Low Earth orbit (LEO) satellites located at altitudes of 500 km~1500 km can carry much stronger signals and move faster than medium Earth orbit (MEO) satellites at about a 20,000 km altitude. Taking advantage of these features, LEO satellites promise to make contributions to navigation and positioning where global navigation satellite system (GNSS) signals are blocked as well as the rapid convergence of precise point positioning (PPP). In this paper, LEO-based optimal global navigation and augmentation constellations are designed by a non-dominated sorting genetic algorithm III (NSGA-III) and genetic algorithm (GA), respectively. Additionally, a LEO augmentation constellation with GNSS satellites included is designed using the NSGA-III. For global navigation constellations, the results demonstrate that the optimal constellations with a near-polar Walker configuration need 264, 240, 210, 210, 200, 190 and 180 satellites with altitudes of 900, 1000, 1100, 1200, 1300, 1400 and 1500 km, respectively. For global augmentation constellations at an altitude of 900 km, for instance, 72, 91, and 108 satellites are required in order to achieve a global average of four, five and six visible satellites for an elevation angle above 7 degrees with one Walker constellation. To achieve a more even coverage, a hybrid constellation with two Walker constellations is also presented. On this basis, the GDOPs (geometric dilution of precision) of the GNSS with and without an LEO constellation are compared. In addition, we prove that the computation efficiency of the constellation design can be considerably improved by using master–slave parallel computing.

A method of using Interrupted-Sampling Repeater Jamming (ISRJ) to cancel radar target echo is proposed. After the principles of ISRJ are expounded, three conditions that should be met for radar echo cancellation are proposed, including range synchronization, phase coherent and amplitude match. Then, the restriction for key parameters of ISRJ, i.e. the repeater power, the delay time and frequency of repeater are deduced in theory. At last, the influences of those parameters on radar echo cancellation are discussed in detail. Some numerical results are presented to verify the effectiveness of this method. This work is beneficial to the jamming design and use of ISRJ.

High-performance packet classification algorithms have been widely studied during the past decade. Bit-Vector-based algorithms proposed for FPGA can achieve very high throughput by decomposing rules delicately. However, the relatively large memory resources consumption severely hinders applications of the algorithms extensively. It is noteworthy that, in the Bit-Vector-based algorithms, stringent memory resources in FPGA are wasted to store relatively plenty of useless wildcards in the rules. We thus present a memory-optimized packet classification scheme named WeeBV to eliminate the memory occupied by the wildcards. WeeBV consists of a heterogeneous two-dimensional lookup pipeline and an optimized heuristic algorithm for searching all the wildcard positions that can be removed. It can achieve a significant reduction in memory resources without compromising the high throughput of the original Bit-Vector-based algorithms. We implement WeeBV and evaluate its performance by simulation and FPGA prototype. Experimental results show that our approach can save 37% and 41% memory consumption on average for synthetic 5-tuple rules and OpenFlow rules respectively.

This paper proposes a new clustering algorithm which integrates Fuzzy C-means clustering with Markov random field (FCM). The density function of the first principal component which sufficiently reflects the class differences and is applied in determining of initial labels for FCM algorithm. Thus, the sensitivity to the random initial values can be avoided. Meanwhile, this algorithm takes into account the spatial correlation information of pixels. The experiments on the synthetic and QuickBird images show that the proposed method can achieve better classification accuracy and visual qualities than the general FCM algorithm.

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