Carried on the deck of the satellite maritime tracking and control ship, Yuan Wang 6, we have conducted a long-term on-ship dynamic experiment for a star sensor in the South Pacific. Motion-blurred star images of the star sensor obtained under different dynamic conditions are processed by our previously proposed region confined restoration method method, after which the SNR of the motion-blurred star images and the identification rate of the star sensor have been improved remarkably. With the attitude-correlated frames approach, the random noise aroused by the motion of the ship is reduced further. As a result, considering the accuracy and star observation length of the ship-borne star sensor, we believe reported for the first time, a three-axis attitude accuracy of better than 5 arcseconds is obtained in our on-ship experiment.

Pulsed laser range-gated imaging (PLRGI) is one of the most effective methods to achieve underwater high-resolution imaging. When searching target, there are two methods can be used: fixed gate and sliding gate. In practice, fixed gate has a small depth of field of view and sliding gate cannot meet the need of real time. In order to overcome these problems, multi-slice integration (MSI) method is proposed in this paper. First, the laser energy received by the PLRGI system is derived from radiative transfer theory. In addition, range intensity profiles (RIP) of MSI method is established. Experiments are carried out in lab to validate the RIP model and results show that theoretical data and experimental data are in good coincidence. Then the gate width and the number of pulse assigned to each slice are discussed. Finally, to prove the effectiveness of the MSI method, experiments are carried out in a boat tank. Results show that the MSI method is better than the fixed gate method for surveillance, and can acquire a clear image of the target at 18 m (4.5 times of the attenuation length) in the water with attenuation coefficient of 0.25 m⁻¹.

Compared to conventional floodlighted imaging systems, a pulsed laser range-gated imaging system can get a real-time high performance underwater image when the distance of target is known. Otherwise, optical properties of the water, mainly the attenuation coefficient of the water, should be obtained to automatically set the parameters of the imaging system. Typically, special instrument is required to measure the attenuation coefficient of the water. In this work, a water attenuation coefficient estimation method is proposed merely with the pulsed laser range-gated imaging system. The imaging model of the backscattered light of the pulsed laser range-gated imaging system is built through the light propagation theory. As a result, the water attenuation coefficient is calculated by nonlinear estimation method. Experiments under different water conditions are designed and carried out to verify the proposed method. Results show that the relative errors between the estimation and the reference are about 1% for the water in the water tank and seawater pool, and about 4% for the towing boat tank, respectively.

The multi-slice integration (MSI) method is one of the approachs to extend the depth of view (DOV) of the pulsed laser range-gated imaging (PLRGI) system. When the DOV is large enough and exceeds the depth of focus of the system, it may make some targets in the image clear and others blurred. In addition, forward scatter is also considered to have a blurring effect on the image. There is very little literature to solve the combined effect of forward scatter and defocus. An imaging model is built based on the model from Jaffe–McGlamery and Fourier optics. According to the imaging model, backscattered light is independent from reflected light from the target, and forward scatter has a relationship with the reflected light. Thus, backscattered light should be removed before deblurring. First, rolling ball and intensity transformation are used to remove the backscattered light and enhance the image. Then, a deep learning model based on Transformer is used to deblur the image. To enable the deep learning model to accommodate different degrees of blurred image, 16 different blur kernels are generated according to the imaging model. Sharp images from a DPDD dataset were chosen to train the model. Images of varying degrees of blur were collected from a water tank and a boat tank by the PLRGI system as test sets. Image deblurring results show that the proposed method can remove different levels of blur and can deal with images which have sharp targets and blurred targets together.

Star sensors undergo laboratory calibration before they leave the factory. In addition, recalibration is necessary after they experience vibration, deformation, etc. Using the analysis of attitude-dependent and attitude-independent interstar angular invariance calibration methods (IAICMs) as a reference, an attitude-correlated frame-based calibration method (ACFCM) is proposed in this work, which combines the advantages of both methods. Using outdoor star observations, the ACFCM correlates star image frames obtained at different times via a strapdown gyro unit. As a result, the number of efficient star images for calibration increases rapidly and the distribution of star images becomes much more uniform, thus improving the calibration accuracy of the star sensor. A simulation and experimental tests were designed and carried out. Both the simulation and experimental results verify the feasibility of the proposed ACFCM method. Furthermore, by comparing our method with the IAICMs, the repeatability and reliability of the principal point obtained from the calibration with the ACFCM method proposed in this work were significantly improved.