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The existing reconstruction algorithms for 2-D signals, most of which convert the original signals into 1-D signals, could be more efficient and accurate. Based on the 2-Dimensional measurement model (2-DMM), a novel recovery algorithm is proposed and then its effectiveness is proved. In 2-DMM, the rows and columns of 2-D signals are measured simultaneously by two independent sensing matrixes. The new algorithm reconstructs the signals as a whole. It relieves the artificial effects and scale expansion caused by the traditional algorithms. Both theoretical analysis and experiment simulation demonstrate that the recovery probability and Reconstruction-SNR performance of the new algorithm are better than those of the existing reconfiguration algorithms under the condition of 2-D measurement. While the operation complexity of the new algorithm is lower than the counterpart of 1-D conversion algorithms. [ABSTRACT FROM AUTHOR]

The existing back-track iterative reconstruction algorithms for reconstructing the original signal fast and well always require the prior information of signal sparsity for accurate recovery. But sometimes it's hard to meet the requirement in practice. Aiming at this problem, a new blind sparsity back-track reconstruction algorithm based on smooth l0 -norm constraint is proposed and its convergence is demonstrated. The new algorithm does not need the sparse prior and the smooth l0-norm issued to estimate the sparsity of signal and determine the support set in the truncation process. The new algorithm is effective as other back-track ones and is able to avoid recovery failure due to unknown or underestimated sparsity as well. The theoretical analysis and experiment simulation prove that the performance of the new algorithm is better than that of the existing back-track iterative reconstruction algorithms in the sparsity unknown conditions. [ABSTRACT FROM AUTHOR]

Traditional identification algorithms for Duffing oscillator are mainly based on the transitions of phase diagram, and they need a large number of cumulative inputs to transform the system from chaos to the large-scale periodic state, and these limit their applications of Duffing oscillator. A new algorithm based on Kalman gain is proposed in the paper to predict state transitions of the Duffing oscillator before transitions in the phase diagram. By the establishing of Duffing state equations and setting the control condition of measurement equations, an improved form of Kalman gain can be obtained to effectively predict the state changes of the Duffing oscillator, experiments of harmonic signal detections show that the predictive algorithm can not only reduce at least 50% input points to identify the phase transitions of oscillator, but also the detection accuracy is obviously improved. [ABSTRACT FROM AUTHOR]

A new nonlinear filter method Cubature Kalman Filter (CKF) is improved for passive location with moving angle-measured sensors’ measurements.Firstly,it used Empirical Mode Decomposition (EMD) algorithm to estimate measurement noise covariance; And then the covariance of the procession noise and measurement noise is brought into the circle; Meanwhile,CKF is improved by the way of square root to keep its stability and positivity,and the results of track by Extend SCKF are compared with the results by Unscented Kalman Filter (UKF) in the text;By the tracking results to the velocity of the target, Extend SCKF algorithm can not only track the target with unknown measurement noise but also improve the passive position precision remarkably as the same difficulty as UKF.

To improve the poor performance of the maneuvering target tracking, Particle Filters (PF) and velocity constrained Particle Filters (VCPF) are introduced to track the maneuvering target. According to the prior information, outrange particles are discarded during prediction step, and the distribution and the weight of particles are adjusted. Simulation result shows that VCPF can track the maneuvering target stablely. Furthermore, the convergence rate and track accuracy of the algorithm can also be effectively improved.