Star extraction method for star sensor at a high angular rate
When a star sensor slews at a high angular rate, the stars in the sensed image are smeared out over many pixels, which results in a decreasing signal-to-noise ratio (SNR). Therefore, the faint stars are not capable of being detected, and the centroidal precision of the bright stars decrease when using traditional image threshold segmentation algorithm and centroid method. In this paper, we propose a method to obtain accurate star location for star sensor at a high angular rate. We first utilize Laplacian to segment the star image, and then each pixel is marked after it is compared with the neighbouring one. Finally, the centroids of stars are calculated by using target pixels, and the centroiding errors are compensated. The experiment results illustrate that the proposed technique can extract more stars than other traditional methods under the condition of high angular rate, and also can improve the locating precision of star effectively.
Modification and implementation of a triangle star pattern recognition algorithm
When the star positional error increases, the traditional triangle star identification algorithm has a higher redundancy and matching rate is slower. In order to overcome this problems, the implementation methods of triangle star pattern recognition algorithm is proposed. compared with such methods of other literature, this method has lower redundancy and faster matching. On the base of triangle star identification algorithm , a fourth measured star is used to decrease the number of redundancies. Finally, A verification identification is used to get one matching between observed star triangle and navigation star triangle when the positional noise is serious. Fly and numerous night sky experimental results show that the algorithm has higher rate of correct star pattern recognition, lower redundancy and faster matching, it is convenient for engineering application.
The Analysis on Fire Smoke Exhausting Robot Vibration Based on Wavelet Transform
Belt relaxation, which affects a lot on operation reliability of the fire smoke exhausting robot, is hard to detect. In this paper, a new method for belt relaxation feature recognition based on continuous wavelet transform (CWT) is proposed, and a vibration model which simplified the robot as a 2-dof forced vibration system under harmonic excitation is established. The vibration acceleration signal has been collected using the IEPE sensor and data acquisition card, experimental results verified the accuracy of the vibration model, and weak impact signal caused by the belt relaxation was distinguished, that testify the practicability of the CWT method in belt relaxation feature recognition.