An improved altimeter-derived gravity anomaly from shipborne gravity based on the mean sea surface height constraint factors method

Coastal marine gravity modeling faces challenges due to the degradation of the quality and poor coverage of altimeter data in coastal regions. The effective fusion of shipborne gravity data and altimeter-derived marine gravity data can make shipborne gravity data more useful for the accurate estimation of altimeter-derived coastal marine gravity. A mean sea surface height constraint factor (MSSHCF) method based on the ordinary kriging method and the remove-restore technique is proposed to fuse altimeter-derived gravity model with shipborne gravity data. In this method, all data are standardized during the interpolation process to reduce the error and mean sea surface as a vertical variable is added to the semi-variance function in ordinary kriging to obtain the residual shipborne gravity as corrected data source. The coastal marine gravity models V2.1 and V3.1 which fused altimeter-derived gravity data with shipborne gravity data and V1.1 without shipborne gravity data at a spatial resolution of 1′×1′ can be obtained. Validation experiments show that the accuracy of the gravity model V3.1 obtained by the MSSHCF method more closely agrees with the validated gravity model DTU17 and SS V31 than the model V2.1 obtained by the ordinary kriging interpolation method and the V1.1 model. Our results were validated against shipborne gravity data; the accuracy of model V3.1 was 4.95 % higher than the model V1.1 in South China Sea area A and 2.48 % higher in South China Sea area B. Meanwhile, the accuracy of model V3.1 was 2.07 % higher than model V2.1 in South China Sea area A and 2.42 % higher in South China Sea area B. The effects of distance from the coast and sea depth on the marine gravity model were also evaluated. The results show that the gravity model V3.1 has higher accuracy with the change in ocean distance and depth than the V2.1 and V1.1 gravity models. Thus, our study shows that the MSSHCF method effectively refines coastal altimeter-derived gravity using shipborne gravity data.     

Formation control for networked multiagent systems with a minimum energy constraint

Minimum-energy formation achievement problems for networked multiagent systems are investigated, where information networks with leaderless and leader-follower structures are respectively addressed and information networks are randomly switching. The critical feature of this work is that the energy constraint is minimum in the sense of the linear matrix inequality, but limited-budget control and guaranteed-cost control cannot realize a minimum-energy formation. Firstly, the leaderless minimum-energy formation control problem is converted into an asymptotic stability one via a nonsingular transformation and state space decomposition, and based on linear matrix inequality techniques, sufficient conditions for analysis and design of leaderless minimum-energy formation achievement are proposed, respectively, which can be solved by the generalized eigenvalue method. Then, main results of minimum-energy formation achievement of leaderless networked multiagent systems are extended leader-follower networked multiagent systems, where the asymmetric property of the leader-follower information network is well dealt with by two nonsingular transformations. Finally, two simulation examples are shown to verify the main results for minimum-energy formation achievements of leaderless and leader-follower networked multiagent systems, respectively.     

基于惯性递推原理的行人自主定位方法综述及展望

在室内环境或者卫星信号遮挡环境下,如何实现单兵或者行人的自主定位是目前研究的热点问题.其中,基于惯性递推原理的行人自主定位方法由于无需提前布设基础设施,且具有体积小、成本低、完全自主、不受干扰、实时性高等特点,因此具有广泛的研究价值和意义.本文围绕基于惯性递推原理的行人自主定位方法展开介绍,针对动态步态分析、零速区间检测、惯性器件误差约束、运动误差约束方法和融合算法等方面技术进行了综述.最后,对基于惯性递推原理的行人自主定位方法的发展方向进行了展望.     

基于状态改变的机器人路径规划算法

为了解决目前机器人路径规划中时效性低、避障解算量大的问题,借鉴直流电路中理想电感元件两端电压能够从感抗状态恢复到稳定状态的现象,提出了一种利用机器人状态检测量对机器人状态实时检测,从而实现避障的机器人路径规划算法.首先对绕行单个固定障碍物的机器人进行深入分析,然后采用状态叠加的方法,生成任意位置多障碍物同时存在的避障路径.同时对斥力系数进行改进,以确保机器人能够有效到达目标点.为了验证算法在多障碍物环境下的路径规划能力,进行了数值仿真模拟实验.仿真结果表明,使用该算法规划机器人路径时,能够在避免局部极小值点和目标不可达问题的前提下,在较短时间内规划出机器人由起始点到目标点的路径.