Photoelectric Theodolite Sensor Fusion for Stable High-Speed Tracking
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Solution Overview
Problem
Existing photoelectric theodolites face challenges in stable automatic tracking of high-speed moving targets due to manual data selection and unsmooth transitions between sensors, leading to potential target loss and instability in operation.
Innovation Solution
An autonomous fusion tracking method that includes real-time reading of sensor measurements, least square extrapolation, error covariance calculation, and a tri-state discrimination model to dynamically adjust sensor weighting factors and states for stable tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data selection is used to track targets, then the operator can select data with highest image clarity, but the switching process is heavily dependent on operator experience and difficult to meet automatic operation requirements
Solution Approach 1:
The system automatically evaluates and selects sensor data based on predefined quality metrics (image clarity, signal strength, noise level) without requiring manual operator intervention. The data quality evaluation module autonomously determines which sensor provides the best measurement quality and switches between sensors automatically, making the system self-sufficient in data selection tasks.
Solution Approach 2:
The system continuously monitors data quality metrics from multiple sensors and uses this feedback to dynamically adjust sensor selection and weighting. The evaluation results are fed back to the tracking control module in real-time, enabling automatic adaptation to changing target conditions and ensuring consistent measurement quality without manual intervention.
2Measurement precision
If sensor switching is performed manually, then the operator can select the best sensor, but the target is easily lost if the switch is not timely
Solution Approach 1:
The system pre-evaluates data quality metrics for all available sensors before switching is needed, maintaining a ready list of alternative sensors with their current quality assessments. This preliminary preparation ensures that when a sensor needs to be switched, the system can immediately transition to the pre-identified best alternative without delay, preventing target loss.
Solution Approach 2:
The system continuously monitors and evaluates data quality from all sensors simultaneously, maintaining uninterrupted tracking by always having a ready backup sensor with evaluated metrics. The continuous evaluation ensures that switching can occur at any moment without breaking the tracking chain, maintaining continuous useful action in target acquisition.
3Measurement precision
If conventional fusion tracking methods are used in isolation, then algorithms exist for adaptive weighting, but they fail to combine with photoelectric theodolite tracking system and cannot reflect data quality in real-time
Solution Approach 1:
The data quality evaluation module serves multiple functions: it assesses sensor performance for fusion weighting, provides feedback for automatic sensor selection, and generates metrics for tracking performance monitoring. This multi-functional design allows the same core algorithm to address multiple system requirements without proportionally increasing complexity.
Solution Approach 2:
The patent introduces a data quality evaluation module as an intermediary between raw sensor data and the fusion tracking algorithm. This intermediary processes and standardizes data from multiple sensors, providing cleaned and evaluated inputs to the fusion algorithm, thereby facilitating integration without requiring fundamental changes to either the sensors or the core tracking system.
4Reliability
If multiple sensors with different parameters are integrated, then detection capability is improved, but unsmooth transition of measurement data affects stable operation
Solution Approach 1:
The system dynamically adjusts sensor weighting factors and selection based on real-time data quality evaluation rather than using fixed parameters. The fusion weights are continuously updated according to current sensor performance metrics, allowing the system to adapt to changing conditions and maintain stable operation despite using multiple sensors with different parameters.
Solution Approach 2:
The system changes operational parameters (sensor weighting factors, active sensor selection) based on evaluated data quality metrics. By dynamically adjusting these parameters according to real-time sensor performance, the system smooths transitions between sensors and maintains stable tracking operation, eliminating the instability caused by rigid parameter configurations.
Data Source
AI summary
An autonomous fusion tracking method based on sensors of a photoelectric theodolite includes steps of calculating least square extrapolation values of each of the sensors at a current moment and average values of the measurement values of the sensors at the current moment; substituting the least square extrapolation values respectively into improved error covariance recursive formulas, and calculating error covariances of each of the sensor at the current moment in real time based on the improved error covariance recursive formulas, and calculating weighting factors of each of the sensors at the current moment in real time according to the error covariances of each of the sensors at the current moment; constructing a tri-state discrimination model; and performing, by the photoelectric theodolite, autonomous fusion tracking on a to-be-measured target based on the tri-state discrimination model to obtain a tracking result. The method realizes an automatic operation of the photoelectric theodolite.