15results about How to "Achieving High-Precision Estimation" patented technology

The invention relates to the technical field of lithium battery charging and discharging management, and discloses a lithium battery charging and discharging detection and management method, device and equipment and a storage medium, and the method comprises the steps: carrying out the data collection of the charging and discharging process of a lithium battery pack, applying consistent Gaussian noise, and obtaining an electrode-level expansion data set and a battery-level expansion data set; performing feature mapping and feature mixing by minimizing the maximum average difference loss function to obtain a mapping feature vector; estimating the state of charge and the state of health of the battery through a fusion model of a recurrent neural network and a physical information hybrid neural network to obtain a state of charge estimated value and a state of health estimated value; and constructing a battery pack electric heating model of the lithium battery pack, and generating a corresponding charging and discharging control instruction sequence. Self-adaptive adjustment can be carried out according to the real-time state of the battery, potential safety hazards such as over-charging and over-discharging and out-of-control temperature are effectively avoided, and the use safety of the battery pack is remarkably improved.

The invention discloses an ROV positioning navigation system based on vision inertial navigation fusion and pose correction, which comprises an inertial measurement value and image preprocessing module loaded on an ROV, the inertial measurement value and image preprocessing module is connected with a vision and inertial navigation data fusion module through a wire, and the vision and inertial navigation data fusion module is connected with a pose correction module. The invention also discloses a positioning navigation method of the ROV positioning navigation system based on visual inertial navigation fusion and pose correction. According to the method, the inertial data provided by the IMU and the environment characteristic information captured by the visual sensor are fused, high-precision estimation of the position and attitude of the ROV can be realized, the sensor data are optimally fused by using the MSCKF algorithm, the influence of the error of a single sensor on the positioning result is reduced, and the positioning precision and reliability of underwater operation are remarkably improved.

The invention belongs to the technical field of carrier frequency offset estimation, and particularly relates to a carrier frequency offset estimation method based on Newton orthogonal matching pursuit. The method comprises the following specific steps of: splitting CFO into two parts of frequencies, namely off-grid deviation and grid point frequency, and further modeling an observation signal as a parameter weighted compressed sensing problem; using a Newton orthogonal matching pursuit algorithm to iteratively optimize the off-grid deviation of the CFO and the estimation of the grid point frequency, and obtaining a CFO candidate frequency set; and obtaining a required CFO estimation result from the candidate set by using the lead code information of the target user. According to the method, the complexity requirement of an actual system is met, meanwhile, the precision of CFO estimation is remarkably improved, and good robustness is achieved.

The invention belongs to but is not limited to the technical field of radio detection direction finding, and discloses a double-antenna three-dimensional rotating baseline direction finding method, device and system. Inter-channel phase deviation table compensation is carried out, equal-frequency-interval full-working-frequency-band measurement is carried out on phase deviation between two measurement channels in advance, and a channel phase deviation compensation table AB is formed; the compensation module is used for compensating inherent phase deviation between the channel A and the channel B; after the double-antenna three-dimensional rotating baseline direction finding device starts to work, a baseline formed by the antennas AB rotates anticlockwise at an angular velocity omega, two receiving channels synchronously collect data, and phase difference is calculated for the data corresponding to the baselines AB; based on a two-dimensional joint search method, through combination of coarse search and fine search, an azimuth angle and a pitch angle are estimated. According to the technical scheme provided by the invention, the direction finding range and reliability of a traditional system are greatly expanded, meanwhile, the cost is basically not increased, and the large-scale application of the technical system can be promoted.

The application discloses a single-epoch multi-frequency GNSS ambiguity precision factor refinement estimation method. The method comprises the following steps: weighting single-epoch multi-frequency pseudorange and carrier phase observation of a reference station and a rover station respectively; calculating a harmonic mean of the same satellite pseudorange observation weight and a harmonic mean of the carrier phase observation weight; calculating a weighted geometric mean of the multi-frequency carrier wavelength and a weighted geometric mean of the multi-frequency non-difference carrier phase observation standard deviation; constructing a baseline vector non-difference weighted coefficient matrix of the carrier and the pseudorange observation; and finally calculating a single-epoch multi-frequency GNSS ambiguity precision factor value according to the above parameters. The application can realize high-precision estimation when the same or different weighting models are adopted for the pseudorange and the carrier phase, and the estimation precision can reach 10 ‑6 weeks, which is about 99.98% higher than that of a traditional model, and provides a reliable prior success rate estimation for single-epoch multi-frequency GNSS rapid precise positioning.

This invention discloses a visibility estimation method and apparatus based on binocular vision. The method includes: simultaneously acquiring left and right views using a calibrated binocular camera; inputting the image pairs into a stereo matching deep neural network to obtain a disparity map and converting it into a depth map; inputting the left view and depth map into a dual-branch deep convolutional neural network to extract multi-scale features; fusing RGB features and depth features through a cross-modal feature fusion module; and finally outputting a visibility estimate through a feature aggregation and regression module. The apparatus includes a binocular image acquisition unit, a data processing and visibility estimation unit, and a result output unit. This invention solves the depth ambiguity problem of monocular vision by fusing binocular depth information and image appearance information, achieving high-precision, non-contact visibility surface measurement. It has the advantages of low cost and flexible deployment, and is suitable for visibility monitoring in traffic scenarios such as highways.

The invention relates to a method and system for reconstructing a hyperspectral image based on an RGB image, and the method comprises the steps: S1, selecting an RGB image in a space; s2, processing the RGB image to obtain end member information of the RGB image; s3, generating end member spectral representation based on the end member information of the RGB image; s4, encoding, decoding and attention modeling are carried out on the RGB image, and abundance information of the RGB image is obtained; and S5, constructing a rough hyperspectral image based on the end member information of the RGB image and the abundance information of the RGB image, performing feature enhancement on the rough hyperspectral image and the RGB image, and performing fusion to obtain a final hyperspectral image. According to the invention, the hyperspectral image can be effectively constructed based on the RGB image.

This invention discloses a UAV state estimation method based on a two-stage search, relating to the field of low-altitude sensing and control technology. The method includes: constructing a three-dimensional reference coordinate system for a substation area, a set of no-entry flight zones, and a constraint grid table; generating an environmental constraint data packet; configuring and transmitting OTFS sensing frames based on the environmental constraint data packet, acquiring echo sequences, performing time synchronization, frequency offset correction, static clutter suppression, and frequency domain pre-whitening processing to generate a weighted observation sequence and a gridded environmental cost table; performing a coarse search using two-dimensional orthogonal matching pursuit under environmental constraints to obtain a unique candidate state group; performing a continuous domain fine search based on the unique candidate state group to complete position and velocity correction and generate current cycle state data; and generating initial state data for the next cycle based on the current cycle state data. This invention balances search efficiency and estimation accuracy through a two-stage search; and improves the accuracy of UAV state estimation through precise time delay and precise Doppler fitting.

The invention discloses an amplitude-phase error self-calibration direction of arrival greedy estimation method. The method comprises the following steps: S1, establishing an array receiving signal model containing an amplitude-phase error; s2, calculating an effective grid size and an iteration stop threshold value of the search grid; s3, performing coarse search on the search grid, and detecting an initial direction of arrival and an initial signal corresponding to the maximum projection energy; if the maximum projection energy is lower than an iteration stop threshold, skipping to S6; s4, performing Newton iteration refinement on the initial direction of arrival to obtain more accurate continuous angle estimation and signal estimation; s5, alternately updating the signal waveform of the signal source and the diagonal calibration matrix, updating the residual signal according to the signal waveform and the diagonal calibration matrix, rejecting the current signal source, and returning to S3; and S6, removing a weak source through pruning, performing optimization, and outputting a final direction of arrival and diagonal calibration matrix estimation. According to the method, error transmission is avoided, and the direction-of-arrival estimation precision under the condition of unknown array errors is remarkably improved.

The invention relates to the technical field of signal processing, in particular to a bee colony target super-resolution detection method and system based on spatial spectrum signal analysis, by adopting the method provided by the invention, a traditional detection algorithm and a super-resolution detection algorithm are creatively fused, and the advantages of the traditional detection algorithm and the super-resolution detection algorithm are organically combined. While the high-precision super-resolution function is realized, the calculation complexity of the system is reduced through algorithm optimization and efficient utilization of calculation resources. Compared with a system purely depending on a super-resolution method, the scheme technology can better give consideration to resolution and real-time performance, the instant response capability in a complex scene is ensured, secondly, through introduction of a signal processing technology, the resolution capability of the radar to a target angle is enhanced, and even in a dense target scene, the resolution of the radar to the target angle is improved. And the angle information of the adjacent targets can be distinguished as much as possible, so that the target detection and tracking capability of the system is improved.

The application discloses a kind of lithium ion battery distributed thermal process sensor fault estimation method, comprising: using two-dimensional partial differential equation to describe rectangular lithium ion battery distributed thermodynamic equation;Distributed thermal model of large size lithium ion battery is constructed;Using Chebyshev-Galerkin method, distributed thermodynamic equation and distributed thermal model are decomposed into reduced order model described by standard state space equation in time domain;State variable in reduced order model is constructed using sensor measurement output and a Hurwitz matrix, and enhanced reduced order model is constructed;Enhanced adaptive observer and error state space equation are constructed using enhanced reduced order model;Based on error state space equation, the fault strength of temperature sensor that fails is estimated using fast adaptive algorithm.The application can effectively estimate sensor fault, including time-invariant and time-varying fault, and single sensor and multiple sensor fault.

This invention discloses an off-grid DOA estimation method and system for polar impulse noise environments, belonging to the field of polar underwater acoustic detection. The method includes: obtaining an array received data matrix based on signals received through a uniform linear array with half-wavelength spacing; initializing the maximum number of iterations, column full-rank random matrices, and row full-rank random matrices; iteratively solving the improved mixed correlation entropy of the residual fitting error matrix as the objective function for subspace decomposition optimization based on the maximum mixed correlation entropy criterion to obtain the optimized array received data matrix; and using an iterative sparse projection algorithm to jointly estimate the sparse signal matrix and grid offset based on a new off-grid model constructed using multi-order Taylor expansion terms, thereby estimating the target's DOA. This invention overcomes the grid mismatch problem to achieve high-precision estimation of target location and exhibits good performance in both Gaussian noise and impulse noise environments.

The application discloses a kind of amplitude-phase error self-calibration's wave direction greedy estimation method, comprising: S1: the array received signal model including amplitude-phase error is established;S2: the effective grid size of search grid, iteration stop threshold is calculated;S3: coarse search is carried out on search grid, and the initial wave direction and initial signal corresponding to the maximum projection energy of detection are detected;If the maximum projection energy is lower than iteration stop threshold, then jump to S6;S4: Newton iteration refinement is carried out to initial wave direction, and more accurate continuous angle estimation and signal estimation are obtained;S5: source signal waveform and diagonal calibration matrix are alternately updated, and residual signal is updated accordingly, and current source is eliminated, and return S3;S6: weak source is eliminated and optimized by pruning, and the final wave direction and diagonal calibration matrix estimation are output.The application avoids error transmission, and significantly improves the wave direction estimation precision under unknown array error condition.

The invention provides a sensing data processing method and device based on a pressure insole, equipment and a medium, and belongs to the technical field of data processing. The method is applied to the pressure insole, the pressure insole comprises at least one sensing unit, each sensing unit comprises a hemispherical force guiding column and a corresponding set of capacitance nodes, and the hemispherical force guiding columns are used for distributing multi-dimensional pressure of the sole to the set of capacitance nodes so that the at least one sensing unit can output at least one set of sensing capacitance signals. The method comprises the steps of converting at least one group of sensing capacitance signals into at least one group of plantar distribution pressure values according to pressure calibration data of at least one sensing unit, and inputting the plantar distribution pressure values into a pre-trained multi-dimensional pressure prediction model in a two-dimensional vector form, and predicting based on the at least one group of plantar distribution pressure values to obtain a plantar three-dimensional pressure vector corresponding to the plantar multi-dimensional pressure. According to the invention, high-precision estimation of the complete three-dimensional ground reaction force can be realized.

The application provides an eight-transmitting and eight-receiving millimeter wave radar and a detection method thereof. The radar comprises an antenna group and a main control module. The phase centers of eight receiving antennas and eight transmitting antennas in the antenna group are distributed along first and second preset directions in a preset coordinate system, respectively, and are symmetrically distributed about a preset symmetry axis. The main control module is used to implement the detection method. The method comprises the following steps: controlling the eight transmitting antennas to transmit radar signals to a target to be detected in a preset order; when each receiving antenna samples echo signals of the same transmitting antenna, analyzing the corresponding echo signals to obtain point cloud data; performing phase compensation on the instantaneous horizontal angle and the instantaneous pitch angle corresponding to each point according to the eight transmitting antennas, the phase centers corresponding to each transmitting antenna, and each instantaneous speed to obtain a compensated horizontal angle and a compensated pitch angle; and taking the instantaneous distance, the instantaneous speed, the compensated horizontal angle, and the compensated pitch angle as perception information.