In order to keep stable navigation accuracy when the blind node （BN） moves between two adjacent clusters, a distributed fusion method for the integration of the inertial navigation system （INS） and the wireless se...In order to keep stable navigation accuracy when the blind node （BN） moves between two adjacent clusters, a distributed fusion method for the integration of the inertial navigation system （INS） and the wireless sensor network （WSN） based on H∞ filtering is proposed. Since the process and measurement noise in the integration system are bounded and their statistical characteristics are unknown, the H∞ filter is used to fuse the information measured from local estimators in the proposed method. Meanwhile, the filter can yield the optimal state estimate according to certain information fusion criteria. Simulation results show that compared with the federal Kalman solution, the proposed method can reduce the mean error of position by about 45% and the mean error of velocity by about 85 %.展开更多
There are many filtering methods that can be used for the initial alignment of an integrated inertial navigation system. This paper discussed the use of GPS, but focused on two kinds of filters for the initial alignme...There are many filtering methods that can be used for the initial alignment of an integrated inertial navigation system. This paper discussed the use of GPS, but focused on two kinds of filters for the initial alignment of an integrated strapdown inertial navigation system (SINS). One method is based on the Kalman filter (KF), and the other is based on the robust filter. Simulation results showed that the filter provides a quick transient response and a little more accurate estimate than KF, given substantial process noise or unknown noise statistics. So the robust filter is an effective and useful method for initial alignment of SINS. This research should make the use of SINS more popular, and is also a step for further research.展开更多
In this paper , the principle of H∞ filtering is discussed and H_∞ filter is constructed, which is used in the initial alignment of the strapdown inertial navigation systems(SINS). The error model of SINS is derived...In this paper , the principle of H∞ filtering is discussed and H_∞ filter is constructed, which is used in the initial alignment of the strapdown inertial navigation systems(SINS). The error model of SINS is derived. By utilizing constructed H∞ filter, the filtering calculation to that system has been conducted. The simulation results of the misalignment angle are given under the condition of unknown noises. The results show that the process of alignment with H∞ filter is much faster and with excellent robustness.展开更多
Nonlinear estimation problem is investigated in this paper. By extension of a linear H_∞estimation with corrector-predictor form to nonlinear cases, a new extended H_∞filter is proposed for time-varying discrete-tim...Nonlinear estimation problem is investigated in this paper. By extension of a linear H_∞estimation with corrector-predictor form to nonlinear cases, a new extended H_∞filter is proposed for time-varying discrete-time nonlinear systems. The new filter has a simple observer structure based on a local linearization model, and can be viewed as a general case of the extended Kalman filter (EKF). An example demonstrates that the new filter with a suitable-chosen prescribed H_∞bound performs better than the EKF.展开更多
This paper is concerned with the exponential H_∞ filtering problem for a class of discrete-time switched neural networks with random time-varying delays based on the sojourn-probability-dependent method. Using the av...This paper is concerned with the exponential H_∞ filtering problem for a class of discrete-time switched neural networks with random time-varying delays based on the sojourn-probability-dependent method. Using the average dwell time approach together with the piecewise Lyapunov function technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with random time-varying delays which are characterized by introducing a Bernoulli stochastic variable.Based on the derived H_∞ performance analysis results, the H_∞ filter design is formulated in terms of Linear Matrix Inequalities(LMIs). Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed design procedure.展开更多
This paper is concerned with the problem of robust H∞ filtering for linear discrete-time systems with multiple state delays and polytopic uncertain parameters. Attention is focused on the design of full-order, reduce...This paper is concerned with the problem of robust H∞ filtering for linear discrete-time systems with multiple state delays and polytopic uncertain parameters. Attention is focused on the design of full-order, reduced-order and zeroth-order robust H∞ filters on the basis of a recently published parameter-dependent Lyapunov stability result. Sufficient conditions for the existence of such filters are formulated in terms of linear matrix inequalities, upon which admissible filters can be obtained from convex optimization problems. The proposed methodology has been shown, via a numerical example, to be much less conservative than previous filter design methods in the quadratic framework.展开更多
In H.264,computational complexity and memory access of deblocking filters are variable,dependent on video contents.This paper proposes a VLSI architecture of deblocking filters with adaptive dynamic power,which avoids...In H.264,computational complexity and memory access of deblocking filters are variable,dependent on video contents.This paper proposes a VLSI architecture of deblocking filters with adaptive dynamic power,which avoids redundant computations and memory accesses by precluding the blocks that can be skipped.The vertical and horizontal edges are simulta-neously processed in an advanced scan order to speed up the decoder.As a result,dynamic power of the proposed architecture can be reduced adaptively(up to about 89%) for different videos,and the off-chip memory access is improved when compared to previous designs.Moreover,the processing capability of the proposed architecture is in particular appropriate for real-time deblocking of high-definition television(HDTV,1920×1080 pixels/frame,60 frames/s video signals) video operation at 62 MHz.Using the proposed architecture,power can be reduced by up to about 89% and processing time by from 25% to 81% compared with previous designs.展开更多
基金The National Basic Research Program of China (973 Program) (No. 2009CB724002)the National Natural Science Foundation of China (No. 50975049)+2 种基金the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20110092110039)the Program for Special Talents in Six Fields of Jiangsu Province (No.2008143)the Program Sponsored for Scientific Innovation Research of College Graduates in Jiangsu Province,China (No. CXLX_0101)
文摘In order to keep stable navigation accuracy when the blind node （BN） moves between two adjacent clusters, a distributed fusion method for the integration of the inertial navigation system （INS） and the wireless sensor network （WSN） based on H∞ filtering is proposed. Since the process and measurement noise in the integration system are bounded and their statistical characteristics are unknown, the H∞ filter is used to fuse the information measured from local estimators in the proposed method. Meanwhile, the filter can yield the optimal state estimate according to certain information fusion criteria. Simulation results show that compared with the federal Kalman solution, the proposed method can reduce the mean error of position by about 45% and the mean error of velocity by about 85 %.
基金the National Natural Science Foundationunder Grant No.60604019.
文摘There are many filtering methods that can be used for the initial alignment of an integrated inertial navigation system. This paper discussed the use of GPS, but focused on two kinds of filters for the initial alignment of an integrated strapdown inertial navigation system (SINS). One method is based on the Kalman filter (KF), and the other is based on the robust filter. Simulation results showed that the filter provides a quick transient response and a little more accurate estimate than KF, given substantial process noise or unknown noise statistics. So the robust filter is an effective and useful method for initial alignment of SINS. This research should make the use of SINS more popular, and is also a step for further research.
文摘In this paper , the principle of H∞ filtering is discussed and H_∞ filter is constructed, which is used in the initial alignment of the strapdown inertial navigation systems(SINS). The error model of SINS is derived. By utilizing constructed H∞ filter, the filtering calculation to that system has been conducted. The simulation results of the misalignment angle are given under the condition of unknown noises. The results show that the process of alignment with H∞ filter is much faster and with excellent robustness.
文摘Nonlinear estimation problem is investigated in this paper. By extension of a linear H_∞estimation with corrector-predictor form to nonlinear cases, a new extended H_∞filter is proposed for time-varying discrete-time nonlinear systems. The new filter has a simple observer structure based on a local linearization model, and can be viewed as a general case of the extended Kalman filter (EKF). An example demonstrates that the new filter with a suitable-chosen prescribed H_∞bound performs better than the EKF.
基金supported by the National Natural Science Foundation of China(Grant Nos.61573096 and 61272530)the Natural Science Foundation of Jiangsu Province of China(Grant No.BK2012741)the 333 Engineering Foundation of Jiangsu Province of China(Grant No.BRA2015286)
文摘This paper is concerned with the exponential H_∞ filtering problem for a class of discrete-time switched neural networks with random time-varying delays based on the sojourn-probability-dependent method. Using the average dwell time approach together with the piecewise Lyapunov function technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with random time-varying delays which are characterized by introducing a Bernoulli stochastic variable.Based on the derived H_∞ performance analysis results, the H_∞ filter design is formulated in terms of Linear Matrix Inequalities(LMIs). Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed design procedure.
文摘This paper is concerned with the problem of robust H∞ filtering for linear discrete-time systems with multiple state delays and polytopic uncertain parameters. Attention is focused on the design of full-order, reduced-order and zeroth-order robust H∞ filters on the basis of a recently published parameter-dependent Lyapunov stability result. Sufficient conditions for the existence of such filters are formulated in terms of linear matrix inequalities, upon which admissible filters can be obtained from convex optimization problems. The proposed methodology has been shown, via a numerical example, to be much less conservative than previous filter design methods in the quadratic framework.
基金Project (No. NSS’USA5978) supported by the National Science Foundation of the United States under the East Asia Pacific Program
文摘In H.264,computational complexity and memory access of deblocking filters are variable,dependent on video contents.This paper proposes a VLSI architecture of deblocking filters with adaptive dynamic power,which avoids redundant computations and memory accesses by precluding the blocks that can be skipped.The vertical and horizontal edges are simulta-neously processed in an advanced scan order to speed up the decoder.As a result,dynamic power of the proposed architecture can be reduced adaptively(up to about 89%) for different videos,and the off-chip memory access is improved when compared to previous designs.Moreover,the processing capability of the proposed architecture is in particular appropriate for real-time deblocking of high-definition television(HDTV,1920×1080 pixels/frame,60 frames/s video signals) video operation at 62 MHz.Using the proposed architecture,power can be reduced by up to about 89% and processing time by from 25% to 81% compared with previous designs.
