increase
Solution Approach 1:
The virtual twin system performs self-updating by automatically incorporating ownship motion data and measurement corrections into its state vector without external intervention. The stochastic filtering algorithm iteratively refines target trajectory estimates using the virtual twin's predicted motion, eliminating the need for complex manual calibration or external computational resources, thus improving measurement precision while controlling device complexity.
3Device complexity
If a single physical baseline is used for passive triangulation, then device complexity is reduced, but measurement precision deteriorates due to limited baseline length and probabilistic bearing measurements
Solution Approach 1:
The patent transitions from a single physical baseline to a virtual baseline by introducing a virtual twin dimension. The virtual twin replicates the ownship's position and orientation at different times, creating an extended baseline in the temporal-dimensions that significantly increases the effective baseline length for triangulation without adding physical sensors, thus improving measurement precision while maintaining low device complexity.
4Ease of operation
If traditional passive ranging methods are used, then ease of operation is maintained, but productivity and processing speed decrease due to high computational complexity and time requirements
Solution Approach 1:
The virtual twin is pre-configured with the ownship's initial state and motion characteristics before target tracking begins. By pre-computing the virtual twin's trajectory predictions and preparing the stochastic filtering parameters in advance, the system reduces real-time computational requirements and accelerates target state vector updates, thus improving productivity while preserving ease of operation.
Data Source
AI summary
A passive optical system tracks and determines the trajectories of targets at long range. Potential targets are initially identified from video images from a moving platform (ownship). A state vector that includes the target bearing is calculated based on a time series of these video images. A number of “virtual twins” of the ownship are then launched by using a stochastic filter to generate updates of this state vector along a predetermined flight path continuing that of the ownship. After each launch, the flight path of the ownship is altered to thereby create a baseline separation. The trajectory of the target is estimated by triangulation based on the paths of the ownship and virtual twin, and the time series of bearing data from the ownship and virtual twin. By using frequent launches of virtual twins, the present system iteratively improves the target's predicted trajectory over long ranges.