Diffraction separation by plane-wave prediction filtering

Seismic data processing typically deals with seismic wave reflections and neglects wave diffraction that affect the resolution. As a general rule, wave diffractions are treated as noise in seismic data processing. However, wave diffractions generally originate from geological structures, such as fractures, karst caves, and faults. The wave diffraction energy is much weaker than that of the reflections. Therefore, even if wave diffractions can be traced back to their origin, their energy is masked by that of the reflections. Separating and imaging diffractions and reflections can improve the imaging accuracy of diffractive targets. Based on the geometrical differences between reflections and diffractions on the plane-wave record; that is, reflections are quasi-linear and diffractions are quasi-hyperbolic, we use plane-wave prediction fltering to separate the wave diffractions. First, we estimate the local slope of the seismic event using plane- wave destruction filtering and, then, we predict and extract the wave reflections based on the local slope. Thus, we obtain the diffracted wavefield by directly subtracting the reflected wavefield from the entire wavefield. Finally, we image the diffracted wavefield and obtain high-resolution diffractive target results. 2D SEG salt model data suggest that the plane-wave prediction filtering eliminates the phase reversal in the plane-wave destruction filtering and maintains the original wavefield phase, improving the accuracy of imaging heterogeneous objects.

Random noise attenuation by f–x spatial projection-based complex empirical mode decomposition predictive filtering

The frequency–space（f–x） empirical mode decomposition（EMD） denoising method has two limitations when applied to nonstationary seismic data. First, subtracting the first intrinsic mode function（IMF） results in signal damage and limited denoising. Second, decomposing the real and imaginary parts of complex data may lead to inconsistent decomposition numbers. Thus, we propose a new method named f–x spatial projection-based complex empirical mode decomposition（CEMD） prediction filtering. The proposed approach directly decomposes complex seismic data into a series of complex IMFs（CIMFs） using the spatial projection-based CEMD algorithm and then applies f–x predictive filtering to the stationary CIMFs to improve the signal-to-noise ratio. Synthetic and real data examples were used to demonstrate the performance of the new method in random noise attenuation and seismic signal preservation.

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.

Distributed Filtering Algorithm Based on Tunable Weights Under Untrustworthy Dynamics

Aiming at effective fusion of a system state estimate of sensor network under attack in an untrustworthy environment, distributed filtering algorithm based on tunable weights is proposed. Considering node location and node influence over the network topology, a distributed filtering algorithm is developed to evaluate the certainty degree firstly. Using the weight reallocation approach, the weights of the attacked nodes are assigned to other intact nodes to update the certainty degree, and then the weight composed by the certainty degree is used to optimize the consensus protocol to update the node estimates. The proposed algorithm not only improves accuracy of the distributed filtering, but also enhances consistency of the node estimates. Simulation results demonstrate the effectiveness of the proposed algorithm. © 2014 Chinese Association of Automation.

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.

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.

Inversion-based data-driven time-space domain random noise attenuation method

Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter （PEF）, is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data＇s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance.

PASSIVE LOCATION AND TRACKING USING DOA AND TOA MEASUREMENTS OF SINGLE NONMANEUVERING OBSERVER

Passive location and tracking (PLAT) of a moving emitter can be implemented by multi-sited observers or by single maneuvering observer using DOA measurements only. In this article, the principle and method of passive location and tracking of a moving emitter by a single non-maneuvering observer using DOA and TOA measurements are presented and described. Computer simulation of PLAT of a moving emitter in two dimensional plane was implemented. It is shown that convergent and accurate tracking data can be obtained.

An optimal filter based MPC for systems with arbitrary disturbances

In this study, a linear model predictive control(MPC) approach with optimal filters is proposed for handling unmeasured disturbances with arbitrary statistics. Two types of optimal filters are introduced into the framework of MPC to relax the assumption of integrated white noise model in existing approaches. The introduced filters are globally optimal for linear systems with unmeasured disturbances that have unknown statistics. This enables the proposed MPC to better handle disturbances without access to disturbance statistics. As a result, the effort required for disturbance modeling can be alleviated. The proposed MPC can achieve offset-free control in the presence of asymptotically constant unmeasured disturbances. Simulation results demonstrate that the proposed approach can provide an improved disturbance ?rejection performance over conventional approaches when applied to the control of systems with unmeasured disturbances that have arbitrary statistics.

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.

Forecasting and optimal probabilistic scheduling of surplus gas systems in iron and steel industry

To make full use of the gas resource, stabilize the pipe network pressure, and obtain higher economic benefits in the iron and steel industry, the surplus gas prediction and scheduling models were proposed. Before applying the forecasting techniques, a support vector classifier was first used to classify the data, and then the filtering was used to create separate trend and volatility sequences. After forecasting, the Markov chain transition probability matrix was introduced to adjust the residual. Simulation results using surplus gas data from an iron and steel enterprise demonstrate that the constructed SVC-HP-ENN-LSSVM-MC prediction model prediction is accurate, and that the classification accuracy is high under different conditions. Based on this, the scheduling model was constructed for surplus gas operating, and it has been used to investigate the comprehensive measures for managing the operational probabilistic risk and optimize the economic benefit at various working conditions and implementations. It has extended the concepts of traditional surplus gas dispatching systems, and provides a method for enterprises to determine optimal schedules.

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.

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.

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.

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.

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.

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.

Analysis of fault detection method based on predictive filter approach

A new detection method for component faults based on predictive filters together with the fault detectability, false alarm rate, missed alarm rate and upper bound of detection time are proposed. The efficiency of the method is illustrated by a simulation example of a second-order system. It is shown that the fault detection method using predictive filters has a small delay, a low false alarm rate and a low missed alarm rate. Furthermore the filter can give accurate estimates of states even after a fault occurs. The real-time estimation provided by this method can also be used for fault tolerant control.

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.

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.