A Fusion Tracking Algorithm for Electro-Optical Theodolite Based on the Three-State Transition Model

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 17; p. 5847
• Main Authors: Zhang, Shixue, Wang, Houfeng, Song, Liduo, Li, Hongwen, Liu, Shuai
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 09.09.2024
• Subjects: Accuracy; Algorithms; Cameras; data fusion; fusion tracking; photoelectric theodolite; Photogrammetry; range measurement and control; Sensors; Systems stability; data fusion; fusion tracking; range measurement and control; photoelectric theodolite
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24175847

This study presents a novel approach to address the autonomous stable tracking issue in electro-optical theodolite operating in closed-loop mode. The proposed methodology includes a multi-sensor adaptive weighted fusion algorithm and a fusion tracking algorithm based on a three-state transition model. A refined recursive formula for error covariance estimation is developed by integrating attenuation factors and least squares extrapolation. This formula is employed to formulate a multi-sensor weighted fusion algorithm that utilizes error covariance estimation. By assigning weighted coefficients to calculate the residual of the newly introduced error term and defining the sensor's unique states based on these coefficients, a fusion tracking algorithm grounded on the three-state transition model is introduced. In cases of interference or sensor failure, the algorithm either computes the weighted fusion value of the multi-sensor measurement or triggers autonomous sensor switching to ensure the autonomous and stable measurement of the theodolite. Experimental results indicate that when a specific sensor is affected by interference or the off-target amount cannot be extracted, the algorithm can swiftly switch to an alternative sensor. This capability facilitates the precise and consistent generation of data, thereby ensuring the stable operation of the tracking system. Furthermore, the algorithm demonstrates robustness across various measurement scenarios.