Salvo Target Tracking Using Kinematic Probability Segmentation
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Solution Overview
Problem
Conventional target state estimator tracking in a salvo mission often results in track breaks due to lead objects being part of the acquired and tracked objects, making it difficult to achieve convergence of the target state.
Innovation Solution
A follow-on object is configured to track a target by initializing the track state within its sensor field-of-view, performing target-state estimator processing, and re-initializing the track state based on kinematic characteristics such as line-of-sight velocity and estimated range if the probability of the object being a lead object exceeds a threshold, thereby ensuring successful tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional target state estimator tracking is used in a salvo mission, then target tracking is performed, but track breaks occur due to lead objects being part of the acquired and tracked objects
Solution Approach 1:
The tracking system segments objects into different categories (lead objects versus valid targets) by evaluating kinematic characteristics and computing probabilities. This segmentation allows the system to apply different tracking strategies to different object types, preventing track breaks caused by misidentifying lead objects as valid targets.
Solution Approach 2:
The system performs preliminary evaluation of kinematic characteristics and computes the probability that a tracked object is a lead object before finalizing the tracking decision. This preliminary action allows the system to re-initialize track state proactively when a lead object is detected, preventing track breaks before they occur.
2Reliability
If lead objects are distinguished and track state is re-initialized, then track breaks are reduced, but additional processing steps are required
Solution Approach 1:
The system changes the parameter being monitored from simple object detection to kinematic characteristic evaluation. By computing probabilities based on kinematic parameters (such as velocity, acceleration, and trajectory), the system can automatically distinguish lead objects from valid targets using mathematical thresholds, adding processing steps but maintaining systematic and automated operation.
3Measurement precision
If kinematic characteristics are evaluated to compute probability of lead object, then tracking accuracy is improved, but computational load increases
Solution Approach 1:
The system replaces complex mechanical or manual target identification processes with computational methods. By using algorithms to evaluate kinematic characteristics and compute probabilities, the system achieves high identification accuracy through mathematical processing rather than physical or manual analysis, shifting the computational load to processing power while maintaining precision.
Data Source
AI summary
A follow-on object for use in a salvo mission in which one or more lead objects (LO) and a follow-on object track a target. A track state of a tracked object within a sensor field-of-view (FOV) of the follow-on object is initialized. Target-state estimator (TSE) processing based on sensor measurements from the sensor FOV is performed to maintain the track state of the tracked object. Kinematic characteristics of the tracked object are evaluated based on the sensor measurements to compute a probability that the tracked object is an LO based on the evaluated kinematic characteristics. If the probability is not greater than a threshold, the tracked object is designated as the target and TSE processing is resumed. Otherwise, the tracked object is designated as an LO and the track state is re-initialized and the track of the LO is excluded from some intercept task considerations.