174 results about "Nonlinear filtering" patented technology

A system (10) analyzes signals representative of a subject's brain activity in a signal processor (12) for information indicating the subject's current activity state and for predicting a change in the activity state. One preferred embodiment uses a combination of nonlinear filtering methods to perform real-time analysis of the electro-encephalogram (EEG) or electro-corticogram (ECoG) signals from a subject patient for information indicative of or predictive of a seizure, and to complete the needed analysis at least before clinical seizure onset. The preferred system then performs an output task for prevention or abatement of the seizure, or for recording pertinent data.

The invention may comprise is a method and system of processing an image such as a road sign wherein the image is viewed from a host such as an automobile. The method begins with the initiating of a scan of an object to obtain an input. The input is converted to a signal before comparing the input with a set of stored inputs to determine a match. The converting step occurs via a fourier or similar transform to produce a transformed input; and, then filters the transformed input using nonlinear filtering.

A strong maneuver-based target tracking method comprises the following steps: initializing parameters, interactively inputting models, judging the covariance matrix, filtering in parallel, updating the model probabilities, interactively outputting the models, filtering in a fixed-delay smoothing manner, and judging whether the state updating is completed; on the basis of the IMM algorithm, the IMM algorithm for recalculating the weight is used, that is, RIMM. The method not only uses the model probabilities, but also takes full advantage of the filtering covariance matrix, so that the tracking accuracy is higher. In addition, an SRCK method is used in the filter forecasting stage, and adopts the spherical integrating criterion and the radial integrating criterion. Compared with a wide UKF algorithm in nonlinear filtering, the target tracking method optimizes the sigma point sampling strategy and the weight distribution in the UKF. Meanwhile, QR factorization is introduced in SRCKF, so that the matrix squiring operation is avoided, and the filtering stability is improved. On the basis, the filtering in a fixed-delay smoothing manner is also introduced, so that the real-time property and the accuracy of target tracking are further improved.

The invention relates to an airborne distributed POS transfer alignment method based on H infinity and CKF (Capacity Kalman Filtering) hybrid filtering. The method comprises the following steps: first, incorporating a deflection deformation error into measurement noise, determining a speed match measurement equation and an attitude match measurement equation, and primarily establishing a transfer alignment model of a system; designing nonlinear filtering, namely a CKF algorithm with high precision based on Kalman filtering; finally, achieving the purposes of weakening and eliminating the deflection deformation and a dynamic lever arm by utilizing good robust adaptive ability of H infinity filtering, designing an H infinity filtering algorithm, combining an H infinity filtering module and the CKF filtering, and designing a new H infinity and CKF hybrid filtering algorithm so as to estimate more accurate position, speed and attitude information of a slave inertial navigation system to complete transfer alignment. According to the airborne distributed POS transfer alignment method based on the H infinity and CKF hybrid filtering, the problem that the position, speed and attitude information of the slave inertial navigation system cannot be estimated accurately under the situation that the deflection deformation is difficult to model is solved, so that the airborne distributed POS transfer alignment method based on the H infinity and CKF hybrid filtering has the characteristics of high precision and strong anti-interference capacity.

A system and method for generating a multidimensional navigation solution utilizes Global Positioning System (GPS) data to obtain highly reliable and accurate navigational solutions in high interference and dynamic environments, at a performance level which has been heretofore unattainable. Additional sensors such as inertial (gyros and accelerometers), altimeters, radars, etc. may be employed in a deeply integrated configuration. The approach taken in the present invention differs from that of previous techniques through its exploitation of nonlinear filtering methods; as a result, the navigation system architecture and processes employed yield significant improvements in navigation system performance, both in code tracking and reacquisition, and in carrier tracking and reacquisition. The improvements are particularly significant at low signal/noise ratios, where conventional approaches are especially susceptible to loss of code lock or carrier lock.

The invention discloses a nonlinear system state estimation method based on Kalman filtering positioning, and proposes a strongly adaptive Kalman filtering mechanism which combines a nonlinear filtering algorithm and Kalman filtering. The method comprises the steps: carrying out the simultaneous estimation of node positions and channel parameters through employing an RSSI state estimation algorithm based on square root volume Kalman filtering, and obtaining the estimation value of a state vector; employing the Kalman filtering for further processing according to the linear change of a state equation, obtaining optimal estimation, and building a strongly adaptive square root volume Kalman filtering algorithm; giving the design steps of the strongly adaptive square root volume Kalman filtering algorithm; and calculating a theoretical square root error lower bound under a state space model based on the RSSI state estimation. The method enables an estimation result to be improved, and improves the precision. The method does not need to excessively depend on improper initial conditions, can be well adapted to a highly nonlinear system, and is not liable to enable the algorithm to be divergent and ineffective.

A method for beautifying a human face in a digital image is adapted to beautify a face area of an input image. The method includes setting a selection window to select a partial image area in the input image; setting a target pixel in the selection window, and setting other pixels as comparison pixels; performing a detail checking process according to a variance between the target pixel; performing a luminance checking process on the target pixel to determine; performing a nonlinear filtering process to filter the target pixel by using a nonlinear filter to generate a filtered value, and providing a mixing ratio to mix the target pixel with the filtered value at the mixing ratio to generate a completed pixel; replacing the original target pixel with the completed pixel; and repeating the above steps until all pixels are completed.

Methods and apparatus for conditioning communications signals based on detection of high-frequency in the polar domain. High-frequency events detected in a phase-difference component of a complex baseband signal in the polar domain are detected and used as a basis for performing hole-blowing on the complex baseband signal in the quadrature domain and/or nonlinear filtering either or both the magnitude and phase-difference components in the polar domain. Alternatively, high-frequency events detected in the phase-difference signal that correlate in time with low-magnitude events detected in a magnitude component of the complex baseband signal are used as a basis for performing hole-blowing on the complex baseband signal in the quadrature domain and/or nonlinear filtering either or both the magnitude and phase-difference components in the polar domain.

The invention relates to a method of particle filtering based on multi-suggesting distribution, which belongs to fields of signal processing, artificial intelligence and machine vision. The invention uses various suggesting distribution in the frame of particle filter: checking, firstly, probability distribution, extended Kalman filter, unscented Kalman filter etc., treating a sample particle using dividing and conquering sampling strategy, dividing total particle numbers into a plurality of portions, extracted from different suggesting distribution respectively. To this end, the run time of particle filter can reduced and the operating efficiency is able to be improved, while the estimated precision of particle filter can not be lost. User may carry out parameter arrangement according to the demand for time and precision. The invention has extensive application prospect in the field which relates to nonlinear filtering problem.

One embodiment of the present invention provides a system that uses nonlinear filtering while resizing an image to preserve sharp detail. The system starts with an original image, which is a digital image comprised of a plurality of pixels. Next, the system resizes the original image. This involves first producing an initial resized image by using neighboring pixel values in the original image (or a conventional resizing operation) to produce initial values for subpixel locations in the original image. The system then applies a nonlinear filter using pixel values in the original and initial resized images to produce a resized output image. When this nonlinear filter updates pixel values, it weights the contributions of neighboring pixels nonlinearly. This non-linear weighting of neighboring pixels can be used to significantly reduce (or eliminate) the mixing pixel values from both sides of a sharp edge caused by conventional linear filters, and can thereby alleviate the problem of blurring sharp edges during image resizing operations.

The invention discloses a target tracking method based on an LSTM neural network, and belongs to the technical field of target tracking. According to the method, the LSTM is used for tracking the complex and non-linear motion target, and the problems that target tracking is difficult, a target model is difficult to establish and the tracking precision is low are solved; the method comprises the following steps: firstly, acquiring latitude and longitude information and speed information of a target, and processing acquired data; designing an LSTM neural network structure for single target tracking; and finally, adjusting LSTM neural network parameters to realize target tracking. According to the method, the nonlinear filtering process is effectively simplified, and a complex nonlinear target can be effectively tracked; the establishment of a target motion model and the utilization of a traditional filtering algorithm are not needed; estimating the target motion state of the next momentby using historical target motion information; adjusting internal parameters of the neural network by using a back propagation algorithm; the learning rate attenuation method reduces the calculation amount and improves the precision.

The invention provides an application method of an improved trackless Kalman filtering algorithm to underwater integrated navigation. The application method comprises the following steps: defining a coordinate system required to be used; establishing a state equation of an SINS/DVL subsystem at the underwater submersible stage; establishing a measurement equation of the SINS/DVL subsystem at the underwater submersible stage; establishing a state equation of the SINS/DVL subsystem at the water surface position correction stage; establishing a measurement equation of the SINS/DVL subsystem at the water surface position correction stage; establishing a nonlinear filtering equation at the underwater submersible stages; after a period of underwater diving, an AUV emerges from the water surface,the nonlinear filtering equation at the water surface position correction stages is established, an improved unscented Kalman filter solution is performed, the time update, the measurement update andthe filter update are completed, and the timing position information correction is completed. The application method provided by the invention has the benefits that the filtering settlement efficiency and the navigation accuracy of an underwater integrated navigation system can be improved, the complexity of filtering algorithm calculation is reduced, and the real time and the stability are ensured.

The invention discloses an earth-Lagrange combined constellation autonomous orbit determination method based on inter-satellite ranging. The method includes following steps: step 1, establishing a state equation of an earth-Lagrange combined constellation autonomous orbit determination system; step 2, establishing a measurement equation of the earth-Lagrange combined constellation autonomous orbit determination system; step 3, determining a filtering method for realizing orbit parameter estimation; step 4, specifically realizing the earth-Lagrange combined constellation autonomous orbit determination method based on a selected filtering algorithm. By introducing a Lagrange satellite, the problem of deficient range when only inter-satellite ranging information is utilized for autonomous orbit determination is solved effectively, and complexity of system equipment is lowered; statistical characteristics of system noise are estimated online in real time through a self-adaptive nonlinear filtering algorithm, so that requirements on noise prior information are low, stability of the autonomous orbit determination filtering algorithm is improved, and autonomous orbit determination accuracy is improved.

Nonlinear filtering and deblocking applications utilizing SIMD (single instruction multiple data) sign and absolute value operations are disclosed. The method of one embodiment includes receiving first data for a first block and second data for a second block. The first data and said second data include a plurality of rows and columns of pixel data. A block boundary between the first block and the second block is characterized. A correction factor for a deblocking algorithm is calculated with a first instruction for a sign operation that multiplies and with a second instruction for an absolute value operation. Data for pixels located along said block boundary between the first and second black are corrected.

The invention discloses a Mars final approaching section autonomous navigation method based on relative measurement information, and belongs to the technical field of deep space exploration. For achieving Mars final approaching section autonomous navigation, a final approaching and surrounding dynamic model is established. According to differential information of X-ray pulsar arrival time and Doppler velocity measurement information of a Mars orbiter, based on nonlinear filtering algorithm, the states of an approaching detector and the Mars orbiter undergo joint estimation, absolute navigation is achieved through utilization of two kinds of relative measurement information including the differential information of X-ray pulsar arrival time and the Doppler velocity measurement information of the Mars orbiter, and the accuracy of Mars final approaching section autonomous navigation and the accuracy of entry point state estimation are improved. The method has the following advantages: introduction of a planet ephemeris error is avoided, the adverse effect on navigation performance due to nondeterminacy of pulsar parameters is limited, the problem of state divergence resulting from long observation time of pulsars is overcome, the navigation performance of two detectors is improved at the same time, and the navigation filter accuracy and the convergence speed are increased.

A nonlinear filtering system determines the duration of a designated event in an SPR sensorgram, selects a filter length based on the determined duration of the designated event in the SPR sensorgram, and applies a nonlinear filter, having the selected filter length to the SPR sensorgram, to establish an output signal.

The invention relates to a no-gyro satellite gesture determination method based on tensor product multi-cell robust heavy hydrogen (H2) filtering, and belongs to the technical field of aircrafts. As for the nonlinear characteristics of satellite gesture dynamic equations and kinematical equations, the no-gyro satellite gesture determination method based on the tensor product multi-cell robust H2 filtering provides the multi-cell robust H2 filtering based on tensor product conversion to transform a nonlinear filtering problem into a linear filtering problem. The no-gyro satellite gesture determination method based on the tensor product multi-cell robust H2 filtering includes the steps of first establishing a state equation and a star sensor measurement equation of a gesture determination system and transforming a nonlinear system into a linear variable parameter error system by utilizing Jacobian linearization, then establishing linear parameter varying (LPV) system multi-cell model description according to tensor product model transformation and acquiring state estimation correction amount of the gesture determination system through combining with the robust H2 filtering, and last correcting gesture single-step pre-measurement acquired through a extended Kalman filter (EKF) method by utilizing the estimation correction amount to obtain a gesture estimation value. Accordingly, real-time updated filtering gain in the EKF method is avoid, and filtering calculated amount is reduced greatly.

An embodiment of the invention provides an echo detecting method, a device thereof and a system, wherein the method comprises the steps of receiving a far-end audio signal; performing nonlinear filtering processing on the far-end audio signal, determining a first echo detecting signal which comprises a nonlinear echo predicating signal; performing linear filtering processing on the first echo predicating signal, and obtaining a second echo predicating signal, wherein the second echo predicating signal comprises a nonlinear echo predicating signal and a linear echo predicating signal. The echodetecting method, the device thereof and the system can realize identification of the nonlinear echo component and the linear echo component in an echo signal and improves predication accuracy of theecho signal, thereby effectively eliminating the nonlinear echo component and the linear echo component in the echo signal and improving calling quality.

The invention discloses a target tracking method based on interactive multiple models. The target tracking method comprises the following five steps of 1, according to the target dynamic condition, setting five groups of Singer model parameters, and building five Singer models; 2, performing interactive multiple model nonlinear filtering on the five Singer models, and evaluating the movement speed, acceleration and position information of a target; 3, by using the movement speed and acceleration of the target, calculating the turning angle rate of the target; 4, comparing the turning angle rate of the target and a set threshold value, and judging whether the turning movement occurs or not; when the turning movement does not occur, using the position information obtained in step 2 as a target tracking result; 5, when the turning movement occurs, selecting turning angle rates of three adjacent moments to build three turning models, and performing the interactive multiple model nonlinear filtering, so as to obtain the target position as the target tracking result. The target tracking method has the advantages that the implementation is easy, the target tracking accuracy is improved, and the reliability and accuracy of the target tracking are guaranteed.

Systems and methods of estimating an efficiency of a section of a steam turbine are disclosed. The systems and methods include determining a set of measurement data obtained directly from a set of sensors on the steam turbine, determining a set of derived data relating to measurements that cannot be obtained directly from the set of sensors, and estimating the efficiency of the section using the set of measurement data and the set of derived data. The systems and methods disclosed use physics-based models combined with nonlinear filtering techniques to estimate steam turbines' efficiencies when physical sensors are not available. These models capture the behavior of different components of the power plant, including all steam turbine sections, admission and crossover pipes, flow junctions, admission and control valves.

The invention relates to a nonlinear filtering method based on polytope differential inclusion, which belongs to the technical field of system filtering and control. The method comprises the steps of describing a nonlinear filtering error system with a PLDI (Programming Language Design And Implementation) model, converting a nonlinear filtering algorithm design problem to a linear uncertain system robust filtering algorithm design problem, designing a dynamic equation for estimated error correction solving by a hybrid robust H2/H-to-infinity filtering method, then designing a discrete nonlinear filtering equation by combining an EKF (Extended Kalman Filter) one-step prediction equation, and applying the discrete nonlinear filtering equation to a nonlinear discrete system to obtain the state estimation of the nonlinear discrete system in real time. According to the method, the nonlinear filtering design is simplified, the filtering gain is not required to be updated in real time, and the jacobian matrix is not required to be computed in real time in the implementation process, so that the computation amount is reduced greatly, the real-time performance of the nonlinear filtering is improved effectively, and the method is applicable to the design of a nonlinear filter.

The invention discloses a nonlinear-filtering-based satellite navigation tracking loop lock loss detection method, which comprises the following steps of: (1) determining the state quantity and observed quantity of a satellite signal tracking loop; (2) establishing a state equation and an observation equation corresponding to the state quantity and the observed quantity obtained by the step (1) respectively; (3) establishing a satellite navigation tracking loop filter based on the state equation and the observation equation obtained by the step (2); (4) setting a false alarm probability according to the counting characteristics of a fault detectable quantity, and determining a detection threshold under the condition of the false alarm probability; (5) setting a loop updating cycle, and calculating a covariance matrix of information in loop updating of each time; (6) setting a lock loss detection cycle to be integrally times the loop updating cycle and not less than the loop updating cycle; (7) calculating the fault detectable quantity in detection of each time, and comparing the fault detectable quantity with the detection threshold; and (8) if the fault detectable quantity is greater than the threshold, resetting the loop filter. By the nonlinear-filtering-based satellite navigation tracking loop lock loss detection method provided by the invention, tracking loop lock loss caused by the sudden change of the observed quantity can be effectively detected, and the filter can be timely reset, thereby improving the fault tolerance and robustness of the nonlinear-filtering-based satellite navigation tracking loop.

An digital image input, possibly being either pre-compressed or decompressed, is enhanced; its edges are preserved while any compression artifacts, like blocking and ringing, are simultaneously reduced. The enhancement method enhances images with luminance and chrominance functions, incompletely defined or undefined, on a set of pixels so that the missing information is extrapolated while the image is simultaneously enhanced. The method consists of up to three integrated sub-processes: the image sharpening flow; the de-quantization filtering; and the means of control of the local rate of flow. The image sharpening flow is an iterative nonlinear filtering schema intertwining a local median filter and a suitably chosen linear filter. A local geometric control mechanism allows selective application and adaptation of an algorithm allowing selective removal of local artifacts. De-quantization filtering ensures that the enhancement process does not alter information contained in the image beyond its quantization constraints.

The present invention relates to a failure detection method for a gas circuit part of an aero-engine based on adaptive particle filtering. Compared with linear filtering and a linear model method, gas circuit health diagnosis based on a nonlinear filtering algorithm and a nonlinear component level model solves the diagnostic problem of failures of component performances in a transition state of the engine, is capable of fully mining nonlinear characteristics of the engine and improves the precision and the reliability of diagnosis, and solves the problem of limitation of performing gas circuit component failure diagnosis by using a centralized structure according to a standard particle filtering algorithm; and furthermore, the method provided by the present invention takes a fused filtering structure as a basis, and by virtue of two steps of estimating noise standard deviation in real time and adding inequality constraints, defects of the standard particle filtering, such as heavy calculation burden of a central processing unit, ignorance of effective prior information and low robustness of measured noise, can be effectively avoided, so that the filtering precision is improved, and the accuracy of gas circuit health diagnosis is ensured.

The invention relates to a target tracking method under arbitrary linear constraints, comprising: acquiring target position measurement information from an observation radar to construct a measurementvector, wherein the measurement vector comprises distance measurement and azimuth measurement of a target relative to the original of an observation radar coordinate system; performing state augmentation of the state vector of the target at the current time point using the state vector of the target at previous time points to obtain an augmented state vector and its corresponding state equation,wherein the augmented state includes states of the k time point and the previous d consecutive time points, and d represents the time span of the augmented part; constructing pseudo-measurement according to the linear trajectory shape of the target motion to describe the arbitrary linear constraint relationship, and augmenting the pseudo-measurement into the measurement vector to obtain an augmented measurement equation; and using a nonlinear filtering method, and performing filtering by using the augmented state equation and the augmented measurement equation. The method constructs the pseudo-measurement, uses the linear trajectory shape information to improve the filtering precision, and provides a new solution for the target tracking problem under any linear constraints.

The invention discloses a particle filtering method based on differential evolution and belongs to the field of signal processing and nonlinear filtering. The method is an improvement on particle filtering and is mainly characterized in that significance distribution of particle filtering is generated through unscented Kalman filter so as to make full use of latest observation information; differential iteration optimization processing instead of traditional resampling operation is carried out on sampled particles generated through significance distribution so as to obtain an optimal particle point set. According to the method, the process of simply duplicating or discarding particles through a traditional resampling algorithm is replaced by the process of evolving or optimizing the particle set through a differential evolution algorithm, the problems of particle degeneracy and particle impoverishment during particle filtering are effectively mitigated, the utilization ratio of the particles is improved, and estimation accuracy is higher; the method has good application prospects in the field of signal processing and nonlinear filtering.