Science and Technology on Electronic optic Control Laboratory

Luoyang, China

Science and Technology on Electronic optic Control Laboratory

Luoyang, China

Time filter

Source Type

Fang J.,Yantai Naval Aeronautical and Astronautical University | Dai S.,Yantai Naval Aeronautical and Astronautical University | Xu W.,Nanhua University | Zou J.,Science and Technology on Electronic optic Control Laboratory | Wang Y.,Science and Technology on Electronic optic Control Laboratory
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | Year: 2016

The movement model of highly maneuvering hypervelocity-target is difficult to construct accurately, and the existence of bad measurements in tracking process may lead to filtering divergence. In order to deal with these problems, a tracking algorithm applicable to highly maneuvering hypervelocity-target is proposed. This algorithm derives a new strong tracking square-root cubature Kalman filter (ST-SRCKF) structure from the orthogonality principle, and introduces multiple fading factors. The solution and function position of fading factors are both different from original ST-SRCKF. According to the statistical characteristics of innovation that the trace of innovation covariance matrix is in a chi-square distribution, a modified CS-Jerk model is constructed. The model describes target movement more accurately. When the modified CS-Jerk model is combined with the modified ST-SRCKF, highly maneuvering hypervelocity-target is tracked with high precision. Simulation results show that the modified algorithm has better tracking performance for highly maneuvering hypervelocity-target. © 2016, Editorial Board of JBUAA. All right reserved.


Liu Y.,Luoyang Institute of Electro optical Equipment | Liu Y.,Science and Technology on Electronic optic Control Laboratory | Zhao Z.-Y.,Luoyang Institute of Electro optical Equipment | Zhao Z.-Y.,Science and Technology on Electronic optic Control Laboratory | And 4 more authors.
Xitong Fangzhen Xuebao / Journal of System Simulation | Year: 2012

Based on the optimal information fusion rule in the mean of linear minimum variance, a new kind of improved weighted fusion rule was proposed. In addition, combining multiscale analysis in wavelet domain, a kind of multi-sensor optimal information fusion in wavelet domain was built. It is the introduction of improved weighted fusion rule and multiscale decomposition, and the fusion performance, including precision and complexity, has been improved than matrix-weighted in time domain. An example of radar tracking illuminates its usefulness. © copyright.


Dong Z.,Beihang University | Dong Z.,Science and Technology on Electronic optic Control Laboratory | Chen Z.,Beihang University | Zhou R.,Beihang University | Zhang R.,Flight Automatic Control Research Institute
Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications, ICIEA 2011 | Year: 2011

This article is concerned with the UAV path re-planning problem to be solved by using a novel approach based on the hybrid of virtual force and A * search algorithm (HVFA). The formulation of the UAV path re-planning is presented, and the hybrid system model of virtual force (VF) is given. The scheme of path re-planning is designed and the corresponding computational complexity is analyzed quantitatively. Simulation results prove the feasibility and usefulness of using HVFA for UAV path re-planning. Performance comparisons between the HVFA method and the fuzzy virtual force (FVF) method demonstrate that the proposed approach is fast enough to meet the real-time requirements of the online path planning problems. © 2011 IEEE.


Duan H.B.,Beihang University | Yu Y.X.,Beihang University | Zhao Z.Y.,Science and Technology on Electronic Optic Control Laboratory
Science China Information Sciences | Year: 2013

With the improvement of the aircraft flight performance and development of computing science, uninhabited combat aerial vehicle (UCAV) could accomplish more complex tasks. But this also put forward stricter requirements for the flight control system, which are the crucial issues of the whole UCAV system design. This paper proposes a novel UCAV flight controller parameters identification method, which is based on predator-prey particle swarm optimization (PSO) algorithm. A series of comparative experimental results verify the feasibility and effectiveness of our proposed approach in this paper, and a predator-prey PSO-based software platform for UCAV controller design is also developed. © 2013 Science China Press and Springer-Verlag Berlin Heidelberg.


Dai H.,Yantai Naval Aeronautical and Astronautical University | Dai H.,Science and Technology on Electronic optic Control Laboratory | Lu J.,Yantai Naval Aeronautical and Astronautical University | Guo W.,Yantai Naval Aeronautical and Astronautical University | And 2 more authors.
Optik | Year: 2016

Accurate attitude information needed by the shipboard equipment is greatly influenced by the deformation of the deck. To solve this problem, some inertial measurement unit (IMU), which is composed by gyros and accelerators, are installed in the key battle points on the deck. The displacement of the key battle point will be measured by the IMU. Inspired by the rapid transfer alignment of inertial navigation system presented by the American scholar Kain, a novel deformation estimation Kalman filter is designed. The measurement model is designed by matching the output of the IMU and the main inertial navigation system of the ship. Then the Kalman filter is applied to estimate the accurate deformation of the key battle point online. The performance of the Kalman filter is influenced by the observability of the system. Observability analysis method named PWCS (piece-wise constant system) is applied for this new presented method. The efficiency of the presented method is proved by theoretical analysis and computer simulation. Simulation results also show that the presented method can estimate the deformation online successfully. © 2016 Elsevier GmbH. All rights reserved.


Xue Y.,Nanjing University of Aeronautics and Astronautics | Zhuang Y.,Nanjing University of Aeronautics and Astronautics | Ni T.,No.723 Institute of China Shipbuilding Industry Corporation | Ouyang J.,Nanjing University of Aeronautics and Astronautics | Wang Z.,Science and Technology on Electronic optic Control Laboratory
Journal of Systems Engineering and Electronics | Year: 2012

There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptive evolutionary algorithm (ESEA) to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA outperform its competitors.


Xia P.,Nanjing University of Aeronautics and Astronautics | Chen M.,Nanjing University of Aeronautics and Astronautics | Zou J.,Science and Technology on Electronic Optic Control Laboratory | Feng X.,Science and Technology on Electronic Optic Control Laboratory
Lecture Notes in Electrical Engineering | Year: 2016

This paper focuses on the application of UAVs (unmanned aerial vehicles) on the information battlefield, and an intention prediction method for air targets is studied. Four factors of the enemy UAVs including velocity, angle, offense, and detection are analyzed and predicted by Grey Markov chain. Then, by combining the predicted factors with the rules provided by rough set, the enemy UAVs’ intention in the following short time can be deduced. The prediction method is studied utilizing incomplete information, and the feasibility of the developed prediction method is proved by the simulation results. © Springer Science+Business Media Singapore 2016.


Xu B.,Nanjing University of Aeronautics and Astronautics | Zhuang Y.,Nanjing University of Aeronautics and Astronautics | Xue Y.,Nanjing University of Aeronautics and Astronautics | Wang Z.,Science and Technology on Electronic optic Control Laboratory
Journal of Central South University of Technology (English Edition) | Year: 2012

A self-adaptive learning based immune algorithm (SALIA) is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned problems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms (CLONALG), and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10 7 in average. © 2012 Central South University Press and Springer-Verlag Berlin Heidelberg.


Zhao Z.,Northwestern Polytechnical University | Lu G.,Science and Technology on Electronic Optic Control Laboratory
International Journal of Intelligent Computing and Cybernetics | Year: 2012

Purpose: The purpose of this paper is to present a hybrid method of intelligent optimization algorithm and Receding Horizon Control. The method is applied to solve the problem of cooperative search of multi-unmanned aerial vehicles (multi-UAVs). Design/methodology/approach: The intelligent optimization of Differential Evolution (DE) makes the complex problem of multi-UAVs cooperative search a regular function optimization problem. To meet the real-time requirement, the idea of Receding Horizon Control is applied. An Extended Search Map based on hormone information is used to describe the uncertain environment information. Findings: Simulation results indicate effectiveness of the hybrid method in solving the problem of cooperative search for multi-UAVs. Originality/value: The paper presents an interesting hybrid method of DE and Receding Horizon Control for the problem of cooperative multi-UAVs. © Emerald Group Publishing Limited.


Geng W.-X.,Science and Technology on Electronic Optic Control Laboratory | Kong F.,Science and Technology on Electronic Optic Control Laboratory | Ma D.-Q.,Science and Technology on Electronic Optic Control Laboratory
26th Chinese Control and Decision Conference, CCDC 2014 | Year: 2014

To process the uncertainty of decision-making environment and the real-time during the tactical decision of UAV medium-range air combat, a hybrid tactical decision-making method based on rule sets and Fuzzy Bayesian network (FBN) was proposed. By studying the process of UAV air combat, the main factors that affect the tactical decision were analyzed. A corresponding FBN and expert system were built up. The hybrid system retained the advantage of expert system by the first call to it. In the mean time, the system could also process the uncertainty of decision-making environment by means of the FBN. Finally, through the air combat simulation, the correctness, real-time and effectiveness in an uncertain environment of the hybrid tactical decision-making method were verified. © 2014 IEEE.

Loading Science and Technology on Electronic optic Control Laboratory collaborators
Loading Science and Technology on Electronic optic Control Laboratory collaborators