7 results about "Cross-covariance" patented technology

The invention discloses a power distribution network state estimation method and device based on high-order volume Kalman filtering, and the method comprises the steps: firstly calculating a volume point of a state variable of a power distribution network at a current moment, calculating a propagation volume point according to a state equation of the power distribution network, and calculating the state variable of the power distribution network according to the propagation volume point; and calculating a state predicted value at the next moment and a predicted value of the state error covariance matrix, and performing volume transformation on the propagation volume point to obtain a volume point at the next moment. Then, propagation is carried out on a volume point at the next moment through a measurement function, and a measurement predicted value at the next moment, a measurement error covariance matrix and a cross covariance matrix are calculated; and finally, calculating a Kalman filtering gain according to the measurement error covariance matrix and the cross covariance matrix, correcting a state prediction value by adopting the calculated Kalman filtering gain, and updating the state error covariance matrix, so that the state estimation precision is improved, and the estimation accuracy is improved. And the robustness and the precision of the system facing bad data are effectively enhanced.

This invention provides a radar target detection method and apparatus based on dual-polarization maximum eigenvalues. The method includes: receiving first polarization echo data and second polarization echo data, and determining a detection unit and at least two reference units based on the first polarization echo data and the second polarization echo data; for any unit, determining a cross-covariance matrix based on the first polarization echo data and the second polarization echo data, and determining the maximum eigenvalue corresponding to the unit based on the cross-covariance matrix; determining an average maximum eigenvalue based on the maximum eigenvalues ​​corresponding to each reference unit; and determining a target detection result based on the maximum eigenvalue corresponding to the detection unit, the average maximum eigenvalue, and a preset threshold factor, wherein the target detection result includes whether the target exists or does not exist. This reduces the computational complexity of radar target detection, fully leverages the distinguishability of target echo signals and sea clutter, and improves the detection accuracy of radar target detection.

The invention provides a zero-trust intelligent network connection vehicle group trusted cooperative positioning method in a composite network attack scene, and the method specifically comprises the steps: firstly constructing a motion state model and a relative measurement model of a multi-vehicle system, and obtaining the vehicle state and measurement information through combining an IMU and a UWB sensor; distributed local prediction is realized through left and right decomposition of cross covariance between vehicles; a random attack model comprehensively considering DoS, FDI and hybrid attacks is designed, attack probability modeling is introduced, and a state error covariance upper bound is derived to quantify cooperative positioning credibility and ensure error convergence; in combination with a heterogeneous cooperation mechanism of distributed prediction and centralized updating, only a temporary master control agent broadcasts necessary updating parameters, so that the communication load is reduced, and the capability of adapting to dynamic topology is improved. Finally, according to the method provided by the invention, the accurate positioning and the system stability of the vehicle group can still be kept even under the condition of failure or hostile attack of a part of links.

This invention relates to energy storage technology and discloses a collaborative control method and system for an energy storage converter. The collaborative control method for the energy storage converter includes the following steps: acquiring multi-time-period electrical parameter measurements of the target energy storage converter's grid connection point through a data acquisition interface; dividing the electrical parameter measurements into a training dataset and a target dataset according to a program-defined partitioning rule; calling a covariance calculation module to calculate an autocovariance matrix based on the training dataset and a cross-covariance vector based on the training dataset and the target dataset; generating predicted values ​​of the target electrical parameters based on the autocovariance matrix and the cross-covariance vector using a prediction algorithm module; subtracting the predicted values ​​from the actual measured values ​​of the target dataset using a compensation processing module to obtain compensated electrical parameter commands; and controlling the energy storage converter to switch operating modes according to the compensated electrical parameter commands.

The application discloses a SOC estimation method based on a deep fusion neural network and a UKF (Unscented Kalman Filter), relates to the technical field of all-vanadium redox flow batteries, and aims to realize high-precision estimation of the state of charge of a battery. The method comprises the following steps: a second-order equivalent circuit model of the battery is established, and parameter identification is performed on the second-order equivalent circuit model; a group of sigma points are obtained through unscented transformation, the propagation state estimation and the propagation state prediction of each sigma point are calculated, the first-order statistical moments of the prior state estimation and the prior state prediction are obtained through unscented transformation, the cross covariance is obtained, and the observation difference and the state update difference are obtained; the observation difference and the state update difference are input into a neural network system to obtain Kalman gain, the prior state estimation is updated through the Kalman gain, and the state of charge of the battery is predicted.

The application discloses a diversity gain and decoding mode switching method of a low-frequency wireless communication system, which comprises the following steps: collecting two pieces of data in a time period T1 before code sending and a time period T2 during code sending and carrying out band-pass filtering to obtain N3, N4, X3 and X4; calculating a noise covariance matrix C1 of N3 and N4, a sample covariance matrix C2 of a known sequence section in X3 and X4 and a cross covariance matrix C3; expanding C1 to the dimension of C2 to obtain C4; multiplying the sum of C4, C2 and a regularization coefficient and a unit matrix to obtain two filters after inverting, and then multiplying the two filters with C3 to obtain X7 after filtering X3 and X4; accumulating the total energy of the carriers of each frequency of the known sequence section of X7 to obtain a detection coefficient; comparing the relative signal-to-noise ratio of each detection coefficient with the size of a deep fading threshold to determine the applicable decoding mode of X7. The application can improve the signal-to-noise ratio and adaptively switch the decoding mode based on the carrier detection state.

The invention relates to the technical field of data processing, in particular to an abnormal behavior accurate identification method based on multistage safety management. According to the method, time delay is obtained according to a mapping relation between a target speed instruction sequence and an observation speed feedback sequence. The method comprises the following steps: acquiring an instruction track point set representing expected motion and a feedback track point set representing actual motion based on speed information, acquiring feedback track form entropy according to distribution chaos of singular values in a cross covariance matrix of the instruction track point set and the feedback track point set, calculating entropy gain, a non-rigid deformation residual error and an entropy-time delay coupling coefficient as multi-stage working condition feature vectors, and determining whether an abnormal working condition behavior is generated or not through comparison with a standard multi-stage working condition feature vector. According to the invention, before the motion deviation of AMR reaches a danger threshold value, early warning signals can be resisted in advance and abnormal behaviors can be diagnosed based on the meaning of each characteristic representation, so that a multi-stage safety management system can be converted from passive event response to active risk prevention.