Deep Neural Network-Based 4-Quadrant Analog Sun Sensor Calibration

Many error sources influence the calibration experiment of 4-quadrant sun sensors,making the calibration of sun sensors cumbersome and its accuracy difficult to improve.Any continuous function on a bounded closed set can be approximated by a deep neural network.This paper uses the deep neural network model to approximate the error model.The data-driven training network is adopted to continuously modify the model parameters to fit the error compensation model and ensure that the accuracy reaches the target requirements after calibration.Considering that the deep neural network model needs a considerable amount of data,the neural network model training is divided in 2 stages.In the preliminary stage,cubic surface fitting is used to generate a dataset,which is small in size and controllable.After the completion of the initial training,the experimental data are used to fine-tune the model to achieve error compensation.The accuracy can be improved from 1°(1σ)to 0.1°(1σ)after the incident angle of the sun sensor is corrected.The error compensation model eliminates the loss of accuracy caused by the distortion of light spots at the edge of the field angle and provides a favorable condition for the expansion of the field angle of the 4-quadrant analog sun sensor.

Space Noncooperative Target Trajectory Tracking Based on Maneuvering Parameter Estimation

The space noncooperative target maneuvering trajectory tracking is essential for the safety of the on-orbit spacecraft.For the noncooperative target,the maneuvering model is complex and changeable.Besides,the maneuvering dynamics model,the operation time,and the maneuvering frequency are previously unknown.It is difficult to achieve high-precision maneuvering trajectory tracking.In this paper,a novel real-time maneuvering trajectory tracking algorithm is developed,in which the maneuvering trajectory of the noncooperative target is discretized first,and then the differential algebra method is utilized to estimate the maneuvering parameter of the noncooperative target in the discretized time.Since the discretized period is very short,the maneuvering parameters of the target in the next discretized time are assumed to be the same as those in the previous discretized time,and the estimated maneuvering parameters are utilized to predict the target’s relative state in the next discretized time to achieve maneuvering trajectory tracking.Compared with the interactive multimodel method(IMM),the proposed method can estimate the maneuvering parameter of the noncooperative target in real time,which greatly reduces the tracking error caused by the mismatching of the target’s maneuvering model.In order to verify the effectiveness of the algorithm,a simulation of a noncooperative target’s maneuvering trajectory tracking is provided.The results demonstrated that the proposed method could track the noncooperative target maneuvering in real time,and the estimation accuracy was improved by about 93.07%compared with the IMM.