Spacecraft cluster flight, which is a novel multi-spacecraft flight mode, has become an important research direction of distributed space system for the future, especially has unique advantage in continuous detection on area. Aiming at the satellite group formed by several satellite clusters, an orbit design method satisfying the long-term continuous and stable area-coverage is proposed. The basic dynamic model is built under the influence of J2 perturbation, the calculation method of "node period" and "node day" is put forward and then the long-term continuous stable group orbit initialization design conditions are built. Validation of orbit design is made under the typical scenario and through the STK simulation, the simulation results show that this design method can realize the fixed time revisiting and long-term stability of coverage of the target area.
The process of infrared images via computer-based algorithms for better application is a frontier field integrating physical technology with computer science. One of the key techniques in infrared image processing is the detection of infrared targets. This technique is extensively applied in security and defense systems and search and tracking systems. However, due to their small size, dim light and lack of texture, the detection of infrared targets is a technical problem. One strategy to address this problem is to transform the detection work into a non-convex optimization problem of recovering a low-rank matrix (background) and a sparse matrix (target) from a patch-image matrix (original image) based on IPI (infrared patch-image) model. When targets are clear and recognizable, the APG (accelerated proximal gradient) algorithm works effectively to solve it. However, when targets become much dimmer and are screened by the intricate texture of background, the experimental detection results degrade dramatically. In order to solve this problem, a novel method via IRNN (iteratively reweighted nuclear norm) is proposed in this paper. Experimental results show that under different complicated backgrounds, targets with higher SCRG (signal-to-clutter ratio gain) values and BSF (background suppression factor) values can be acquired through IRNN algorithm compared with the APG algorithm, which means that our method performs better.
Vessel detection has been widely used in Marine Surveillance to automatically detect potential threats over a huge oceanic area. However, the uncertainty of ship direction and the interference of environment factors, such as sea waves and cloud, will greatly reduce the detection accuracy. In order to solve the above problems and provide robustness to environmental conditions, we combine the property of polar-logarithmic coordinate and proposed our method named “Dual-operator log-pol top-hat filter (DOLPTH)” to make better use of the difference information between the vessel and the background. Different situations are designed to test the performance of our algorithm compared with other algorithms. The experimental results show that in both cases, DOLPTH can maintain high accuracy and has good detection performance.
Small target detection plays a crucial role in infrared warning and tracking systems. A background suppression method using morphological filter based on quantum genetic algorithm (QGMF) is presented to detect small targets in infrared image. Structure element of morphological filter is encoded and the best structure element is selected using quantum genetic algorithm. The optimized structure element is used for background suppression to detect small target. Experimental results demonstrate that QGMF has good performance in clutter suppression, and obtains higher signal-to-clutter ratio gain (SCRG) and background suppression factor (BSF) than the one using the fixed structure element with the same size.