22 results about "Linearization error" patented technology

The invention discloses an extended Kalman filtering method based on dynamic self-adaption residual error enhancement, and relates to the field of optimization algorithm error accumulation methods, and the extended Kalman filtering method based on dynamic self-adaption residual error enhancement significantly improves the sensing precision and reliability of a system through cooperative processing of multi-source heterogeneous sensing information, and improves the accuracy and reliability of the system. A sliding window covariance estimator is introduced, time-varying statistical characteristics of IMU angular velocity noise and GPR echo noise are identified in real time, a dual-threshold residual monitoring mechanism is designed, Jacobian matrix update frequency is dynamically adjusted to balance linearization errors and calculation loads, a robust correction term based on a Mahalanobis distance is established, and the robustness of the algorithm is improved. And abnormal observation interference caused by intermittent failure of the sensor is effectively inhibited.

The invention relates to the technical field of digital filters, and discloses a quantization sensing wide linear minimum error mode adaptive filter, which performs non-uniform low-bit quantization on an input signal and an expected signal by using a quantization parameter to obtain a corresponding quantization value; constructing a linear factor based on the quantization parameter; based on the non-roundness of the input signal, constructing a normalized correlation matrix of the input signal and the linearized error signal, and constructing a compensation matrix in combination with a linear factor; obtaining input signal quantized values at a current moment and a historical moment, constructing an augmented input signal vector, calculating a product of the augmented input signal vector and the compensation matrix, and constructing a corrected input vector; performing wide linear filtering on the corrected input vector to obtain an output signal, and calculating an error signal in combination with an expected signal quantized value; and updating the weight vector at the next moment by using the corrected input vector and the error signal. According to the invention, by introducing a non-uniform low-bit quantization and deviation compensation mechanism, the cost and energy consumption of the analog-to-digital converter are remarkably reduced, and a relatively low steady-state error is kept.

The invention discloses a system inertia and primary frequency modulation coefficient estimation method and device, equipment and a medium. A Sigma point set is generated according to posterior state estimation including inertia, primary frequency modulation coefficient and frequency deviation at the last moment and covariance of the posterior state estimation; a prediction state and statistics thereof are obtained through nonlinear state transfer function transformation; obtaining a predicted measurement value and a statistic thereof through measurement function transformation, and calculating a cross covariance of a state and measurement; the prediction state is updated by using the actual frequency deviation and the Kalman gain calculated based on the measurement covariance and the cross covariance, and the current optimal state estimation is obtained; and finally, inertia and a primary frequency modulation coefficient are extracted from state estimation. According to the method, real-time, joint and online accurate identification of the inertia and the primary frequency modulation coefficient is realized, the problems of linearization error and parameter coupling neglect of a traditional method are effectively solved, the robustness and accuracy of estimation are remarkably improved, and reliable state sensing support is provided for stable analysis of the frequency of a power system.

The invention discloses a shape error correction method for free-form surface machining, which comprises the following steps of: analyzing the path characteristics of an original cutter through a linearization error theory, establishing a slow cutter servo turning cutter path simulation model, and describing the cutting path of the cutter by adopting an Archimedes spiral. And on the basis, cutter radius compensation is carried out, cutter contact data after compensation are obtained, and the accuracy of a cutting track is ensured. Then, obtaining point clouds of the free-form surface to be processed, and obtaining source point clouds and target point clouds respectively; in order to realize reliable matching between the source point cloud and the target point cloud, point cloud coarse registration is used for coarse registration, and then point cloud fine registration is used for fine registration. And finally, calculating a shape error through a least square method, and applying the shape error to correction so as to obtain a processed free-form surface type.

The application relates to the field of signal processing and discloses a multi-baseline phase interferometer virtual phase line boundary folding processing method and system. The method comprises the following steps: performing sub-interferometer division, and generating an initial candidate modulus pair set based on system physical constraints; performing boundary folding detection on a measured phase pair, and judging whether the measured phase pair falls into a boundary dangerous area; if folding risk is detected, corresponding virtual modulus pairs are generated, the initial candidate modulus pair set is expanded by using the virtual modulus pairs, and an expanded candidate set is formed; based on a minimum linearization error criterion, an optimal modulus pair most matched with the measured phase pair is selected from the expanded candidate set; a global ambiguity vector is solved step by step, and finally, a signal angle of arrival is estimated. Through active identification and compensation of boundary folding effects, the robustness and accuracy of angle measurement under low signal-to-noise ratio conditions are significantly improved.

This invention discloses a multi-sensor fusion positioning method for complex port environments, comprising: S1, establishing a multi-sensor coordinate system; S2, extracting constraints in environments with GPS signals and rich structure; S3, extracting constraints in environments without GPS signals but with rich structure; S4, extracting constraints in environments without GPS signals and with degraded structure; and S5, establishing a nonlinear fusion positioning framework for different sensors, fusing all nonlinear constraints obtained from different sensors in different environments. This method achieves seamless vehicle positioning under changing scene features and environments, effectively reduces linearization errors, and solves the positioning problem in degraded structure environments.

This invention discloses a collaborative localization and obstacle avoidance control system and method for unmanned aerial vehicle (UAV) swarms. This method uses a first comprehensive cost function to issue swarm change commands only when the accuracy improvement from configuration changes significantly outweighs the cost of flight maneuvers, effectively filtering out the ping-pong effect caused by frequent swarm changes. Through coarse-to-fine hierarchical localization processing, the method utilizes FM coarse localization solutions to resolve the TDOA observation equations in the far-field high-altitude region. GDOP The technical challenge of regional divergence is addressed by using a "hot start" mechanism to map the initial value of GN to EKF in real time, thus avoiding the accuracy collapse caused by the accumulation of linearization errors in the cold start phase of traditional filtering algorithms. This invention overcomes the limitation of decoupling the logic of "obstacle avoidance" and "positioning accuracy maintenance." Within the rolling optimization framework of model predictive control, it explicitly embeds rigid geometric constraints based on the Laplace matrix, enabling the cluster to replace disordered drift with elastic compression when performing obstacle avoidance actions.

The invention provides a method and system for calculating a Cole-Cole model by using a rational fraction approximation method, a medium and a product, and relates to the field of electric digital data processing. An error expression between a Cole-Cole target model and a rational fraction approximation function before solving is multiplied by a denominator polynomial of the approximation function, and a denominator coefficient to be solved is moved to a linear expression from a denominator position, so that a linearized error function is obtained. Then, on the basis of the linearization error, discrete sampling points are selected in the full frequency band, and a target function with the purpose of minimizing the sum of errors is established. A nonlinear problem which needs to be solved through complex iteration originally is converted into an optimization problem which can be solved in a one-time and deterministic mode through a standard linear solving algorithm. The high efficiency of the solving process and the stability of the result are ensured, the effect and the accuracy of the induced polarization effect are improved, and meanwhile the cost is reduced.

The application provides a multi-agent reinforcement learning active power distribution network collaborative optimization method and system, belongs to the field of active power distribution network operation optimization and intelligent control, and constructs a global linearized voltage sensitivity model; each distributed resource is regarded as an agent, a local observation vector of each agent is input into a policy network to obtain an original action, and a real node voltage is calculated; whether the real node voltage satisfies a voltage safety constraint is checked, if yes, the original action is taken as an execution instruction, and if not, step 4 is entered; in step 4, online correction is performed, active injection adjustment, reactive injection adjustment and node voltage relaxation variable are solved jointly, and a candidate safety action is obtained; in step 5, power flow checking is performed, and a linearization error compensation term is updated; if the real node voltage does not satisfy the voltage safety constraint, the updated linearization error compensation term is substituted into step 4, an action correction amount is solved, and steps 4 and 5 are iteratively executed; and the safety projection layer prediction accuracy and the action correction reliability are improved.

The invention belongs to the technical field of wind power prediction, and particularly relates to a method for predicting ultra-short-term wind power based on an improved UKF algorithm. According to the method, a square root UKF, normalized innovation square gating and self-adaption, multi-information joint updating and multi-model interaction technologies are fused on the basis of the UKF. The method comprises the following steps: establishing a nonlinear state space model, generating a propagation sigma point by SR-UKF to complete time updating, acquiring, measuring and executing NIS gating, fusing and innovating a multi-information strategy and self-adapting to a noise covariance, constraining a projection to ensure that a physical boundary and the covariance are positive definite, fusing multiple working condition probabilities by IMM, and outputting a prediction result and uncertainty information. According to the method, linearization errors are avoided, and prediction robustness is improved; the problems of phase lag, unstable numerical value and the like are solved through multiple technologies respectively, second-to-minute level accurate prediction is achieved, and meanwhile deliverable capacity information capable of being directly used for control and performance is output.

A method for estimating a constrained zonotope enclosing a motion state of at least one mobile target, the method comprising: a) linearizing a nonlinear transition model at an element of a first zonotope (Zx(tk-1)), b) computing transition linearization errors at different elements of the first zonotope (Zx(tk-1)), c) computing a second zonotope that contains all linearization errors, d) propagating (102) the first zonotope so as to obtain an a priori zonotope ({circumflex over (Z)}x(tk)) enclosing the motion state at a second moment (tk) after the first moment (tk-1), wherein the a priori zonotope ({circumflex over (Z)}x(tk)) is a linear projection of a hypercube, the hypercube being subject to at least one linear constraint, e) updating (104) the a priori zonotope ({circumflex over (Z)}x(tk)) so as to obtain an a posteriori constrained zonotope (Z′x(tk)) enclosing the motion state at the second moment (tk).

The application relates to an optical satellite remote sensing image autonomous accurate positioning method and device, wherein the method comprises the following steps: acquiring two image data of a target area and imaging data corresponding to the two image data by a target satellite target ground load at different observation angles, and constructing an imaging equation corresponding to each image data according to the imaging data; constructing an error equation based on the imaging equation, the installation angle of the target ground load and the longitude and latitude information of homonymic points between the two image data; linearizing the error equation to generate a linearized error equation, and solving the linearized error equation to obtain the longitude and latitude of target homonymic points corresponding to the target satellite and the installation angle of the target load, and performing autonomous positioning of the target satellite according to the longitude and latitude of the target homonymic points and the installation angle of the target load. Therefore, the problem that the traditional calibration technology based on ground reference data highly depends on the accuracy and timeliness of external reference images and greatly influences the accuracy of geometric calibration is solved.

The application discloses a kind of flexible connection constraint under multi-node distributed filtering estimation method, belong to deep space exploration technical field.The implementation method of the application is as follows: by selecting the node with maximum observability in adjacent node, the state estimation thereof is utilized to establish flexible connection constraint, and the constraint least square problem of distributed filtering estimation is formed;The condition that inequality constraint is converted into equality constraint needs to be satisfied is analyzed using KKT condition, the flexible connection constraint is converted from inequality constraint to equality constraint, to avoid optimization process under the influence of inequality constraint to fall into local optimum, by constraint local linearization, the analytical expression of node state estimation mean value and estimation error covariance is obtained, the efficient estimation of flexible lander node state is realized, and the state estimation result conforming to the physical characteristics of flexible connection is generated;In addition, the nonlinear constraint linearization error is reduced by iterative estimation, and the node state estimation accuracy of flexible lander is further improved.

The application provides a distributed off-road vehicle state observation method based on visual fusion and nonlinear MHE. First, the front road surface texture features are extracted through the vehicle-mounted visual system and the adhesion coefficient distribution is predicted. Then, the MHE framework is used to explicitly process the physical constraints. The problems that the results of traditional filtering algorithms such as EKF are prone to divergence under strong nonlinear working conditions due to linearization errors, and the state values that violate the physical laws are calculated under extreme working conditions due to the lack of explicit physical constraints are solved. The optimal state is solved under the premise of meeting the physical constraints, and the physical consistency of state estimation is ensured, and the robustness under off-road working conditions is improved.

The application discloses an inertial-based integrated navigation system error state model correction and optimization method and system, and belongs to the field of integrated navigation control. In a local integrated navigation coordinate system, the influence of misalignment angles and position error angles on the mismatch problem of a calculated navigation coordinate system and a real navigation coordinate system is comprehensively considered, a nonlinear velocity error state corrected by misalignment angles and position error angles is redefined, and a new inertial-based integrated navigation system error state model is derived and established. Compared with the ST-EKF, the application not only considers the influence of linearization errors caused by misalignment angles, but also adds a correction term of the position error angles, further perfects the inertial-based integrated navigation system error state model optimization theory, effectively improves the performance and environmental applicability of state estimation of the inertial-based integrated navigation system, and the superiority is more significant especially in the case that the initial state error is large.

This invention discloses a vehicle dynamics modeling method and system that integrates eigenvalue stability constraints, belonging to the field of vehicle dynamics modeling and machine learning technology. By acquiring vehicle operating state and control input data, standardized data is obtained through normalization. A deep neural network is used to map the vehicle state to a high-dimensional feature space. Controlled linear evolution prediction is performed using the system matrix and input matrix to obtain predicted feature data. A composite loss function is constructed, including reconstruction error, linearization error, evolution error, physical prior error, and eigenvalue stability error, where the eigenvalue stability error constrains the eigenvalue magnitude of the system matrix to not exceed a preset threshold. The network and matrix parameters are iteratively optimized with the composite loss function as the objective. After training, stability verification is performed to obtain state convergence evaluation results. This invention achieves stable modeling and accurate prediction of vehicle dynamics.

The application discloses a PPP / UWB tight combination positioning method and system and a storage medium, wherein a third-order spherical radial volume criterion is used to realize nonlinear propagation of a measurement equation, and the influence of linearization errors of a traditional method is effectively weakened; by introducing variance component estimation, a GNSS and UWB measurement variance and covariance matrix is adaptively adjusted according to residual statistical characteristics, and adaptive adjustment of the measurement variance and covariance matrix is realized; meanwhile, in combination with a robust weight function, observation data containing gross errors and outliers are weighted and suppressed, and the robustness and anti-interference ability of a filtering system are improved. Compared with a traditional PPP / UWB combination method, the application can adaptively adjust the variance and covariance matrix according to environmental changes, and the influence of inaccurate empirical values and observation abnormalities on positioning results is significantly reduced; through volume Kalman filtering, linearization errors caused by the removal of high-order terms in Taylor first-order expansion are avoided.

This invention relates to the field of digital filter technology and discloses a quantization-aware wide-linearity minimum error modulus adaptive filter. It utilizes quantization parameters to perform non-uniform low-bit quantization on the input signal and the desired signal to obtain the corresponding quantized values; a linearity factor is constructed based on the quantization parameters; a normalized correlation matrix between the input signal and the linearized error signal is constructed based on the non-circularity of the input signal, and a compensation matrix is ​​constructed in conjunction with the linearity factor; the quantized values ​​of the input signal at the current and historical times are obtained to construct an augmented input signal vector, and its product with the compensation matrix is ​​calculated to construct a corrected input vector; wide-linearity filtering is performed on the corrected input vector to obtain the output signal, and the error signal is calculated in conjunction with the quantized value of the desired signal; the weight vector at the next time step is updated using the corrected input vector and the error signal. This invention significantly reduces the cost and energy consumption of the analog-to-digital converter by introducing non-uniform low-bit quantization and a bias compensation mechanism, while maintaining a low steady-state error.

The invention provides an energy storage battery state-of-charge estimation and equalization control method, which relates to the technical field of battery management and is characterized by comprising the following steps of: acquiring voltage and current data of a battery end, establishing a battery equivalent circuit model and identifying model parameters; carrying out charge state estimation based on an unscented Kalman filtering algorithm, calculating a state and observing covariance by propagating weighted sampling points, and avoiding a linearization error; on the basis of estimation, a double-layer balance control strategy is executed, and SOC or voltage is selected as a balance variable for dynamic adjustment according to the SOC difference in the battery pack and between the battery packs; and furthermore, a three-section type charging and voltage safety interval judgment discharging control strategy is combined, so that intelligent charging and discharging management of the energy storage battery is realized. The method can improve the precision and stability of state-of-charge estimation, effectively improves the consistency of the battery pack, supports bidirectional optimization control of energy flow, and is suitable for energy storage systems such as all-vanadium redox flow batteries and the like.

The invention belongs to the field of hypersonic flight vehicle trajectory optimization and guidance control, and relates to a trajectory optimization and guidance method in a mode conversion process of a combined power flight vehicle. An aircraft particle dynamics model is established, trajectory optimization is performed on a climbing section and a modal conversion section in combination with an adaptive Gaussian pseudo-spectrum method, and a flight strategy of converting potential energy into kinetic energy is designed, so that the aircraft crosses a thrust trap through diving acceleration when thrust is insufficient. Meanwhile, flight-thrust integrated control is achieved based on a trajectory linearization control method, an error equation is linearized along the optimized trajectory, a time-varying feedback control law is designed, and high-precision stable tracking of the optimized trajectory is achieved by cooperatively adjusting an attack angle and an accelerator. According to the online application-oriented intelligent tracking method, a deep neural network is trained to learn a mapping rule of a flight state, an error and an optimal parameter, an optimal control parameter is screened in combination with a time-frequency domain performance index, and the tracking robustness of a complex working condition is enhanced.

The present application belongs to the field of hypersonic vehicle trajectory optimization and guidance control, and relates to a trajectory optimization and guidance method in the mode conversion process of a combined power vehicle. By establishing a vehicle particle dynamics model, trajectory optimization is performed on the climb segment and mode conversion segment in combination with an adaptive Gauss pseudospectral method, a flight strategy is designed to exchange potential energy for kinetic energy, and the vehicle is accelerated to cross the thrust trap through diving when the thrust is insufficient. At the same time, integrated flight and thrust control is realized based on a trajectory linearization control method, the linearized error equation along the optimized trajectory is designed, and a time-varying feedback control law is designed to realize high-precision stable tracking of the optimized trajectory through coordinated adjustment of the angle of attack and the throttle. An intelligent tracking method for online application trains a deep neural network to learn the mapping rule of the flight state, the error and the optimal parameters, and combines time and frequency domain performance indicators to select the optimal control parameters, thereby enhancing the tracking robustness in complex working conditions.

The invention discloses a PPP / UWB tight combination positioning method and system, and a storage medium, and the method achieves the non-linear propagation of a measurement equation through a third-order ball radial volume criterion, and effectively weakens the influence of the linearization error of a conventional method. Through introduction of variance component estimation, GNSS and UWB measurement variance covariance matrixes are adaptively adjusted according to residual statistical characteristics, and adaptive adjustment of the measurement variance covariance matrixes is realized. And meanwhile, in combination with a robust weight function, weighted suppression is performed on observation data containing gross errors and abnormal values, so that the robustness and the anti-interference capability of the filtering system are improved. Compared with a traditional PPP / UWB combination method, the method has the advantages that the variance and covariance matrix can be adaptively adjusted according to environmental changes, and the influence of inaccurate empirical values and observation abnormity on a positioning result is remarkably reduced; through volume Kalman filtering, linearization errors caused by rejection of high-order terms in Taylor first-order expansion are avoided.