Satellite fault diagnosis method based on predictive filter and empirical mode decomposition

A novel satellite fault diagnosis scheme is presented based on the predictive filter and empirical mode composition（EMD）.First,the predictive filter is utilized to obtain the fault estimation,which is corrupted by noise.Then the EMD method is introduced to decompose the fault estimation into a finite number of intrinsic mode functions and extract the trend of faults for fault diagnosis.The proposed scheme has the ability of diagnosing both abrupt and incipient faults of the actuator in a satellite attitude control subsystem.A mathematical simulation is given to illustrate the effectiveness of the proposed scheme.

Study on eliminating noises in short-period data of geomagnetic intensity by using nu-merical multichannel predictive filtering

The effectiveness of eliminating the noises in short-period data of geomagnetic intensity recorded in a little seismo-geomagnetic array by using numerical multichannel predictive filtering has been studied. The result shows that this technique is effective to fit external magnetic disturbance to inner electromagnetic induced difference field and reduce the noise level of difference data successfully. The filter quality factor Q of two examples in this work are 0. 86 and 0.68 respectively. The spectral analysis shows that during geomagnetic-calm days the fourfold-frequency harmonics of S q in difference data are main components. The length of the optimum filter depends on not only the frequency of predicable energy in difference data but also maybe the phase difference between input and expected output data. It is difficult to obtain the filter fitting both the data during magnetic-disturbed days and calm days. The result shows that the conductivity in Yanqing-Huailai basin west to Beijing may be much non-uniform.

PREDICTION OF OCEANIC DATA

The Kalman filter is used to predict the velocity of littoral current, the wave direction, the sea depth and the wave steepness. In this paper the Kazumasa model has been modified to deal with two cases: 1) For the positions a bit far from the shore, the interaction between the velocity of littoral current as well as the wave direction and the sea depth as well as the wave steepness must be considered. 2) For the positions very close to the shore, three new parameters describing the asymmetry wave are introduced to deal with wave breaking. The results from the modified model are compared with observed data, and the comparison indicates that the modified model is better and capable of giving more accurate results.

Natural Gradient Approach to Multichannel Blind Deconvolution

In this paper we study the geometrical structures of FIR filters and their application to multichannel blind deconvolution. First we introduce a Lie group structure and a Riemannian structure on the manifolds of the FIR filters. Then we derive the natural gradients on the manifolds using the isometry of the Riemannian metric. Using the natural gradient, we present a novel learning algorithm for blind deconvolution based on the minimization of mutual information. Some properties of the learning algorithm, such as equivariance and stability are also studied. Finally, the simulations are given to illustrate the effectiveness and validity of the proposed algorithm.

Radio Disturbance Suppression for Ground Wave Radar

A new method of single sample polarization filtering is proposed. The algorithm is fast and suitable for the polarization processing of stationary or nonstationary polarized disturbed signals with one or more independent disturbances. A ground wave polarimetric radar with the ability of radio disturbance suppression is then introduced. Some numerical results demonstrate the effectiveness of single sample polarization filtering method for ground wave polarimetric radar.

Particle filter based visual tracking with multi-cue adaptive fusion

To improve the robustness of visual tracking in complex environments such as: cluttered backgrounds, partial occlusions, similar distraction and pose variations, a novel tracking method based on adaptive fusion and particle filter is proposed in this paper. In this method, the image color and shape cues are adaptively fused to represent the target observation; fuzzy logic is applied to dynamically adjust each cue weight according to its associated reliability in the past frame; particle filter is adopted to deal with non-linear and non-Gaussian problems in visual tracking. The method is demonstrated to be robust to illumination changes, pose variations, partial occlusions, cluttered backgrounds and camera motion for a test image sequence.

All-optical NRZ-to-AMI conversion using linear filtering effect of silicon microring resonator

We experimentally demonstrate 10-Gb/s format conversion from non-return-to-zero （NRZ） to alternatemark-inversion （AMI） using the linear filtering effect of silicon microring resonator. Our discussion and analysis in simulation further show that a 10-Gb/s AMI signal with good quality can be obtained by a resonator with a notch depth larger than 25 dB when the 3-dB bandwidth is 0.4 nm.

A cost driven predictive maintenance policy for structural airframe maintenance

Airframe maintenance is traditionally performed at scheduled maintenance stops.The decision to repair a fuselage panel is based on a fixed crack size threshold,which allows to ensure the aircraft safety until the next scheduled maintenance stop.With progress in sensor technology and data processing techniques,structural health monitoring（SHM） systems are increasingly being considered in the aviation industry.SHM systems track the aircraft health state continuously,leading to the possibility of planning maintenance based on an actual state of aircraft rather than on a fixed schedule.This paper builds upon a model-based prognostics framework that the authors developed in their previous work,which couples the Extended Kalman filter（EKF） with a firstorder perturbation（FOP） method.By using the information given by this prognostics method,a novel cost driven predictive maintenance（CDPM） policy is proposed,which ensures the aircraft safety while minimizing the maintenance cost.The proposed policy is formally derived based on the trade-off between probabilities of occurrence of scheduled and unscheduled maintenance.A numerical case study simulating the maintenance process of an entire fleet of aircrafts is implemented.Under the condition of assuring the same safety level,the CDPM is compared in terms of cost with two other maintenance policies：scheduled maintenance and threshold based SHM maintenance.The comparison results show CDPM could lead to significant cost savings.

Ultrahigh speed OOK-to-PSK conversion using linear filtering in silicon ring resonators

A scheme to achieve ultrahigh speed all-optical format conversion from on-off keying （OOK） to phase-shift keying （PSK） by using the linear filtering in the silicon ring resonators is proposed. It is shown that the OOK-to-PSK conversion can be achieved through a linear signal processing. Simulation results are provided for the 160-Gb/s non-return-to-zero （NRZ）-to-PSK and carrier-suppressed （CS） return-to-zero （RZ）-to-（CS）RZPSK conversions.

Track before detect for point targets with particle filter in infrared image sequences

The problem of detecting and tracking point targets in a sequence of infrared images with very low signalto-noise ratio (SNR) is investigated in this paper. A track before detect algorithm for infrared (IR) point target is developed based on particle filter. The particle filter is used to estimate the state of the target in track stage. The unnormalized weights of the output of the filter are used to approximately construct the likelihood ratio for hypothesis test in detection stage. Experiment results with the real image sequences that SNR is about 2.0 show that the proposed algorithm can successfully detect and track point target.

Processing for laser radar range images

Imaging laser radar can give intensity and range images,which provide integrated 3-dimensional (3D) information about objects.However, dropouts and range anomalies exacerbate range images, which makes their background cluttered and target blurred.For background suppression,a new algorithm that combines intensity image and its mean is presented.By using this algorithm to process actual laser radar range images, most background noises are suppressed.According to range anomalies characteristics,multitemplate selection order mean filtering algorithm is presented and used for actual ladar range images where the distance between two targets is 77 m. This algorithm obtains the clear range image in which the interval of two objects is 75 m.The result shows that the processing algorithm is correct and effective.

Robustness estimation of software-synchronized all-optical sampling for fiber communication systems

The robustness of the software-synchronized all-optical sampling for optical performance monitoring is estimated for 10-Gb/s fiber communication systems. It reveals that the software-synchronized algorithm is sensitive to the signal degradation caused by chromatic dispersion and nonlinearity in optical fibers. The influence of timing jitter and amplitude fluctuation of the sampling pulses is also investigated. It is found that stringent requirements are imposed on the quality of the sampling pulse and the tolerance of 1-dB Q penalty is measured. Considering the practically available optical sampling pulse sources, the results indicate that the amplitude fluctuation of the sampling pulses has the dominant impacts on the software-synchronized method.

Morphological self-organizing feature map neural network with applications to automatic target recognition

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.