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Traditional beamforming performance will significantly degrade with insufficient training samples on multi-dimensional arrays with much higher dimensions. In this paper, a tensor beamforming method that has advantages in the number of training snapshots required is introduced based on the proposed tensor model and the separability of sub-dimensions for multi-dimensional arrays. Then, for coherent interference, based on the model of the sub-dimension's interference covariance matrix in tensor beamforming, a robust tensor beamforming method is proposed by directly estimating the interference covariance matrix. Analysis shows that the proposed method could overcome the non-homogeneous clutter environment and coherent interference and obtain a higher output signal-to-interference-noise ratio. Taking the given two-dimensional polarization-sensitive array as an example, the number of training snaps required by the proposed method is 1/3 of the traditional method, and the output signal-to-interference-to-noise ratio increases by about 2.5 dB under coherent interference scenario. The simulation results verify the effectiveness of the analysis. [ABSTRACT FROM AUTHOR]

In this paper, the Kalman filter robust to outliers and observation noise with unknown distribution is proposed. It is shown that a new criterion for deriving a Kalman filter can be constructed when the It norm of the observation errors in its maximum posterior criterion is replaced by the Huber loss function. Since the Huber loss function can simultaneously describe both lx and It norms of an observation error, it is illustrated that the Kalman filter derived from the new criterion is robust to outliers as l1 norm and its performance is the same as the traditional one when the outliers are free. When the statistical distribution of the observation noise with outliers is unknown, a Gaussian mixture model with unknown parameters is used to describe such a distribution and the variational Bayesian is employed to infer these unknown parameters. In this way, a Kalman filter robust to outliers and unknown statistical distribution of observation noises is given. Both the correctness of the analytical results and the performance of the proposed algorithms superior to the robust Kalman filters reported in literatures are verified by simulations and experiments. [ABSTRACT FROM AUTHOR]

A model and method for super-resolution analysis of rime frequency signal with identical modulation forms and very small carrier frequency difference between its components is proposed. Firstly, the amplitude and the phase of each component are taken as the state variables, the time-domain observation of the signal and the instantaneous frequency slope based on the same modulation form are taken as the measurement equations, and the multi-observation state space model is constructed for the time-frequency signal with minimal carrier frequency difference between components. Then, in order to estimate the number of components required by the model, the original signal is demodulated into a nearly stationary narrow-band signal by using matched demodulation transform, and then the covariance of the narrow-band signal m the time window is estimated by sparse iterative adaptive covariance. Based on the estimated covariance, the number of component in the window is estimated by using the discrimination function estimator method. Finally, taking the mode of estimation results in multiple sliding time windows as the number of signal components, the super-resolution analysis of time-frequency signal can be carried out based on the proposed model and the data fusion filtering algorithm. The simulation results show that the performance of the proposed method is better than other method. [ABSTRACT FROM AUTHOR]

In color flow imaging systems, the ultrasound Doppler signal scattered from blood is heavily corrupted by the strong clutter signal reflected from the slow-moving muscular tissue. Moving target indicator (MTI) is a key technique of rejecting the clutter signal and detecting blood flow. To improve the performance of detecting blood flow, a new MTI scheme is proposed in this paper. It consists of a pre-filter, a clutter weak-rejector and a 2-order AR estimator. After the processing of pre-filtering and clutter weak-rejection, the strong clutter signal is effectively eliminated and its strength is comparable with blood flow. Under such condition, the optimal performance can be attained by the 2-order AR estimator. It is illustrated in the simulation that, compared with the data from traditional methods, the blood flow parameters are estimated with smaller deviation and the lower speed flow is detected.

In order to test the performance of wall filters, we propose a method for simulation of Hybrid Doppler testing signal, which adopts a rectangular power spectrum. In the method, the parameters of clutter, blood flow and system noise can be adjusted with flexibility. The problem about choosing model parameters is discussed as an emphasis. Several reasonable and typical parameter combination patterns are presented. Furthermore, an example is given to explain the process of choosing the parameters and testing the wall filter performance.