204 results about "Filter gain" patented technology

Acoustic noise for wireless or landline telephony is reduced through optimal filtering in which each frequency band of every time frame is filtered as a function of the estimated signal-to-noise ratio and the estimated total noise energy for the frame. Non-speech bands and other special frames are further attenuated by one or more predetermined multiplier values. Noise in a transmitted signal formed of frames each formed of frequency bands is reduced. A respective total signal energy and a respective current estimate of the noise energy for at least one of the frequency bands is determined. A respective local signal-to-noise ratio for at least one of the frequency bands is determined as a function of the respective signal energy and the respective current estimate of the noise energy. A respective smoothed signal-to-noise ratio is determined from the respective local signal-to-noise ratio and another respective signal-to-noise ratio estimated for a previous frame. A respective filter gain value is calculated for the frequency band from the respective smoothed signal-to-noise ratio. Also, it is determined whether at least a respective one as a plurality of frames is a non-speech frame. When the frame is a non-speech frame, a noise energy level of at least one of the frequency bands of the frame is estimated. The band is filtered as a function of the estimated noise energy level.

Noise suppression (speech enhancement) by spectral amplitude filtering using a gain determined with a quantized estimated signal-to-noise ratio plus, optionally, prior frame suppression. The relation between signal-to-noise ratio and filter gain derives from a codebook mapping with a training set constructed from clean speech and noise conditions.

The invention provides a self-adaptive filtering method based on the different measuring characteristics of a GPS (Global Positioning System)/INS (Inertial Navigation System) integrated navigation system, which comprises the following steps: using the data measured by an IMU (Inertial Measurement Unit) and the initial data of an INS to carry out strapdown inertial navigation real-time computation; establishing an integrated filtering system equation of GPS/INS speed and positions; measuring a mutual difference sequence according to the GPS/INS dual systems; statistically calculating the estimated value of a GPS measuring noise covariance; and carrying out self-adaptive Kalman filtering computation. The self-adaptive filtering method provided by the invention realizes real-time tracking of GPS measuring noise and the self-adaptive adjustment of a filtering gain matrix, and enhances the positioning accuracy of the integrated navigation system.

The invention discloses a method for tracking a high maneuvering target, and mainly solves the problems of model mismatching and low tracking precision brought by high maneuvering of a target in the prior art. The method comprises the following steps of: (1) initializing motion states and model parameters; (2) establishing an improved jerk model; (3) forecasting a state of a target; (4) solving a forecast error of an observed value according to receiving data and a state forecast value of radar; (5) determining a fading factor; (6) determining filter gain; and (7) updating the state of the target, and thus realizing target tracking. By the method, the tracking precision of a general maneuvering target is kept, and the tracking performance during sudden change of the state of the target is improved; and moreover, the method can be used for tracking a high maneuvering target.

The invention discloses an adaptive filtering method based on observation noise covariance matrix estimation. The adaptive filtering method comprises the following steps of: constructing a mutual difference sequence and a radial-normal distance difference sequence by utilizing measurement complementary characteristics of different measurement systems in a combined navigation system so as to carryout dynamic estimation on a measurement noise characteristic of a single epoch, measuring an error level of noise mapping according to an estimation result, and constructing an adaptive factor by taking a preset filtering precision as an index to effectively regulate a filtering gain array so as to carry out adaptive Kalman filtering resolution. According to the adaptive filtering method based onthe observation noise covariance matrix estimation, disclosed by the invention, high dynamic estimation of measurement noise characteristics is not only realized, but also the filtering gain array iseffectively updated, and finally the location precision of the combined navigation system is improved.

This invention discloses a noise elimination device and method, which uses two microphones to form two zero point along front and back heart shape microphone to make the main microphone one path of signals composed of aim sound and the other path of signal main comprises noise and then adopts frequency area self-adapting filter; through adjusting filter gains to control whole device wave beam indication range; finally through single channel to remove noise to delete main microphone signal noises.

A method and apparatus identify a clean speech signal from a noisy speech signal. To do this, a clean speech value and a noise value are estimated from the noisy speech signal. The clean speech value and the noise value are then used to define a gain on a filter. The noisy speech signal is applied to the filter to produce the clean speech signal. Under some embodiments, the noise value and the clean speech value are used in both the numerator and the denominator of the filter gain, with the numerator being guaranteed to be positive.

A method includes calculating a first estimated state of charge (SOC) of a battery at a first time, receiving a voltage value representing a measured voltage across the battery at a second time, calculating a filter gain at the second time, and calculating a second estimated SOC of the battery at the second time based on the first estimated SOC, the voltage value, and the filter gain. Another method includes storing, in a memory, a library of equivalent circuit models representing a battery, determining an operational mode of a battery based on a load associated with the battery, selecting one of the equivalent circuit models based on the determined operational mode, and calculating a state of charge of charge (SOC) of the battery using the selected equivalent circuit model.

A system includes first and second wireless nodes having a clock with plural times, a wireless transceiver, and a processor cooperating with the transceiver to transmit and receive packets. The second node transceivers wirelessly communicate with the first or other second node transceivers. The second nodes include a Kalman filter with an output, plural filter gains, and an input representing the difference between: about the time of the clock when a received packet should have ideally been received, and a time when the received packet was actually received as measured by the clock. A circuit provides dynamic adjustment of the filter gains. The Kalman filter output estimates the difference between the time of the receiving node clock and a corresponding one of the times of the transmitting node clock. The second processor cooperates with the Kalman filter output to adjust the times of the receiving node clock.

The invention provides a plate head bent shape detection device which is based on a far infrared image. A measurement method gains the images by the heat radiation of the plate, without any external heat sources or light sources. The invention mainly comprises a CCD camera (charge coupling device), an infrared filter system, an image collecting card, and an image disposal system. The detection device is arranged beside a roller bed which is arranged behind (in front of) a rolling mill; when a hot plate is delivered to the detection system through the roller bed, the CCD camera which is provided with a far infrared filter gains the images of the head of the plate; the image disposal system achieves a smoothening disposal, an image reinforcement disposal, an image cutting disposal, an image morphology disposal, etc. of the gained images so as to gain a shape curve of the head, and fits the curve so as to gain the parameters such as the bent height, the bent curvature, the length of the bent section, etc. of the head of the plate. The invention can eliminate the interference of the steam in the production site to the gained images, the detection speed is fast, the installation of the equipment is simple, the original production process is not changed, and the invention is stable and reliable and suitable for the production sites of medium thick plates and hot continuous rolling.

The invention discloses a multi-target tracking method in the case of loss of measurement data. The multi-target tracking method includes the following steps: initializing probability hypothetical density and potential distribution of a target state set; predicting the probability hypothetical density and potential distribution of the target state set; updating and converting a deviation matrix and a covariance matrix for measurement; introducing a scaling factor, and changing a gain matrix; using an ellipsoid threshold to remove irrelevant measurement data for the weight of a target strength function; performing trimming and merging, and extracting a target state; determining if a target exists or not, returning to the step of predicting the probability hypothetical density and potential distribution of the target state set if so, otherwise ending target tracking. According to the invention, the scaling factor is introduced during updating of a CPHD filter to regulate the filter gain matrix, and the robustness of the filter can be improved; a self-adaption threshold can simplify a measurement set, the observation number of a current observation set is reduced, all the measurement data irrelevant with a detected target is removed, and finally, the computing amount is reduced.

The invention discloses an adaptive unscented Kalman particle filtering method. The method utilizes the theory of the Sage filtering windowing method, also combines the idea of fading, estimates a true covariance matrix of the observed quantity by collecting an epoch innovation vector, and compares the true covariance matrix with the covariance matrix of a filtering recursive model, when a deviation exists between the two covariances, the observed covariance matrix of the system is adaptively adjusted according to the difference. Based on the process, an adaptive fading factor is designed, theobservation noise is further modified, the modified observation noise participates in the solution of a gain matrix, and thus the state estimation can be adaptively adjusted. The scheme of the invention can effectively perform filtering correction on a strongly nonlinear satellite/inertial integrated navigation system, especially when the external noise is abnormal, the filtering gain can be effectively and adaptively adjusted to improve the robustness and positioning accuracy of the system.

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.

The invention discloses a fault estimation method for a class of event-triggered transmission time-varying systems based on an unknown input observer technology. The method comprises establishing a system state space model, and introducing an event trigger signal transmission strategy therein; respectively augmenting an original system state and each possible fault into new state signals, designing filter parameters, recursively solving the least upper bound of an error covariance and minimizing the least upper bound, and calculating the filter gain; utilizing a filter residue corresponding tothe normal situation and a reasonably set threshold to perform fault detection; utilizing a residual matching method to implement fault separation, and taking a fault corresponding component in the corresponding filtered estimation value as a fault estimation value.

The invention provides a fault detection method for a networked control system. The fault detection method comprises the following steps: firstly, two independent Markov chains are used to respectively describe a sensor-to-controller delay and a controller-to-actuator delay, and the networked control system is modeled as a control system with two Markov chains; on the basis, a fault detection filter is constructed and a closed-loop system model is established by using a state augmentation method; and after that, the condition that the closed-loop system is stochastically stable and satisfies the H infinity performance is obtained in the form of a matrix inequality, a controller and filter gain matrix and a solution method of the H infinity reduction level are given, and the relation between the transition probability and the minimum H infinity reduction level is obtained. Examples show that a fault filter obtained by the proposed method is not only sensitive to faults, but also has good robustness to external disturbance, so that the H infinity fault detection problem of the networked control system under the condition that the transition probabilities of the sensor-to-controller delay and the controller-to-actuator delay are both partially unknown is solved.

A method of transforming an N-bit raw wide dynamic range (WDR) Bayer image to a K-bit raw red-green-blue (RGB) image wherein N>K is provided that includes converting the N-bit raw WDR Bayer image to an N-bit raw RGB image, computing a luminance image from the N-bit raw RGB image, computing a pixel gain value for each luminance pixel in the luminance image to generate a gain map, applying a hierarchical noise filter to the gain map to generate a filtered gain map, applying the filtered gain map to the N-bit raw RGB image to generated a gain mapped N-bit RGB image, and downshifting the gain mapped N-bit RGB image by (N−K) to generate the K-bit RGB image.

The invention discloses a dynamic state evaluation method of a generator. The method comprises steps of introducing time-varying multi-dimensional observation noise scale factors into CKF dynamic state evaluation; carrying out on-line adjustment on error measurement according to measurement information so as to allow the noise to be closer to actual noise; and using the adjusted errors to calculate filtering gain so as to precisely correct a state quantity predicated value when bad data is measured, thereby obtaining a precise state quantity evaluation value. The invention also provides a specific construction method for the time-varying multi-dimensional observation noise scale factors and provides solutions for abnormity of inversion of filtering gain. In this way, on the premise that requirements on real-time performance are satisfied, effects on dynamic state evaluation of the generator imposed by measurement bad data can be effectively restrained.

The invention discloses an SINS/DVL integrated navigation method based on improved Sage-Husa adaptive filtering, and mainly aims to solve the problem of integrated navigation based on SINS/DVL under the condition that external measurement noise is unknown. The method mainly comprises the steps of space model establishment, filtering state updating, filtering gain calculation and measurement updating, measurement noise variance matrix calculation, variable sliding window processing and the like. According to the invention, the problems of poor SINS/DVL integrated navigation precision and poor stability under the condition of that the external measurement noise is unknown can be solved. Compared with a Sage-Husa based SINS/DVL integrated navigation method commonly adopted at present, the method has the advantages that the external measurement noise can be accurately estimated, and the integrated navigation precision and the system stability are further improved.

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 an anti-outlier federated filtering method based on innovation orthogonality and belongs to the technical field of data processing. By using the anti-outlier federated filtering method based on the innovation orthogonality, measurement values of a sub-filter are subjected to outlier recognition, and measurement vectors which have outliers are reconstructed by a matrix which satisfies innovation and residual orthogonality, so that the influence of measurement data outliers on filtering precision is effectively reduced; and before main filter fusion, self-adaptive adjustment is carried out on a sub-filter gain and federated filtering information according to the outlier recognition, the influence of continuous outliers on system output stability is reduced, and the filtering precision and stability of the federated filtering are improved.

A sound processing system reduces speaker distortion at high volume levels by attenuating the filter gain and/or tone of audio and mixed output signals. The sound processing system has one or more filters to attenuate the filter gain and tone in response to the high volume levels. The sound processing system also can attenuate the filter gain and tone in response to a sound pressure level, which may be provided by a microphone.