The sequential fusion estimation for multisensor systems disturbed by non-Gaussian but heavytailed noises is studied in this paper. Based on multivariate t-distribution and the approximate t-filter,the sequential fusi...
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The sequential fusion estimation for multisensor systems disturbed by non-Gaussian but heavytailed noises is studied in this paper. Based on multivariate t-distribution and the approximate t-filter,the sequential fusion algorithm is presented. The performance of the proposed algorithm is analyzed and compared with the t-filter-based centralized batch fusion and the Gaussian Kalman filter-based optimal centralized fusion. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective. As the generalization of the classical Gaussian Kalman filter-based optimal sequential fusion algorithm, the presented algorithm is shown to be superior to the Gaussian Kalman filter-based optimal centralized batch fusion and the optimal sequential fusion in estimation of dynamic systems with non-Gaussian noises.
The purpose of this paper is the regulation of water level in a photovoltaic pumping system. To reach this objective, we have developed an algorithm with Matlab / simulink which gives as result, the value of the refer...
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We consider an optimal denial-of-service(DoS) attack scheduling problem of N independent linear time-invariant processes, where sensors have limited computational capability. Sensors transmit measurements to the remot...
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We consider an optimal denial-of-service(DoS) attack scheduling problem of N independent linear time-invariant processes, where sensors have limited computational capability. Sensors transmit measurements to the remote estimator via a communication channel that is exposed to DoS attackers. However,due to limited energy, an attacker can only attack a subset of sensors at each time step. To maximally degrade the estimation performance, a DoS attacker needs to determine which sensors to attack at each time step. In this context, a deep reinforcement learning(DRL) algorithm, which combines Q-learning with a deep neural network, is introduced to solve the Markov decision process(MDP). The DoS attack scheduling optimization problem is formulated as an MDP that is solved by the DRL algorithm. A numerical example is provided to illustrate the efficiency of the optimal DoS attack scheduling scheme using the DRL algorithm.
This paper describes an Adaptive Backstepping Neural Network (ABNN) method used for a ship course tracking control. The ship model is described by a third order nonlinear model whose parameters are unknown. The contro...
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This paper focuses on the challenge of fixed-time control for spatiotemporal neural networks(SNNs) with discontinuous activations and time-varying coefficients. A novel fixed-time convergence lemma is proposed, which ...
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This paper focuses on the challenge of fixed-time control for spatiotemporal neural networks(SNNs) with discontinuous activations and time-varying coefficients. A novel fixed-time convergence lemma is proposed, which facilitates the handling of time-varying coefficients of SNNs and relaxes the restriction on the non-positive definiteness of the derivative of the Lyapunov function. Besides, a more flexible and economical aperiodically switching control technique is presented to stabilize SNNs within a fixed time,efectively reducing the amount of information transmission and control costs. Under the newly established fixed-time convergence lemma and aperiodically switching controller, many more general algebraic conditions are deduced to ensure the fixed-time stabilization of SNNs. Numerical examples are provided to manifest the validity of the results.
This paper describes a Predictive control method used for track following control in hard disc drives (HDD). While reading/ writing data, the head must be positioned on the target data track quickly and precisely, and...
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Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties ...
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Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced ***, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.
Salvo attacking a surface target by multiple missiles is an effective tactic to enhance the lethality and penetrate the defense system. However, existing cooperative guidance laws in the midcourse or terminal course a...
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Salvo attacking a surface target by multiple missiles is an effective tactic to enhance the lethality and penetrate the defense system. However, existing cooperative guidance laws in the midcourse or terminal course are not suitable for long-and medium-range missiles or stand-off attacking. Because the initial conditions of cooperative terminal guidance that are generally generated from the mid-course flight may not lead to a successful cooperative terminal guidance without proper mid-course flight adjustment. Meanwhile, cooperative guidance in the mid-course cannot solely guarantee the accuracy of a simultaneous arrival of multiple missiles. Therefore, a joint mid-course and terminal course cooperative guidance law is developed. By building a distinct leader-follower framework, this paper proposes an efficient coordinated Dubins path planning method to synchronize the arrival time of all engaged missiles in the mid-course flight. The planned flight can generate proper initial conditions for cooperative terminal guidance, and also benefit an earliest simultaneous arrival. In the terminal course, an existing cooperative proportional navigation guidance law guides all the engaged missiles to arrive at a target accurately and *** integrated guidance law for an intuitive application is summarized. Simulations demonstrate that the proposed method can generate fast and accurate salvo attack.
Scale space generation is a fundamental problem in almost all feature extraction algorithms. Often, it is a critical prior step of most image/video analytic applications that are based on the invariance or co-invarian...
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ISBN:
(纸本)9781467374439
Scale space generation is a fundamental problem in almost all feature extraction algorithms. Often, it is a critical prior step of most image/video analytic applications that are based on the invariance or co-invariance of local features, such as SIFT based recognition, matching, and tracking applications. However, it is still quite a challenging problem to enable real-time applications of the extraction of local features due to the fact that scale space generation has a rather large computational complexity. This paper proposes the optimal FPGA design for acceleration of scale space generation. First, in order to derive the mathematical model for scale space generation that fits best in with the FPGA, we have discarded the conventional template convolution based Gaussian filtering scheme and adopted a novel IIR filter based recursive Gaussian blurring algorithm. Then, an approach based on the Retiming technique, which could find the minimal operational period for any given IIR filter, is used to finalize the overall design. For 1024×768 video, the proposed design is able to generate scale spaces at almost 400 fps, which is fast enough to support most real-time applications like object recognition, object matching, and 3D reconstruction.
Electro-hydraulic servo systems(EHSSs) have been widely used in industrial and military applications for their high power-to-size ratio and the ability to supply huge ***,precise control of EHSSs cannot be easily obta...
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ISBN:
(纸本)9781479900305
Electro-hydraulic servo systems(EHSSs) have been widely used in industrial and military applications for their high power-to-size ratio and the ability to supply huge ***,precise control of EHSSs cannot be easily obtained due to their inner nonlinearity and parameter *** load is another factor to decrease the tracking performance of *** adaptive robust control(IARC) was proposed to improve the tracking performance of EHSS,but due to the poor parameter adapting speed,IARC can be further improved to have better *** projection type parameter estimation algorithm is redesigned to increase the adapting speed when parameter is changed.A fast adaptive robust control (FARC) is then proposed to speed up the parameter adapting speed,so that a better tracking performance of FARC is maintained. Simulation results show that the proposed FARC gives an improved tracking performance and a faster parameter adaptation.
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