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Institution

National University of Defense Technology

EducationChangsha, China
About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Radar & Synthetic aperture radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.


Papers
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Journal ArticleDOI
TL;DR: In this article, an improved 8-position rotation scheme and a novel 16 position rotation scheme are proposed for the optical gyro inertial navigation system (INS), and their merits are also discussed.
Abstract: IMU rotation of an inertial navigation system (INS) can bound the free propagation of the INS error introduced by the drifts of inertial sensors. The rotation scheme of the IMU will directly affect the accuracy, structure and costs of the system. A reasonable rotation scheme should remove most of the system errors arising from the drifts of the inertial sensors, and at the same time, should not introduce other additional errors. First, this paper discusses the design and analysis approach of the rotation scheme based on the error propagation equations of the INS. Then, a conventional 8-position rotation scheme is analyzed for the applications of the optical gyro INS, and its drawbacks are discussed in detail. Following these, an improved 8-position rotation scheme and a novel 16-position rotation scheme are proposed for the optical gyro INS, and their merits are also discussed. Simulation results have shown that the 16-position rotation scheme, which can compensate not only the drifts but also the scale factor errors and the misalignment errors of the inertial sensors, is the best rotation scheme and can be used as a practical solution to compensate the drifts of the inertial sensors in the rotational INS.

99 citations

Journal ArticleDOI
TL;DR: This letter proposes a Parzen-window-kernel-based algorithm for ship detection in synthetic aperture radar (SAR) images that uses the data-driving kernel functions of Parzen window to approximate the histogram of real SAR image.
Abstract: This letter proposes a Parzen-window-kernel-based algorithm for ship detection in synthetic aperture radar (SAR) images. First, the data-driving kernel functions of Parzen window are utilized to approximate the histogram of real SAR image, in order to complete the accurate modeling of SAR images. Then, a threshold of global constant false alarm rate is given theoretically, and the numerical solution of the threshold is also derived. The experimental results of the real data of typical targets demonstrate that the algorithm presented is effective.

99 citations

Journal ArticleDOI
TL;DR: In this article, the fabrication and properties of the third generation SiC fibers are compared and discussed and their future development is prospected, while their preparation processes and their application in aeroengine and nuclear energy fields are summarized.

99 citations

Journal ArticleDOI
TL;DR: This paper presents a fault-tolerant aircraft control (FTAC) scheme against actuator faults, where the upper bounds of the norms of the unknown functions are introduced and self-constructing fuzzy neural networks with adaptive laws are capable of obtaining the bounds.
Abstract: This paper presents a fault-tolerant aircraft control (FTAC) scheme against actuator faults. First, the upper bounds of the norms of the unknown functions are introduced, which contain actuator faults and model uncertainties. Subsequently, self-constructing fuzzy neural networks (SCFNNs) with adaptive laws are capable of obtaining the bounds. The bound estimation can reduce the computational burden with a lower amount of rules and weights, rather than the dynamic matrix approximation. Moreover, with the aid of SCFNNs, a multivariable sliding mode control (SMC) is developed to guarantee the finite-time stability of the handicapped aircraft. As compared to the existing intelligent FTAC techniques, the proposed method has twofold merits: fault accommodation can be promptly accomplished and decoupled difficulties can be overcome. Finally, simulation results from the nonlinear longitudinal Boeing 747 aircraft model illustrate the capability of the presented FTAC scheme.

99 citations

Journal ArticleDOI
TL;DR: This paper develops an adaptively internal goal/reward representation for the agent with the proposed goal network and keeps the actor-critic design in heuristic dynamic programming (HDP) and includes a goal network to represent the internal goal signal to further help the value function approximation.
Abstract: Goal representation heuristic dynamic programming (GrHDP) is proposed in this paper to demonstrate online learning in the Markov decision process. In addition to the (external) reinforcement signal in literature, we develop an adaptively internal goal/reward representation for the agent with the proposed goal network. Specifically, we keep the actor-critic design in heuristic dynamic programming (HDP) and include a goal network to represent the internal goal signal, to further help the value function approximation. We evaluate our proposed GrHDP algorithm on two 2-D maze navigation problems, and later on one 3-D maze navigation problem. Compared to the traditional HDP approach, the learning performance of the agent is improved with our proposed GrHDP approach. In addition, we also include the learning performance with two other reinforcement learning algorithms, namely Sarsa(λ) and Q-learning, on the same benchmarks for comparison. Furthermore, in order to demonstrate the theoretical guarantee of our proposed method, we provide the characteristics analysis toward the convergence of weights in neural networks in our GrHDP approach.

99 citations


Authors

Showing all 39659 results

NameH-indexPapersCitations
Rui Zhang1512625107917
Jian Li133286387131
Chi Lin1251313102710
Wei Xu103149249624
Lei Liu98204151163
Xiang Li97147242301
Chang Liu97109939573
Jian Huang97118940362
Tao Wang97272055280
Wei Liu96153842459
Jian Chen96171852917
Wei Wang95354459660
Peng Li95154845198
Jianhong Wu9372636427
Jianhua Zhang9241528085
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
202397
2022468
20212,986
20203,468
20193,695