Yuxin Zhao

An improved variable tap-length LMS algorithm

An improved variable tap-length LMS algorithm is proposed in this paper. As compared with the original fractional tap-length LMS (FT-LMS) algorithm, the proposed algorithm can obtain both a fast convergence rate and a small steady-state error of the tap-length. Algorithm analysis is given which also provides a guideline for the parameter choice of the proposed algorithm. Simulations are performed under low noise and high noise conditions with time varying impulse responses. All simulation results show the advantages of the proposed algorithm as compared with the FT-LMS algorithm, and confirm the algorithm analysis.

Sparse analysis model based multiplicative noise removal with enhanced regularization

This paper proposes a new multiplicative noise removal method using a sparse analysis model. Apart from a data fidelity term, two regularizers are employed in the proposed approach: a regularizer using a learned analysis dictionary and a smoothness regularizer defined on pixel-wise differences.To address the resulting optimization problem, we adapt the alternating direction method of multipliers (ADMM) framework, and present a new optimization method.Experimental results demonstrate the improved performance of the proposed method as compared with several recent baseline methods, especially for relatively high noise levels. The multiplicative noise removal problem for a corrupted image has recently been considered under the framework of regularization based approaches, where the regularizations are typically defined on sparse dictionaries and/or total variation (TV). This framework was demonstrated to be effective. However, the sparse regularizers used so far are based overwhelmingly on the synthesis model, and the TV based regularizer may induce the stair-casing effect in the reconstructed image. In this paper, we propose a new method using a sparse analysis model. Our formulation contains a data fidelity term derived from the distribution of the noise and two regularizers. One regularizer employs a learned analysis dictionary, and the other regularizer is an enhanced TV by introducing a parameter to control the smoothness constraint defined on pixel-wise differences. To address the resulting optimization problem, we adapt the alternating direction method of multipliers (ADMM) framework, and present a new method where a relaxation technique is developed to update the variables flexibly with either image patches or the whole image, as required by the learned dictionary and the enhanced TV regularizers, respectively. Experimental results demonstrate the improved performance of the proposed method as compared with several recent baseline methods, especially for relatively high noise levels.

Feature matching algorithm based on spatial similarity

The disparities of features that represent the same real world entities from disparate sources usually occur, thus the identification or matching of features is crutial to the map conflation. Motivated by the idea of identifying the same entities through integrating known information by eyes, the feature matching algorithm based on spatial similarity is proposed in this paper. Total similarity is obtained by integrating positional similarity, shape similarity and size similarity with a weighted average algorithm, then the matching entities is achieved according to the maximum total similarity. The matching of areal features is analyzed in detail. Regarding the areal feature as a whole, the proposed algorithm identifies the same areal features by their shape-center points in order to calculate their positional similarity, and shape similarity is given by the function of describing the shape, which ensures its precision not be affected by interferes and avoids the loss of shape information, furthermore the size of areal features is measured by their covered areas. Test results show the stability and reliability of the proposed algorithm, and its precision and recall are higher than other matching algorithm.

Control Input Saturation Sliding-Mode Control System Design for Spacecraft Based on Neural Network

For spacecraft tracking system, the input control is saturated because of the actuators constrains. In this paper, the spacecraft single shaft motion is considered and a sliding-mode control system under control input saturation has been investigated. Theory proves that this method can ensure the stability of system and implement effective control under control input saturation. Furthermore, the regulating function of neural network can estimates the control input error caused by saturation, and there is a certain external interference, the RBF neural network can ensure strong robustness of the system. Simulation results show that the proposed sliding-mode control system has strong ability of estimation error and better dynamic performance under control input saturation.

Underwater Terrain Navigability Analysis Based on Multi-beam Data

According to underwater terrain characteristic, the multi-index synthetic evaluation theory is used for calculating matching area of an underwater terrain-aided navigation system in this paper. Considering the terrain elevation standard deviation, roughness, terrain correlation coefficient, topographic information entropy and other crucial terrain characteristic parameters on the effect of navigation performance, and select system evaluation factors set, then the entropy weight method and CRITIC method is respectively used to determine the weight of every evaluation index, finally educe navigation performance evaluation value of the terrain, which provides the basis reference for each selected matching area. All tests show that the evaluation results which determine the weights with CRITIC method are more stable, and the value higher, the navigation performance is better.

Particle flow for sequential Monte Carlo implementation of probability hypothesis density

Target tracking is a challenging task and generally no analytical solution is available, especially for the multi-target tracking systems. To address this problem, probability hypothesis density (PHD) filter is used by propagating the PHD instead of the full multi-target posterior. Recently, the particle flow filter based on the log homotopy provides a new way for state estimation. In this paper, we propose a novel sequential Monte Carlo (SMC) implementation for the PHD filter assisted by the particle flow (PF), which is called PF-SMC-PHD filter. Experimental results show that our proposed filter has higher accuracy than the SMC-PHD filter and is computationally cheaper than the Gaussian mixture PHD (GM-PHD) filter.

Cosparsity-based Stagewise Matching Pursuit algorithm for reconstruction of the cosparse signals

The cosparse analysis model has been introduced as an interesting alternative to the standard sparse synthesis model. Given a set of corrupted measurements, finding a signal belonging to this model is known as analysis pursuit, which is an important problem in analysis model based sparse representation. Several pursuit methods have already been proposed, such as the methods based on l1-relaxation and greedy approaches based on the cosparsity of the signal. This paper presents a novel greedy-like algorithm, called Cosparsity-based Stagewise Matching Pursuit (CSMP), where the cosparsity of the target signal is estimated adaptively with a stagewise approach composed of forward and backward processes. In the forward process, the cosparsity is estimated and the signal is approximated, followed by the refinement of the cosparsity and the signal in the backward process. As a result, the target signal can be reconstructed without the prior information of the cosparsity level. Experiments show that the performance of the proposed algorithm is comparable to those of the l1-relaxation and Analysis Subspace Pursuit (ASP)/Analysis Compressive Sampling Matching Pursuit (ACoSaMP) in noiseless case and better than that of Greedy Analysis Pursuit (GAP) in noisy case.

Auxiliary Function Based Independent Vector Analysis with Spatial Initialization for Frequency Domain Speech Separation

Independent vector analysis (IVA) is one of the state-of-the-art methods for frequency domain speech separation, which can retain the inter-frequency dependency structure to theoretically avoid the classical permutation ambiguity inherent to blind source separation (BSS). Auxiliary function based IVA (AuxIVA) is proposed as a fast form IVA method by adopting the auxiliary function technique to avoid step size tuning. In this paper, the spatial information is introduced as a prior knowledge for AuxIVA to set an initialization, which can not only increase the convergence speed in terms of iteration number but also improve the separation performance. The experimental results with real speech signals and real room recordings confirm the advantage of the proposed method.

Endpoint Triangle's Circumcircle Method in Delaunay Triangulation

In this paper, an improved algorithm of node refinement scheme called endpoint triangles circumcircle method (ETCM) is proposed, which has linear time complexity. Regarding stability, new nodes quantity, time cost and mesh quality as criterions, ETCM and existing node refinement algorithms are evaluated by simulations. The results show that the time cost of ETCM is more independent of data distribution than the existing algorithms, while the performances on other criterions of ETCM are almost the same as the best of existing algorithms.

3-D Human Tracking Based on Particle Filter

A 3-D human tracking method based on particle filter is introduced in this paper. In this method, firstly the face area of the human is extracted based on a skin model and template matching with standard human face from recorded 2-D video images. Then 2-D face positions in the video images are converted to 3-D world coordinates through the calibration parameters. Finally these 3-D position estimates are fed to a 3-D visual-tracker, and both position and velocity of the human can be obtained from the 3-D tracker for further applications, such as teleconference and human motion analysis. Simulations are performed to confirm the effectiveness of the proposed approach.