Line of sight (LOS) attitude determination and calibration is the key prerequisite of tracking and location of targets in space based infrared (IR) surveillance systems (SBIRS) and the LOS determination and calibration of staring sensor is one of the difficulties. This paper provides a novel methodology for removing staring sensor bias through the use of Ground Control Points (GCPs) detected in the background field of the sensor. Based on researching the imaging model and characteristics of the staring sensor of SBIRS geostationary earth orbit part (GEO), the real time LOS attitude determination and calibration algorithm using landmark control point is proposed. The influential factors (including the thermal distortions error, assemble error, and so on) of staring sensor LOS attitude error are equivalent to bias angle of LOS attitude. By establishing the observation equation of GCPs and the state transition equation of bias angle, and using an extend Kalman filter (EKF), the real time estimation of bias angle and the high precision sensor LOS attitude determination and calibration are achieved. The simulation results show that the precision and timeliness of the proposed algorithm meet the request of target tracking and location process in space based infrared surveillance system.
The imagery vendors of the most advanced remote sensing satellites usually only provide the coefficients of rational function model (RFM) to replace the sensor model and the precise imaging parameters (orbit parameter, attitude parameter, and so on). So, the rigorous imaging model was limited to use in the geometric correction of remote sensing image. The RFM method could obtain a better correction performance in most cases. However, when the image contains few numbers or uneven distribution of ground control points (GCPs), such as infrared image, the RFM method could not obtain the expected performance. Therefore, a geometric correction method for linear pushbroom infrared imagery using compressive sampling (CS) is proposed. The core idea of the proposed method is to use the equivalent bias angles to approximate the influence of the errors (thermal distortion, optical distortion, assembly error, satellite orbit errors, attitude errors, and so on) in the imaging process and adopt the CS method to recover the equivalent bias angle signals. Most of the data are processed scene by scene with enough GCPs for each scene in conventional methods. This restriction is broken by using the sparsity of equivalent bias angle signals in the proposed method. The infrared images from the Hyperion of EO-1 are used as experiment data, and the results of experiments demonstrate the feasibility and superior performance of proposed method.
This paper provides a novel methodology for removing sensor bias from a space based infrared (IR) system (SBIRS) through the use of stars detected in the background field of the sensor. Space based IR system uses the LOS (line of sight) of target for target location. LOS determination and calibration is the key precondition of accurate location and tracking of targets in Space based IR system and the LOS calibration of scanning sensor is one of the difficulties. The subsequent changes of sensor bias are not been taking into account in the conventional LOS determination and calibration process. Based on the analysis of the imaging process of scanning sensor, a theoretical model based on the estimation of bias angles using star observation is proposed. By establishing the process model of the bias angles and the observation model of stars, using an extended Kalman filter (EKF) to estimate the bias angles, and then calibrating the sensor LOS. Time domain simulations results indicate that the proposed method has a high precision and smooth performance for sensor LOS determination and calibration. The timeliness and precision of target tracking process in the space based infrared (IR) tracking system could be met with the proposed algorithm．