Track Association for Dynamic Target Tracking System Based on AP Algorithm

Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system,and its accuracy directly impacts on the performance of the whole tracking system.A multi-sensor data association is proposed based on aftinity propagation(AP)algorithm.The proposed method needs an initial similarity,a distance between any two points,as a parameter,therefore,the similarity matrix is calculated by track position,velocity and azimuth of track data.The approach can automatically obtain the optimal classification of uncertain target based on clustering validity index.Furthermore,the same kind of data are fused based on the variance of measured data and the fusion result can be taken as a new measured data of the target.Finally,the measured data are classified to a certain target based on the nearest neighbor ideas and its characteristics,then filtering and target tracking are conducted.The experimental results show that the proposed method can effectively achieve multi-sensor and multi-target track association.

Efficient Inverse Method for Structural Identification Considering Modeling and Response Uncertainties

The inverse problem analysis method provides an effective way for the structural parameter identification.However,uncertainties wildly exist in the practical engineering inverse problems.Due to the coupling of multi-source uncertainties in the measured responses and the modeling parameters,the traditional inverse method under the deterministic framework faces the challenges in solving mechanism and computing cost.In this paper,an uncertain inverse method based on convex model and dimension reduction decomposition is proposed to realize the interval identification of unknown structural parameters according to the uncertain measured responses and modeling parameters.Firstly,the polygonal convex set model is established to quantify the epistemic uncertainties of modeling parameters.Afterwards,a space collocation method based on dimension reduction decomposition is proposed to transform the inverse problem considering multi-source uncertainties into a few interval inverse problems considering response uncertainty.The transformed interval inverse problem involves the two-layer solving process including interval propagation and optimization updating.In order to solve the interval inverse problems considering response uncertainty,an efficient interval inverse method based on the high dimensional model representation and affine algorithm is further developed.Through the coupling of the above two strategies,the proposed uncertain inverse method avoids the time-consuming multi-layer nested calculation procedure,and then effectively realizes the uncertainty identification of unknown structural parameters.Finally,two engineering examples are provided to verify the effectiveness of the proposed uncertain inverse method.

AN ASYMPTOTICAL O((k + 1)n^3L) AFFINE SCALING ALGORITHM FOR THE P_*(k)-MATRIX LINEAR COMPLEMENTRITY PROBLEM

Based on the generalized Dikin-type direction proposed by Jansen et al in 1997, we give out in this paper a generalized Dikin-type affine scaling algorithm for solving the P-*(kappa)-matrix linear complementarity problem (LCP). Form using high-order correctors technique and rank-one updating, the iteration complexity and the total computational turn out asymptotically O((kappa + 1)root nL) and O((kappa + 1)n(3)L) respectively.

Steady-state weights solution to affine projection algorithm

A new expression of the weights update equation for the affine projection algorithm （APA） is proposed that improves the convergence rate of an adaptive flter, particularly for highly colored input signals, and yields greater details of the internal structure. The steady-state weights solution to the APA algorithm is calculated in different step-sizes, which is significantly different from the iteration method. The weights error in steady-state is proved to be zero as the number of the input direction vector increases to infinity, ensuring that the estimated weights of the APA algorithm in steady-state are unbiased and consistent. The sensitivity of the step-size parameter for the steady-state weights is also analyzed. Simulation results show that the steady-state weights of the APA algorithm, obtained from the proposed method, are closer to the true weights than the estimated steady-state weights as determined by the traditional iteration method.

Low Complexity Adaptive Equalizers for Underwater Acoustic Communications

Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm （APA）, selective regressor APA（SR-APA）, family of selective partial update （SPU） algorithms, family of set-membership （SM） algorithms and selective partial update selective regressor APA （SPU-SR-APA） are compared with conventional algorithms such as the least mean square （LMS） in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error （MSE） of SR-APA, SPU-APA0 SPU-normalized least mean square （SPU-NLMS）, SPU-SR-APA0 SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.