54 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 provides a rolling bearing fault diagnosis method, a medium and equipment, and relates to the technical field of large-scale unit bearing fault diagnos.The method comprises the steps that collected original life cycle vibration data of a rolling bearing are obtained; performing early warning processing on the vibration data by using a Beta early warning algorithm, and determining and obtaining a required diagnosis signal according to an early warning interval; determining an input parameter of variational mode decomposition by using the diagnosis signal, performing optimal parameter selection on the variational mode decomposition by combining an improved pelican optimization algorithm, and decomposing the diagnosis signal by combining the optimal parameter with the variational mode decomposition to obtain a plurality of intrinsic mode functions; and evaluating the plurality of intrinsic mode functions by using the correlation entropy in combination with the kurtosis so as to select a proper intrinsic mode function for reconstruction, and obtaining a final diagnosis result according to envelope spectrum analysis of a reconstructed signal. According to the method, the rolling bearing fault can be early warned and accurately diagnosed, and the accuracy, the real-time performance and the intelligent level of fault diagnosis are improved.

The invention relates to an adaptive robust robust information fusion method for GNSS / INS tightly integrated navigation in combination with a neural network, belongs to the field of integrated navigation, and solves the problem of robust information fusion. The method comprises the following steps: establishing and training a neural network model used for distribution fitting of kernel parameters and errors of each channel of the multi-channel heteronuclear generalized correlation entropy Kalman filter; in the GNSS / INS tight integration navigation system INS recursion error estimation process, when the running time reaches the length of a close time observation window, predicting each channel kernel function parameter corresponding to each satellite in the filter; in the generalized correlation entropy Kalman filtering, performing INS error state estimation and recursion error correction by adopting the predicted kernel function parameters of each channel; and when the running time reaches the length of the next close time observation window, predicting kernel function parameters of each channel again. The method can deal with general noise of which the overall distribution is in a non-Gaussian form, and has robust capability.

The invention discloses a multi-agent wave band selection method based on clustering and various experience pools, and belongs to the field of remote sensing image processing. Hyper-spectral images are collected to generate a sample set of the hyper-spectral images, and the sample set is divided into a training sample set and a test sample set; grouping the training sample set through a wave band clustering method based on correlation entropy and a greedy algorithm, and constructing a multi-agent wave band selection model; performing model training on the multi-agent waveband selection model, and optimizing an experience pool of model training by using a reinforcement learning updating method based on a diversified experience pool; using the trained multi-agent waveband selection model to perform waveband selection on the test sample set to obtain a new data set, and dividing the new data set into a new training sample set and a new test sample set; constructing a classification network, and sending the new training sample set into the classification network for network training; and inputting the new test sample set into the trained classification network for hyperspectral image classification to obtain a classification result and a classification graph.

According to the power distribution network state estimation fusion strategy based on the maximum correlation entropy criterion, compared with a basic weighted least square estimation method, the maximum correlation entropy criterion is adopted, and an objective function is established; applying a state estimator based on the maximum correlation entropy criterion to the state estimation of the power distribution network according to the maximum correlation entropy criterion in combination with an adjustable Parzen window; on the basis of a traditional measurement system, a phasor measurement system is fully utilized, a hybrid estimation structure is adopted, results obtained through estimation of two kinds of measurement data are subjected to fusion processing, and therefore an optimal estimation value is obtained; an algorithm based on a maximum correlation entropy criterion and a fusion structure are combined to be used for state estimation of the power distribution network, and simulation analysis is carried out on an improved IEEE33 node power distribution network system. According to the power distribution network state estimation method based on the maximum correlation entropy criterion, a mixed two-stage evaluation system structure is utilized at the same time, and high-quality phasor measurement is fully utilized on the premise that state estimation measurement data which is widely used is guaranteed.

The invention relates to the technical field of community detection, and discloses a robust semi-supervised community detection method and system based on correlation entropy symmetric non-negative matrix factorization, and the method comprises the following specific steps: collecting community nodes, and constructing a community detection network model; constructing an initial adjacency matrix based on a community detection network model; on the basis of a pairwise constraint propagation algorithm, supervised information learning is carried out on the adjacency matrix to obtain a new adjacency matrix; a Gaussian kernel is adopted as a kernel function, and correlation entropy is constructed; constructing an optimization problem of non-negative matrix factorization of a new adjacent matrix based on correlation entropy and graph regularization; and solving an optimization problem by adopting a semi-quadratic optimization method to obtain an optimization matrix, and obtaining a final community detection result. The method solves the problem that a constraint propagation mechanism from constraint nodes to unconstraint nodes is lacked in the prior art, and has the characteristic of high anti-noise performance.

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 invention relates to a distributed power distribution network fusion estimation method. The method comprises the following steps: S1, establishing a power distribution network system model; s2, obtaining predicted values and a predicted information matrix of the node i and a neighbor node j thereof; s3, setting an iteration initial step and a filtering estimation value when t times of fixed point iteration is carried out on the ith node; s4, Cholesky decomposition is carried out; s5, calculating a consensus weighted item of the node i, and obtaining an optimal estimation value of a current state by using a fixed point iteration method of a maximum correlation entropy extension information filter; and S6, comparing the current state estimation value with the estimation value of the previous iteration step, if the current state estimation value is smaller than a threshold value, taking the current state estimation value as the state estimation value of the next iteration, and calculating the state estimation error covariance, otherwise, updating the number of iterations, and returning to S5. Compared with the prior art, the method has the advantages of improving the precision of the filter adopted by the power distribution network state estimation method and the like.

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 provides a two-dimensional direction of arrival estimation method under impulse noise, and the method comprises the steps: obtaining a first output matrix outputted by an augmented co-prime array and a second output matrix outputted by an augmented expanded co-prime array through a preset receiving model; the preset receiving model comprises a parallel line structure composed of an augmented co-prime array and an augmented expanded co-prime array, conjugate operation is carried out on ECA, the non-circular characteristic of signals is fully utilized, meanwhile, in order to suppress impulse noise, a correlation entropy method based on a generalized dustpan function (GVF) is utilized, and the non-circular characteristic of the signals is fully utilized. Compared with a traditional method, the method has better robustness.

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.

The present invention provides a data registration method and system based on a multi-beam echo sounder, the method comprising the following steps: obtaining observation point cloud data and transforming it to a mobile coordinate system; coarsely registering data of two adjacent frames based on an ICP algorithm; based on the waveform characteristics of the observation point cloud data, screening a feature point set whose curvature meets a threshold value, and constructing a feature matching point pair; establishing an objective function about a transformation matrix based on the maximum value of a correlation entropy model as a target, and completing iterative optimization of precise registration of data. By sequentially transforming the detection data to a reference coordinate system and a mobile coordinate system to eliminate the influence of installation errors and posture errors, the measurement accuracy of the data is improved, and by screening the data to obtain a feature point set whose curvature meets a threshold value, the amount of data processing and data redundancy can be reduced, and then by introducing correlation entropy as an error metric and combining iterative optimization methods, high-precision registration of data can be achieved.

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 present invention discloses a generator state estimation method applicable to non-Gaussian noise based on an adaptive Cauchy kernel correlation entropy loss criterion, comprising the following steps: (1) initializing each parameter for generator state estimation; (2) constructing a DOS network attack model to determine whether a DOS network attack occurs; (3) calculating the state prediction value and the error covariance matrix; (4) calculating the measurement prediction value and the cross-covariance matrix; (5) calculating the state gain, the state estimation value, and the state estimation error covariance; (6) performing iterative calculations for the next moment according to steps (3)-(5) until the loop ends, and outputting the state estimation result. This method combines the Cauchy kernel correlation entropy loss criterion (CMC) with the cubature Kalman filter (CKF) framework to make up for the defects of traditional state estimation methods in non-Gaussian noise environments and DOS network attacks, such as low estimation accuracy and weak robustness.

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.