Yang Wan-hai

Decision fusion with censoring wireless sensors under Rice fading channels

This paper addressed the issue of fusing decisions transmitted via Rice fading channels, with censoring transmission strategies. We derived an optimal likelihood ratio rule based on the channel state information. However, acquiring channel information may be too expensive for resource-constraint sensor networks. Under this circumstance, suboptimal likelihood ratio fusion rule is proposed, requiring only the information of channel statistics. In addition, when the signal-to-noise ratio (SNR) of channels is quite low, the reduced forms are also derived. Results from both theoretical analysis and simulations show that suboptimal fusion rules need much less information and computation, which only causes slight performance degradation. Suboptimal fusion rules are practicable for decision fusion systems in resource-constraint censoring wireless sensor networks.

Multi-sensor Target Recognition Fusion Based on Fuzzy Theory

In the process of the multi-sensors target recognition fusion, focused on the problem that it is difficult to determine the reliability of each sensor and how the data measured by different sensors are fused, a multi-sensor target recognition fusion method based on fuzzy theory is proposed. The mutual supportability of multiple sensors is obtained from the correlation function. Then by the membership function, the reliability of information provide by each sensor is gained. Finally, the supposed fusion result of multi-sensors target recognition can be produced on the basis of fuzzy integration function. The method is simple computationally and can objectively reflect the reliability of each sensor and interrelationship between these sensors. By applying the method to the target recognition, the simulation experiment shows that it can identify the target accurately and is an effective and feasible multi-sensors target recognition fusion method.

Simulation of compound k-distribution sea clutter

The paper first briefly reviews and analyses compound k-distribution model and spectrum characteristics of sea clutter. A simulation model and corresponding algorithm are presented. According to this model, radar sea clutter with compound k-distribution for amplitude and known spectrum was generated. Simulation results show that presented method is very effective.

Adaptive method for low velocity moving target detection

In radar signal processing, when a target moves with a low velocity, it generates so small a Doppler frequency shift that the target reflected signals are submerged by clutter signals, and the radar cannot detect the target, especially under low signal-to-clutter ratio (SCR) environments. Because of its good performance in intelligent signal processing, an adaptive method is used to eliminate the clutter signals from the received signals in the signals power spectrum figure according to the power spectrum distribution of the clutter.

Firing particle flow detection and tracking in sequence images

A method of detecting and tracking firing particle flow in sequence images is proposed in this paper. The features of the firing particle flow in sequence images are analyzed and described using a fuzzy linguistic variable that is used to detect the moving objects. Moving trajectory tracking of firing particle flow is accomplished adaptively also. This method is feasible and satisfied with experiment results.

Nonlinear model and readback distortions of MR head

Magnetoresistive (MR) heads have been widely used in high density magnetic recording systems. The MR head non-linearity degrades the detection performance. A 3rd-order polynomial model is used to characterize the MR head non-linearity. With the extracted dipulse method, the readback distortions of the MR head are investigated through simulation. Some simulation results are given.

Studies of non-linearities in PRML channel

Magnetoresistive (MR) heads and partial response with maximum likelihood (PRML) detection channel are widely used in high density magnetic recording systems. The PRML is known to be sensitive to the non-linearities. The non-linearities will dramatically degrade the performance of the PRML channel. Here, we study the nonlinear transition shift (NLTS) and the MR head non-linearity. In addition, the extracted dipulse method is used to simulate the NLTS and MR head non-linearity.