33 results about "Correlation entropy" patented technology

The invention relates to the technical field of data processing, in particular to a backtracking analysis model construction method based on an attack chain, which comprises the following steps that: a kernel layer security agent acquires process, file and network behavior characteristics in a hardware isolation environment, and generates an event tuple; the tensor network pipeline performs three-dimensional decoupling mapping on the tuple into a behavior fingerprint vector, an orthogonalization noise feature and an asymmetric adjacent tensor, and compresses the behavior fingerprint vector, the orthogonalization noise feature and the asymmetric adjacent tensor into a space-time topology tensor block; the reinforcement learning controller constructs a directed acyclic graph based on the tensor blocks, calculates connectivity loss and outputs an event risk score; the dynamic routing engine constructs a decision tree model according to the risk mark, the burst frequency and the correlation entropy, and implements three-level shunting and a multiple simulation system to generate an anti-interference index; and when the deviation between the physical trajectory and the digital model exceeds the tolerance, the closed-loop feedback weight coefficient updates the loss function parameter and adjusts the channel resource weight. And the problem of threat discovery delay caused by attack chain breakage under massive events is solved.

The invention discloses a factor graph multi-source information fusion integrated navigation method based on a correlation entropy theory. The method comprises the following steps: constructing a heterogeneous sensor information factor graph model; sensor measurement and prediction state variables are equivalent to two kinds of random variables according to the correlation entropy theory, the random variables are expressed in a correlation entropy kernel function mode, cost function optimization is carried out by introducing an adjustment factor and maximum correlation entropy information, and real-time dynamic adjustment is carried out on the information weight of each sensor; constructing an initial pre-integration object, continuously reading IMU (Inertial Measurement Unit) data, carrying out pre-integration calculation, and storing a current pre-integration result into a sliding window state list; a residual factor is added, the state in the sliding window is set as an optimization variable, iterative optimization is carried out, an optimization objective function is solved, and an iterative optimization combination navigation result is obtained; and evaluating the navigation precision. According to the method, on the premise of ensuring the robustness of the system, the inhibition capability on the abnormal value of the underwater complex environment is remarkably improved, and the pose determination accuracy is improved.

The invention discloses a generator state estimation method and system considering noise and parameter uncertainty constraints. The method comprises the following steps: acquiring model parameters and dynamic state vectors of a generator, and establishing augmented state vectors; performing unscented transformation on the augmented state vector to obtain a particle set; improving to obtain robust mixed Kalman particle filtering, and in the process of performing unscented Kalman filtering on an augmented state vector, taking correlation entropy maximization of a measurement information sequence as a target function, and solving by adopting a fixed point iteration method to obtain a filtering gain; determining a filtering gain according to the updated state of the measurement information; robust mixed Kalman particle filtering is executed, physical constraints of model parameters serve as a feasible region, after resampling, projections of the model parameters in new-generation particles exceed the feasible region, the model parameters are set to be closest boundary points, and then resampling is conducted again; a weighted average value of the particle set is an optimal joint estimation value, a dynamic state estimation value and a model parameter identification result are separated, and reliable uncertainty quantization is provided for state and parameter estimation.

The invention provides a reduced Gaussian kernel adaptive filtering method and system based on nearest center estimation, and relates to the field of data processing, and the method comprises the steps: obtaining an initial parameter and a training set, and constructing an adaptive filtering system based on the initial parameter; performing cluster division on an input signal, obtaining a feature vector of the input signal by calculating a reduced Gaussian kernel, and selecting a corresponding sub-filter according to a cluster for prediction to obtain prediction output; constructing a target optimization problem according to a maximum correlation entropy criterion based on an error between prediction output and expected output, and converting the target optimization problem into a convex optimization problem; solving a convex optimization problem by hybridizing three conjugate gradients, and updating the weight of a sub-filter; and repeating the steps to converge the error of the adaptive filtering system, and completing the adaptive filtering process. According to the method, the problem of dimension disasters existing in a traditional feature mapping method is solved by reducing the Gaussian kernel, the calculation complexity is remarkably reduced, and meanwhile unnecessary calculation overhead is greatly reduced through nearest center estimation.

The invention relates to the technical field of supply chain monitoring, and particularly discloses an automatic sensor detection method and system based on a supply chain, and the method comprises the steps: generating a hierarchical pressure gradient field and an event node distribution diagram through obtaining the transportation vibration energy; if a creep coefficient resetting algorithm is triggered, mapping the event node graph into a weighted directed graph, extracting a key conduction path, and positioning an unpacking release type drift node; and introducing an unpacking stress correction term expansion cross-correlation entropy model, and determining a priority compensation strategy in combination with a time window information entropy to realize dynamic correction of sensor drift. The system comprises a hierarchy analysis module, a topology construction module, a lag positioning module and a drift compensation module. Through cross-scale modeling of vibration energy, material creep and drift compensation, the problem of sensor reading lag caused by stress accumulation in a supply chain can be reduced, and the packaging optimization decision efficiency is improved.

The invention belongs to the technical field of rigid body posture estimation, and discloses a rigid body posture estimation method, system and device based on generalized correlation entropy geometric filtering, and the method comprises the steps: obtaining target motion information, and building a discrete nonlinear kinematics model; the discrete nonlinear kinematics model is initialized; sigma points of a posterior state covariance matrix and a process noise covariance matrix are generated respectively, and the sigma points are propagated and updated through manifold fast unscented transformation; obtaining a prediction state mean value, a priori state covariance matrix and a square root of the priori state covariance matrix; updating the central parameters based on the information of the historical moments; calculating a pseudo measurement matrix and updating the gain; correcting the state estimation by using the gain; judging whether convergence occurs or not, and if not, continuing iteration; otherwise, outputting a posterior state estimation value as a final rigid body posture estimation result at the moment. According to the method, the problem that the robustness and precision of rigid body posture estimation in a non-zero mean value and non-Gaussian noise environment are reduced is effectively solved.

The application discloses a double-fed induction generator rotor speed estimation method based on maximum cross-correlation entropy weighting, and belongs to the field of motor control. The method creatively applies cross-correlation entropy theory to noise covariance estimation of EKF, and designs a complete algorithm system containing dynamic weighting, adaptive kernel bandwidth, mixed robust weighting and numerical reinforcement. The progressiveness is reflected in that the method comprehensively solves the shortcomings of traditional methods in response speed, parameter disturbance resistance and non-Gaussian noise resistance. Theoretical proof and comprehensive simulation experiments prove that the method has better estimation accuracy, robustness and reliability in complex industrial environments such as wind power generation.

The application relates to a three-dimensional extended target tracking method of an adaptive Gaussian process under unknown measurement noise. Firstly, a forgetting factor is adaptively updated by recursively calculating the change rate of the Cramer-Rao lower bound trace, so that the dynamic optimization of filter parameters is realized; then, a projection method based on GP is used to represent a three-dimensional cross-shaped target, three-dimensional point clouds acquired by a sensor are projected to three orthogonal planes, and the three-dimensional extended shape of the target is reconstructed by using two-dimensional contour information; finally, a robust state estimator is constructed by fusing variational Bayesian inference and MCC, the joint posterior distribution of unknown measurement noise and system state is solved by variational approximation, and a cost function is constructed based on the maximum correlation entropy criterion, so that the influence of abnormal values is inhibited in a reweighted manner. The tracking method provided by the application can significantly improve the tracking precision and robustness of a three-dimensional extended target under unknown strong non-Gaussian measurement noise.

The invention discloses a robust electromagnetic inverse scattering inversion method based on maximum correlation entropy, and belongs to the technical field of electromagnetic inverse scattering and signal processing. According to the method, a maximum correlation entropy criterion in an information theory is introduced into a contrast source inversion framework, and a novel cost function which is composed of a data fidelity item and a state fidelity item and is based on MCC is constructed; equivalently converting the minimization problem of the non-convex cost function into a series of iterative weighted least square sub-problems by adopting a positive semi-definite optimization technology; in the iteration process, the influence of outliers in the data is dynamically suppressed through a self-adaptive weighting mechanism depending on a previous iteration residual error, and an alternating optimization strategy is adopted to efficiently solve a contrast source and a contrast function. According to the method, the strong outlier in the data can be self-adaptively suppressed, the reconstruction precision and robustness are far better than those of a traditional method in a non-Gaussian noise environment, and meanwhile, the method also shows superior or equivalent performance in a noiseless or Gaussian noise environment, and has extremely high practical value.

The application discloses a kind of anti-impact noise methods of mixed correlation entropy algorithm based on deviation compensation, comprising the following steps: signal is collected, and the noisy input signal of filter is obtained;Filter calculates the weight coefficient of current time k, and input signal passes through filter, and the filter output signal of current time k is obtained;S30, error signal is obtained based on reference signal through subtractor;Optimization cost function, introduce mixed correlation entropy criterion, by the convex combination of two different kernel width Gaussian functions as kernel function;Compensate input noise, by introducing unbiased criterion, the deviation amount caused by input error is derived in complex domain to compensate;S60, weight coefficient update;S70, repeat, minimize error signal, gradually approach reference signal, so as to achieve the effect of noise suppression.The application can effectively suppress the interference of input noisy and impact noise, improve the flexibility of algorithm and further improve the performance of algorithm.

This invention discloses a single-shot direction-of-arrival (DOA) estimation method based on hyperbolic tangent kernel correlation entropy. The method involves establishing a single-shot sampling signal model; constructing a low-order matrix based on hyperbolic tangent kernel correlation entropy; constructing an orthogonal projection matrix to obtain a single-shot weighted signal subspace fitting equation based on hyperbolic tangent kernel correlation entropy; initializing a quantum locust population and setting parameters; calculating the fitness value of the quantum locust's mapped state position and recording the quantum position corresponding to the mapped state position with the largest fitness value; updating the quantum locust's social position and adaptive weight coefficients to generate Fibonacci weights; updating the quantum locust's quantum position and global optimal position using a simulated quantum rotation gate according to the locust and Fibonacci search strategy; calculating the iteration to the maximum number of iterations and outputting the mapped state position of the last generation of globally optimal quantum locusts as the DOA estimation result of the single-shot direction finding. This invention can effectively perform direction finding for both independent and coherent sources under impulsive noise conditions.

The invention discloses an improved parallel factor target parameter joint estimation method based on mixed multiple correlation entropy, and relates to the technical field of radars. The method mainly comprises the following steps: performing PARAFAC decomposition based on a dynamic weight adaptive mechanism on a transmitting angle equivalent estimation matrix, a receiving angle equivalent estimation matrix and a Doppler frequency equivalent estimation matrix, and constructing a cost function of PARAFAC decomposition based on mixed multiple correlation entropy; converting the constructed cost function into a weighted least square problem based on a semi-quadratic optimization theory, and solving a factor matrix closed analytic solution of a target parameter; and obtaining an estimated target parameter based on the factor matrix closed analytic solution of the target parameter. The method can be applied to joint estimation of Doppler frequency shift, transmitting angle and receiving angle parameters of a low-altitude environment target, automatic pairing can be achieved, and a more reliable and efficient technical solution is provided for the field of low-altitude security and protection.

The application provides a measurement preprocessing method based on correlation entropy and attention mechanism. The dynamic weight is calculated through the Gaussian kernel correlation entropy of the historical innovation and the current innovation in the sliding window, and the abnormal measurement is adaptively identified and suppressed. The method adopts a feedforward inhibition mechanism to block noise propagation, while retaining the optimality of the KF framework, and realizes efficient anomaly detection with lower complexity. Simulation experiments show that in the Gaussian mixed noise and impulse noise scene, the application significantly reduces the mean square error of KF, the dynamic window mechanism is faster than the noise statistical modeling algorithm in response speed, the tracking accuracy is equivalent to Huber-KF, and the parameter adaptability is stronger, which verifies the robustness and tracking performance of the method in non-Gaussian noise, especially in the impulse noise environment, and provides theoretical support for subsequent expansion to the maneuvering target scene and deep learning fusion.

The present application belongs to the technical field of intelligent control, and particularly relates to a 3PE anticorrosion layer production process parameter control method based on intelligent algorithm. The method first collects historical data and real-time production data, and obtains a standardized data set through outlier rejection, missing value filling and data normalization processing. Then, an improved multi-objective optimization algorithm is used for training, taking yield rate and coating uniformity as the target, and outputting the first predicted process parameter containing the optimal parameter interval. Then, a process parameter dynamic graph model is constructed, and the node correlation entropy is calculated and the parameter adjustment is triggered according to the deviation degree of real-time parameters and predicted parameters, historical correlation coefficient between parameters and expert experience, and the optimized production parameters are obtained through multiple iterations. The present application can accurately determine and dynamically adjust the parameters, effectively handle the complex correlation between data, and improve the stability of 3PE anticorrosion layer production and product quality.

The present invention relates to an adaptive robust anti - bias information fusion method for GNSS / INS tightly - coupled navigation combined with a neural network, belonging to the field of integrated navigation, and solving the problem of anti - bias information fusion. In the method, a neural network model for fitting the kernel parameters and error distribution of each channel of a multi - channel heterogeneous generalized correlation entropy Kalman filter is established and trained. During the INS recursive error estimation process of the GNSS / INS tightly - coupled navigation system, when the running time reaches a length of a neighboring time observation window, the kernel function parameters of each channel corresponding to each satellite in the filter are predicted. In the generalized correlation entropy Kalman filter, the predicted kernel function parameters of each channel are used for INS error state estimation and recursive error correction. When the running time reaches the length of the next neighboring time observation window, the kernel function parameters of each channel are predicted again. The present invention can cope with general noise with a non - Gaussian overall distribution and has anti - bias ability.

The application discloses a kind of absolute polynomial correlation entropy DOA estimation methods under pulse noise environment, receive signal by nested array structure antenna, according to received signal and absolute polynomial correlation entropy operator APCO calculate corresponding covariance matrix;With vectorization, ordering and redundancy are removed, obtain new virtual uniform linear array received signal;The continuous part of half wavelength between difference virtual array elements is spatially smoothed, and the spatially smoothed covariance matrix is constructed, and then the precise estimation of DOA is obtained by using the MUSIC method.The method uses absolute polynomial correlation entropy operator APCO to suppress pulse noise outliers, constructs APCO matrix, not only without relying on prior knowledge of noise, but also can get rid of the limitation of traditional correlation entropy algorithm, and can realize high-precision DOA estimation in strong pulse noise, low SNR, limited number of snapshots environment.

The application provides a bearing fault diagnosis method based on correlation entropy and short-time Fourier transform, and the diagnosis method is as follows: step 1, collecting a vibration signal x(i), the sampling length is N, the signal x(i) is an N*1 column vector, and the kernel matrix M of the signal is calculated x ,M x (i,j) = K[x(i),x(j)], K(·) is a kernel function, e (·) is a natural exponential function, sigma is a kernel length, i, j = 1, 2, 3,..., N, M x is an N*N square matrix, since the traditional short-time Fourier transform is susceptible to interference noise, and the bearing outer ring fault characteristic frequency and the system inherent vibration frequency are coupled with each other, it is difficult to effectively extract the bearing outer ring fault characteristic information under the noise interference, compared with the traditional short-time Fourier transform method, the application can effectively suppress the Gaussian noise and non-Gaussian noise in the signal, has the self-adaptive noise reduction performance, and can highlight the bearing fault characteristics.

The invention discloses a maximum correlation entropy Kalman filtering method based on a rational kernel function, and belongs to the technical field of signal processing. The method specifically comprises the following steps: 1) constructing a linear system equation and a measurement equation; step 2) selecting a kernel width of a rational quadratic kernel function, and initializing a system state and a covariance; 3) according to a system equation, updating one-step prediction of a state and a covariance; (4) the state value is initialized again at the fixed point iteration starting moment; 5) performing system model deformation according to the initial system and the measurement equation to obtain an error vector after deformation; 6) according to a concept based on a weighting criterion and entropy, defining a cost function by using an error vector; 7) for the cost function, solving an optimal solution of a state estimation value according to a maximum correlation entropy principle; and step 8) estimating the variance of a posterior estimation value. Compared with the existing GSKF, HF and MCKF algorithms, the method provided by the invention has the advantage that the accuracy of state estimation and the robustness of estimation are greatly improved.

The invention discloses a stratum thickness qualitative prediction method and system, and belongs to the technical field of exploration geophysics. The method comprises the following steps: determining a target horizon in three-dimensional seismic data, and calculating a spectral decomposition result based on three-dimensional continuous wavelet transform; initializing a weight vector of maximum correlation entropy self-organizing mapping, and inputting spectral decomposition feature data; calculating a maximum correlation entropy distance to find a winning neuron; and iteratively updating the neighborhood weight to complete prediction, and verifying validity by using a wedge-shaped model. The system comprises a target module, an initialization module, a calculation module and a prediction module of corresponding functions. According to the method, comprehensive features are extracted through three-dimensional wavelet transform, unsupervised neural network clustering is combined, a large number of labels are not needed, noise immunity is high, the problems that a traditional method is low in thin layer prediction precision and depends on the labels are solved, and the method is suitable for qualitative prediction of the stratum thickness in oil-gas exploration.

This invention discloses a robust target tracking method using a dual-entropy fusion multi-random matrix maneuvering extension. The method employs an interactive multi-model fusion architecture. In the fusion step, a cost function is established using the target motion state estimate of the previous time-instance sub-model as the independent variable, based on the correlation entropy criterion. Maximizing this cost function yields the fused target state estimate. Using relative entropy as the criterion, the sum of information gains of each sub-model regarding the target motion state covariance and target morphological distribution parameters is minimized. Then, a correlation entropy cost function is established using the filtered target motion state estimate of the current time-instance sub-model as the independent variable. Maximizing this cost function achieves target motion state estimation fusion. Simultaneously, the total information gain of each sub-model regarding the target motion state covariance and target morphological distribution parameters is minimized again to obtain a weighted estimate, ultimately yielding a robust joint estimate of the target motion state and morphology at the current time.

The application belongs to the field of array signal processing, and discloses a small snapshot direction of arrival estimation method and system of a quantum dream optimization mechanism under impulse noise and a storage medium. First, an array receiving model under impulse noise environment is established to obtain a receiving data matrix; a robust covariance matrix is calculated based on the data matrix, and a hybrid kernel correlation entropy covariance matrix is constructed by using at least two different kernel functions, and an enhanced correlation entropy covariance matrix is obtained by fusing the two; then, eigenvalue decomposition is performed on the enhanced correlation entropy covariance matrix to establish a weighted signal subspace fitting target function; a quantum dream optimization algorithm is used for solving, and the iteration process is divided into an exploration stage and a development stage by setting a stage switching threshold: only local optimal information of a subgroup is used for position updating in the early stage, and only global optimal information is used for position updating in the late stage; finally, an angle parameter corresponding to a globally optimal position is output. The application can effectively estimate the angle of a coherent source signal under the conditions of impulse noise and small snapshots, has global convergence and real-time performance, and significantly improves the robustness and estimation accuracy in a complex electromagnetic environment.

The invention discloses a rolling bearing weak fault diagnosis method based on cyclic correlation entropy and a low-rank sparse model, and belongs to the field of bearing fault diagnosis, and the method comprises the following steps: collecting a vibration signal of a rolling bearing, and determining a Gaussian kernel length based on the vibration signal by adopting a Czofmann criterion; based on the Gaussian kernel length, calculating a time-varying correlation entropy of the vibration signal by using a Gaussian kernel function; fourier series transformation is carried out on the time-varying correlation entropy to obtain a cyclic correlation entropy; fourier transform is carried out on the cyclic correlation entropy to obtain a cyclic correlation entropy spectrum; performing low-rank sparse decomposition on the cyclic correlation entropy spectrum by using a truncated kernel norm to obtain a sparse matrix; and solving an enhanced envelope spectrum for the sparse matrix, extracting a fault characteristic frequency based on the enhanced envelope spectrum, and judging a fault type based on the fault characteristic frequency.

The invention discloses an ATR engine control system state estimation method based on adaptive robust UKF. The method comprises the steps that a nonlinear discrete model of an ATR engine control system is established; the time updating process comprises the steps of initializing an engine state quantity and a state error covariance matrix, selecting a sampling point, constructing a state quantity of the sampling point, and predicting an estimated value of the engine state quantity and the error covariance matrix; the measurement updating process comprises the steps of updating the state quantity of the sampling point, converting the updated state quantity of the sampling point, calculating an engine output quantity estimated value and a covariance matrix through unscented transformation, and calculating a state-measurement cross covariance matrix; calculating a Kalman gain matrix process through a maximum correlation entropy criterion; and updating a state estimation and covariance matrix process. The method can solve the problems that an existing nonlinear system state estimation method is insufficient in robustness in a complex noise environment, sensitive to abnormal measurement values, difficult to cope with the influence of non-Gaussian noise and the like.

This invention discloses a target tracking state smoothing estimation method and apparatus based on maximizing correlation entropy. The method involves acquiring a first target state vector before the current time and a second target state vector after the current time. Forward recursive filtering is performed based on the first target state vector, and backward recursive filtering is performed based on the second target state vector to obtain the estimated second target state vector at the current time. The first and second target state estimation vectors are then fused to obtain the fused target state estimation vector at the current time. This invention incorporates the maximization of correlation entropy method into the generation process of the fused target state estimation vector, considering all even-numbered terms of the estimation error statistical moments. This makes it highly robust to suppressing outliers with heavy tailing distribution characteristics and achieves satisfactory estimation accuracy in non-Gaussian noise environments.

This invention relates to the field of underwater positioning technology, specifically to an AUV cooperative positioning method based on generalized maximum correlation entropy and capacitive Kalman filtering (CKF) that can effectively improve positioning accuracy. This invention employs GMCC and introduces a generalized Gaussian density kernel function to measure the error of the nonlinear filtering algorithm using generalized correlation entropy. The generalized Gaussian kernel function has the advantages of having many parameters, flexible variation, and adaptability. It can achieve the maximum correlation entropy when the error is minimized. Because the generalized Gaussian kernel contains even-order higher moments of the errors of two variables, it can better handle heavy-tailed noise and large outliers. Experimental verification shows that the technical solution of this invention has better robustness and reliability, and can better handle problems such as outliers and heavy-tailed noise interference. Compared with the traditional CKF, this invention can improve positioning accuracy.

The application discloses an improved parallel factor target parameter joint estimation method based on a hybrid complex correlation entropy, and relates to the technical field of radars. Mainly comprising: performing PARAFAC decomposition on a transmission angle equivalent estimation matrix, a receiving angle equivalent estimation matrix and a Doppler frequency equivalent estimation matrix based on a dynamic weight adaptive mechanism, and constructing a cost function of the PARAFAC decomposition based on a hybrid complex correlation entropy; converting the constructed cost function into a weighted least square problem based on a semi-quadratic optimization theory, and solving a factor matrix closed-form analytical solution of a target parameter; and obtaining an estimated target parameter based on the factor matrix closed-form analytical solution of the target parameter. The application can be applied to joint estimation of a Doppler frequency shift, a transmission angle and a receiving angle parameter of a low-altitude environment target, and can realize automatic pairing, thereby providing a more reliable and efficient technical solution for a low-altitude security field.

The invention provides a UGV anti-interference control method based on disturbance and state collaborative estimation. According to the method, on the basis of establishing a vehicle tracking model containing a lateral error and an orientation error, time-varying disturbance is estimated by using a generalized proportional-integral observer, and a state estimation result based on a maximum correlation entropy criterion is introduced into an updating link of the observer, so that the time-varying disturbance is estimated while the observer keeps the quick response capability to high-order time-varying disturbance. The amplification effect of the high-gain structure on the high-frequency noise of the sensor is effectively reduced; according to the method, disturbance estimation output by the generalized proportional-integral observer serves as prior correction information to be introduced into a state prediction process based on the maximum correlation entropy criterion, prediction error covariance is reduced, unification of disturbance compensation and noise suppression capacity is achieved, and a UGV anti-interference trajectory tracking control law is designed on the basis. A simulation result shows that compared with other methods, the control method has the advantage that the control performance of the lateral deviation and the course error is remarkably improved.

The present invention discloses a method for identifying a model system containing error variables, which mainly solves the problem that existing system identification methods have low identification accuracy or even cannot identify unknown model systems containing error variables when both input and output signals are contaminated by impulse noise. Its implementation scheme is: initializing an adaptive filter; constructing a cost function of the adaptive filter using the maximum overall fractional-order correlation entropy; iteratively updating the weight coefficients of the adaptive filter using the fractional-order gradient method of the maximum overall fractional-order correlation entropy, so that the error between the weight coefficients of the adaptive filter and the weight coefficients of the unknown system is continuously reduced, and finally the unknown system weight coefficients are obtained, completing the identification of the model system containing error variables. The present invention can achieve good identification of unknown model systems containing error variables in weak and strong impulse noise environments, and improves the identification accuracy. It can be used for digital beamforming, channel equalization, noise elimination, and electrocardiogram signal interference elimination.

This invention belongs to the field of rigid body pose estimation technology, and discloses a rigid body pose estimation method, system, and device based on generalized correlation entropy geometric filtering. The method includes: acquiring target motion information and establishing a discrete nonlinear kinematic model; initializing the discrete nonlinear kinematic model; generating sigma points for the posterior state covariance matrix and the process noise covariance matrix, respectively, and propagating and updating the sigma points through a manifold fast unscented transformation; obtaining the predicted state mean, the prior state covariance matrix, and the square root of the prior state covariance matrix; updating the center parameters based on the information from historical moments; calculating the pseudo-measurement matrix and updating the gain; correcting the state estimate using the gain; determining whether convergence has occurred, and if not, continuing the iteration; otherwise, outputting the posterior state estimate as the final rigid body pose estimation result for that moment. This invention effectively solves the problems of robustness and accuracy degradation in rigid body pose estimation under non-zero mean and non-Gaussian noise environments.

The invention discloses a power grid state estimation method based on Hammerstein nonlinear filtering of maximum correlation entropy. The method comprises the following steps: acquiring a measurement signal of each monitoring point of a power grid according to a preset sampling period; inputting the measurement signal into a pre-constructed Hammerstein type nonlinear filtering structure to obtain a state space model corresponding to the measurement signal; optimizing parameters of the state space model according to a maximum correlation entropy strategy to obtain an optimized state space model; and performing synchronization processing on the measurement signal, and inputting the synchronized measurement signal into the optimized state space model to obtain a power grid state estimation result at the current moment. According to the method, a Hammerstein nonlinear filtering structure and a maximum correlation entropy optimization strategy are fused, the technical problem that a traditional filtering algorithm is difficult to consider nonlinear modeling and non-Gaussian noise suppression at the same time is solved, and an efficient and robust solution is provided for precise state estimation of a modern complex power grid.