摘要 :
Battlefield situational awareness is the core condition that determines the success or failure of the battlefield, and it is also an important application direction of photodetectors. The rapid development of AI technology in rece...
展开
Battlefield situational awareness is the core condition that determines the success or failure of the battlefield, and it is also an important application direction of photodetectors. The rapid development of AI technology in recent years is about to cause major changes in future wars. The new AI battlefield will also put forward new urgent needs for situational awareness. This article summarizes the current main modes of collaborative detection of battlefield situation awareness and its research status, including radar / infrared composite detection, multi-source data fusion of radar / infrared detection, cooperative target recognition, target tracking, etc. On this basis, combined with the current development trend of the intelligence level of the main battlefield equipment, we get the development needs of future intelligent battlefield situational awareness for new types of collaborative detection, including requirements for its style, angle, speed, and detection targets of distributed collaborative detection. Based on this, the key development directions and core issues to be solved for intelligent battlefield situational awareness in the future are proposed.
收起
摘要 :
Smoother is a method of improving filtering accuracy more. Standard Gaussian filter and Gaussian smoother formulation is presented in this article. With respect to high dimensional state space model, CKF is an algorithm design wit...
展开
Smoother is a method of improving filtering accuracy more. Standard Gaussian filter and Gaussian smoother formulation is presented in this article. With respect to high dimensional state space model, CKF is an algorithm design with more superiority. Its high stable style of square root CKF is depicted, accordingly square root FI-CKS theory derivation and the flow are deduced. Then the proposed smoother is applied to INS/GPS integrated navigation with large misalignment. The track of INS is first generated, and then SR-CKF, SR-FI-CKS is applied at simulated INS data, under the same condition, making a comparison with UKF, URTSS, FI-EKS, CKF and FI-CKS. The outcomes indicate that FI-EKS diverges while definite sampled nonlinear smoothers are convergent. Especially SR-FI-CKS operates with smoothly and stability, and gives out the highest accuracy on the performance of longitude and latitude estimation.
收起
摘要 :
The method of decentralized estimation is one that is fulfilled through decomposition of state space. Target tracking model with turning angular unknown has both nonlinear state function and measure function. Regular particle filt...
展开
The method of decentralized estimation is one that is fulfilled through decomposition of state space. Target tracking model with turning angular unknown has both nonlinear state function and measure function. Regular particle filter and UKF are both divergent through simulation. Then decentralized particle filter method was used by dividing state and state function into two parts accordingly, through decoupling correlated noise of state function, designing importance function, the two parts were estimated by particle filter separately. The outcome illustrated indicates it not only free of degeneracy, but also having a high accuracy of filtering, shortening time of filtering. At last, a decentralized estimation based on mixed nonlinear filter is addressed, which the weight of local level is not estimated.
收起
摘要 :
The method of decentralized estimation is one that is fulfilled through decomposition of state space. Target tracking model with turning angular unknown has both nonlinear state function and measure function. Regular particle filt...
展开
The method of decentralized estimation is one that is fulfilled through decomposition of state space. Target tracking model with turning angular unknown has both nonlinear state function and measure function. Regular particle filter and UKF are both divergent through simulation. Then decentralized particle filter method was used by dividing state and state function into two parts accordingly, through decoupling correlated noise of state function, designing importance function, the two parts were estimated by particle filter separately. The outcome illustrated indicates it not only free of degeneracy, but also having a high accuracy of filtering, shortening time of filtering. At last, a decentralized estimation based on mixed nonlinear filter is addressed, which the weight of local level is not estimated.
收起
摘要 :
The method of decentralized estimation is one that is fulfilled through decomposition of state space. Target tracking model with turning angular unknown has both nonlinear state function and measure function. Regular particle filt...
展开
The method of decentralized estimation is one that is fulfilled through decomposition of state space. Target tracking model with turning angular unknown has both nonlinear state function and measure function. Regular particle filter and UKF are both divergent through simulation. Then decentralized particle filter method was used by dividing state and state function into two parts accordingly, through decoupling correlated noise of state function, designing importance function, the two parts were estimated by particle filter separately. The outcome illustrated indicates it not only free of degeneracy, but also having a high accuracy of filtering, shortening time of filtering. At last, a decentralized estimation based on mixed nonlinear filter is addressed, which the weight of local level is not estimated.
收起
原文获取