The kernel correlation filtering algorithm cannot judge the occlusion by itself when the object is occluded by foreign objects, and the tracking performance is greatly affected. Aiming at this problem, this paper prop...
The kernel correlation filtering algorithm cannot judge the occlusion by itself when the object is occluded by foreign objects, and the tracking performance is greatly affected. Aiming at this problem, this paper proposes an anti-occlusion object tracking algorithm that fuses response templates and combined features. The peak sidelobe ratio (PSR) is used to determine whether the object is occluded. When the object is partially occluded, a combined feature representation object model is introduced to enhance robustness, and a multi-response template with anti-occlusion performance is constructed to improve the performance of the object when it is occluded by foreign objects. The problem of reducing the available information, thereby improving algorithm performance. Experiments in the OTB-100 data set can prove that the success rate and accuracy of the algorithm have competitive results compared with the comparison algorithms.
This paper discusses the design problem of recursive filtering method for time-varying nonlinear delayed systems(NDSs) with stochastic parameter matrices(SPMs) and censored *** particular,the Tobit Type Ⅰ model provi...
This paper discusses the design problem of recursive filtering method for time-varying nonlinear delayed systems(NDSs) with stochastic parameter matrices(SPMs) and censored *** particular,the Tobit Type Ⅰ model provides a description of the censored *** main objective of this paper is to construct a recursive filter for NDSs with both SPMs and censored *** upper bound of the filtering error covariance is first calculated via mathematical induction,and the upper bound is then minimized by choosing proper filter ***,a sufficient condition is provided to guarantee that the filtering error is uniformly bounded in the mean-square ***,the viability and applicability of the proposed filterin g method are demonstrated using a numerical simulation.
How improve the mapping efficiency and location accuracy of the multi-UAV cluster based on the distributed SLAM technology is a significant problem in overlapping regions. Therefore, this paper mainly proposes a novel...
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With the popularization and development of communication technology, the resource allocation problem of satellites has become a hot research topic. This study proposes a model for satellite resource allocation through...
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This paper investigates the problem of outlier-resistant distributed fusion filtering(DFF)for a class of multi-sensor nonlinear singular systems(MSNSSs)under a dynamic event-triggered scheme(DETS).To relieve the effec...
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This paper investigates the problem of outlier-resistant distributed fusion filtering(DFF)for a class of multi-sensor nonlinear singular systems(MSNSSs)under a dynamic event-triggered scheme(DETS).To relieve the effect of measurement outliers in data transmission,a self-adaptive saturation function is ***,to further reduce the energy consumption of each sensor node and improve the efficiency of resource utilization,a DETS is adopted to regulate the frequency of data *** the addressed MSNSSs,our purpose is to construct the local outlier-resistant filter under the effects of the measurement outliers and the DETS;the local upper bound(UB)on the filtering error covariance(FEC)is derived by solving the difference equations and minimized by designing proper filter ***,according to the local filters and their UBs,a DFF algorithm is presented in terms of the inverse covariance intersection fusion *** such,the proposed DFF algorithm has the advantages of reducing the frequency of data transmission and the impact of measurement outliers,thereby improving the estimation ***,the uniform boundedness of the filtering error is discussed and a corresponding sufficient condition is ***,the validity of the developed algorithm is checked using a simulation example.
In this paper,the distributed state estimation method with resilient attenuation feature is proposed for time-varying fractional-order complex networks under encoding-decoding *** encoding-decoding-induced dynamic err...
In this paper,the distributed state estimation method with resilient attenuation feature is proposed for time-varying fractional-order complex networks under encoding-decoding *** encoding-decoding-induced dynamic errors for distinct nodes are characterized by the truncated Gaussian *** order to compensate the effects induced by encodingdecoding scheme,the variances of encoding-decoding-induced dynamic errors are considered in process of designing the resilient distributed estimation *** particular,the upper bounds of updated estimation error covariances are derived ***,the upper bounds are minimized by constructing the gain matrices at each sampling ***,a sufficient condition is provided to guarantee the boundedness of estimation error dynamics in the mean-square ***,the validity of distributed resilient state estimation scheme is demonstrated by a simulation example.
In this paper, the outlier-resistant distributed filtering problem based on amplify-and-forward relays is studied for discrete time-varying nonlinear multi-rate systems with multiple measurement delays over sensor net...
In this paper, the outlier-resistant distributed filtering problem based on amplify-and-forward relays is studied for discrete time-varying nonlinear multi-rate systems with multiple measurement delays over sensor networks, where the augmenting method is utilized to transform the multi-rate system into a single rate system. An amplify-and-forward(AF) relay is set between the sensor and the filter to extend the transmission distance of the signal and ensure the communication transmission *** outlier-resistant distributed filter is constructed by introducing a saturation function to limit the innovations, then the upper bound on the filtering error covariance is obtained and the filter gain is designed to minimize such obtained upper bound. Finally,a numerical example is used to show the effectiveness of the outlier-resistant distributed filtering algorithm based on AF relays.
In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the curr...
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In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.
Achieving joint learning of Salient Object Detection (SOD) and Camouflaged Object Detection (COD) is extremely challenging due to their distinct object characteristics, i.e., saliency and camouflage. The only prelimin...
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Railway point machines(RPMS) are one of the key equipments in the railway system to switch different routes for the *** monitoring for RPMs is a vital measure to keep train operation safe and *** convenience and low c...
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Railway point machines(RPMS) are one of the key equipments in the railway system to switch different routes for the *** monitoring for RPMs is a vital measure to keep train operation safe and *** convenience and low cost into consideration, a novel intelligent condition monitoring method for RPMs based on sound analysis is ***-domain and frequency-domain features are obtained,and normalized using z-score standardization method to eliminate the influences of different *** particle swarm optimization(BPSO) is utilized to select the most significant discrimination feature *** effects of the selected optimal features are verified using Support vector machine(SVM), 1-Nearest neighbor(1 NN), Random forest(RF), and Naive Bayes(NB).Experiment results indicate SVM performs best on identification accuracy and computing cost compared with the other three *** identification accuracies on normal switching and reverse switching processes reach100% and 99.67%, respectively, indicating the feasibility of the proposed method.
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