195results about How to "Improve estimation accuracy" patented technology

The invention relates to the field of control, in particular to a flight attitude control method and system for an unmanned aerial vehicle, and the method comprises the steps: obtaining an expected attitude angle instruction and actual attitude angle feedback of the unmanned aerial vehicle, calculating an attitude angle error, employing a tracking differentiator to calculate a transition process for the expected attitude angle instruction, and enabling a tracking rate factor to achieve the control of the flight attitude of the unmanned aerial vehicle when the instruction is subjected to step change. Based on initial attitude angle error setting, instruction smooth transition is ensured, based on a transition signal output by a tracking differentiator and an estimated value of an expansion state observer, a basic control quantity is generated through nonlinear state error feedback, and control gain is adjusted on line according to a lumped disturbance amplitude. And predicting the lumped disturbance historical sequence by using a grey prediction model, calculating a predicted value at a future moment, and carrying out algebraic summation on the basic control quantity, a compensation component of the lumped disturbance predicted value and a gyroscopic moment decoupling component to generate an attitude control moment signal so as to realize stable flight of the unmanned aerial vehicle.

The application provides a two-stage denoising neural network-based direction of arrival estimation method, device and electronic equipment. The method comprises the following steps: constructing an array receiving signal matrix and extracting an amplitude matrix and a phase matrix according to the receiving signal of a linear uniform array; extracting the median of all elements in the amplitude matrix and performing range compression to obtain an amplitude statistical feature; inputting the amplitude statistical feature into an adaptive threshold subnetwork to generate a compression coefficient; performing amplitude limiting processing on the amplitude matrix by using the compression coefficient, combining the new amplitude matrix and the phase matrix into a limited amplitude signal matrix; constructing a bounded covariance matrix with a second-order statistical form based on the limited amplitude signal matrix; inputting the bounded covariance matrix into a complex residual convolution subnetwork to generate a noise residual matrix; obtaining an equivalent covariance matrix based on the bounded covariance matrix and the noise residual matrix; and performing spatial spectrum search on the equivalent covariance matrix by using a MUSIC method to obtain a direction of arrival estimation result of a signal source.

This invention discloses a multi-array direct positioning method based on two-dimensional discrete Fourier transform. First, the cross-covariance of the received signals from each observation station is calculated, and the automatically matched azimuth information of each observation station is obtained through two-dimensional discrete Fourier transform (2D-DFT). Then, a phase rotation matrix is ​​constructed to compensate for the azimuth information of each observation station, resulting in more accurate azimuth information. Finally, the target position is directly solved by combining the accurate information from all observation stations using the least squares approach. This multi-array direct positioning method based on two-dimensional discrete Fourier transform can effectively locate targets. Furthermore, this method eliminates the need for two-dimensional spectral peak search, significantly reducing computational complexity. At low signal-to-noise ratios, its positioning performance is close to that of the minimum variance distortionless response (MVDR) direct positioning algorithm and outperforms the traditional angle-of-arrival (AOA-K-Means) clustering two-step positioning method.

The invention discloses an OFDM (Orthogonal Frequency Division Multiplexing) communication channel estimation and signal detection method and system, and belongs to the technical field of channel estimation. The OFDM communication channel estimation and signal detection method comprises the following steps: receiving an LDACS downlink OFDM baseband signal; pilot frequency and data subcarriers are separated; arranging pilot frequency observation into a time sequence block according to a frequency index, and constructing a channel estimation subnet input tensor; performing convolution feature extraction and LSTM (Long Short Term Memory) time sequence modeling on the pilot frequency time sequence block in sequence, and outputting channel estimation of each subcarrier; performing frequency domain equalization on the data subcarriers; the signal detection subnet firstly extracts time sequence features through LSTM, then distributes weights through a self-attention mechanism, and finally outputs bit judgment through full connection; performing parameter optimization on the channel estimation subnet and the signal detection subnet by adopting a two-stage training strategy; the number K of subcarriers, the number P of pilots, the CP length and subcarrier mapping are set according to an LDACS frame structure and a protection interval, and an LDACS link layer interface is directly connected. By adopting the OFDM communication channel estimation and signal detection method and system provided by the invention, the problems of poor estimation precision and insufficient robustness of the existing method in a complex LDACS airspace channel environment can be solved.

The invention discloses a biased binocular visual odometer method and device based on strawberry cuttlefish bionics. The biased binocular visual odometer method comprises the following steps: synchronously acquiring a bright field image and a dark field image collected by a biased binocular camera and inertial data of an inertial measurement unit; constructing a perceptual attention weight grid according to the bright field image and the dark field image, and performing brightness normalization processing on the bright field image and the dark field image; extracting lateral visual features based on the normalized image and the perceptual attention weight grid; and fusing the biased visual features with the inertial data, and solving through nonlinear optimization to obtain a carrier pose estimation result. According to the invention, the dynamic range of the binocular vision system is expanded, and the imaging and sensing capabilities of the system in an HDR scene are improved. And meanwhile, the positioning precision and robustness of the binocular visual odometer in an HDR scene are improved.

The application discloses a battery management method, system, device, equipment and storage medium. The method comprises the following steps: acquiring a current state of charge of a battery; determining a target confidence factor corresponding to the current state of charge according to an association relationship between the state of charge of the battery and the confidence factor, wherein the target confidence factor is used for representing a credibility of a current model parameter of a battery state management model; the battery state management model is used for simulating a voltage response characteristic of the battery; adjusting the current model parameter according to the target confidence factor to obtain a target model parameter of the battery state management model; and performing parameter updating on the battery state management model according to the target model parameter. The embodiment of the application effectively reduces the occurrence of parameter mis-correction, stably guarantees the effect of simulating the voltage response characteristic of the battery by the battery state management model, effectively improves the estimation accuracy of various operating states of the battery, and further improves the precision of the battery management.

This invention discloses a method for improving the accuracy of inertial guidance based on polynomial recursive least squares, comprising: constructing a guidance tool error model based on the difference between inertial guidance telemetry observations and a flight environment function, wherein the guidance tool error model satisfies a linear relationship; constructing a recursive formula based on polynomial recursive least squares based on the guidance tool error model; determining the estimated value of the guidance tool error coefficient at the current recursive time based on the difference between guidance telemetry observations at the current recursive time and the recursive formula of least squares, and using the estimated value of the guidance tool error coefficient to compensate for the guidance telemetry observations at the current recursive time. This invention can significantly improve estimation accuracy and efficiency, and has a wider range of applications and higher engineering value.

The application discloses a distributed interval observer optimization design method for multi-dynamic linear systems, and aims at a multi-dynamic distributed linear system disturbed by a bounded noise. Firstly, a reversible linear transformation is used to decompose an observable virtual subsystem. Then, a distributed interval observer containing output error feedback correction is designed to construct a local state interval estimation. Based on interval operation, an estimation error dynamic and its upper and lower bound explicit expressions are derived to verify the interval effectiveness. Further, Lyapunov stability theory and linear matrix inequality are used to optimize the observer gain and the collaborative coupling gain under Metzler, Hurwitz and numerical reliability constraints. Finally, the observation nodes are updated collaboratively based on the communication topology. The application does not require the system to meet strict assumptions such as cooperativeness, only needs noise upper and lower bound information, can adapt to various dynamic characteristics such as diagonal stability, strong coupling and weak damping oscillation, and has the advantages of high estimation accuracy, good numerical robustness and distributed collaboration support.

The application discloses a multi-target detection method in a complex electromagnetic interference environment for a pulse Doppler radar, a joint reverse diffusion process is introduced to jointly update interference samples and amplitude samples, and thus the problems of target signals being covered or submerged by interference signals, rising of a detection false alarm rate, and difficulty in effectively separating interference from targets can be overcome; a denoising score matching criterion is introduced to train a neural network of an interference score function, so as to solve the problems of high interference intensity and complex structure in multi-target detection in a complex electromagnetic interference environment; and a sparse Bayesian learning method is introduced to adaptively model a sparse structure of multi-target echo signals. The application further provides a multi-target detection device in a complex electromagnetic interference environment. The method provided by the application can realize modeling and suppression of multiple types of interference, and simultaneously improve the detection probability of multiple low observable targets in the distance and Doppler dimension, the interference suppression capability, and the system robustness in a complex electromagnetic environment.

The application provides a kind of energy storage battery estimation method and system based on deep learning fusion, it is related to energy storage battery management technical field, the application collects voltage, current time series data and calculates the ratio of voltage variation and current variation of adjacent sampling time, obtains dynamic resistance sequence, finally as trajectory feature, trajectory feature and temperature time series data time alignment fusion are input into time convolution network, and output residual capacity and health state;At the same time, based on the open-circuit voltage mapped by the residual capacity estimation value, the predicted voltage value is reconstructed by combining the dynamic resistance sequence and the current time series data, and the deviation between the predicted voltage and the measured voltage is constructed as a physical consistency constraint loss, which is combined with the estimation error loss to form a joint loss function. The parameters of the time convolution network are updated by back propagation, thereby solving the problem of failing to capture the dynamic resistance evolution law and lacking physical consistency constraint, and improving the estimation accuracy and model generalization reliability under complex working conditions.

The application discloses a charging remaining time calculation method, an electric vehicle and a storage medium, and relates to the technical field of charging time estimation. The method comprises the following steps: acquiring charging interval division parameters corresponding to a charging starting temperature; determining a charging interval set for remaining time calculation of charging to be performed according to the charging interval division parameters and a starting SOC; determining a plurality of temperature time lengths corresponding to each charging interval in the charging interval set according to a temperature-SOC parameter table and the charging starting temperature to obtain charging time lengths corresponding to the charging intervals one by one, wherein each temperature time length corresponds to the time length of temperature change of the charging interval under a group of temperature-SOC parameters in the temperature-SOC parameter table; and obtaining the remaining charging time length of the battery according to the charging time lengths corresponding to each charging interval in the charging interval set. According to the application, the influence of different charging starting temperatures on the charging interval and the temperature change of each charging interval are combined to calculate the charging time length, and the estimation accuracy is higher.

The application discloses a kind of emergency closing gate front backwater peak estimation method of gated channel, comprising the following steps: S1, one-dimensional unsteady flow simulation model of channel is constructed;S2, determine typical working condition of emergency control;S3, according to the simulation model in step S1, the typical working condition in step S2 is simulated, and the gate front backwater peak value of typical working condition is determined;S4, according to the gate front backwater peak value of typical working condition in step S3, draw gate front backwater peak value curve cluster;S5, according to the gate front backwater peak value curve cluster in step S4, determine the range of gate front backwater peak value when emergency disposal;S6, according to the gate front backwater peak value curve cluster in step S4, determine the accurate gate front backwater peak value when emergency disposal.The application has high estimation accuracy, is convenient and fast to use, can quickly and accurately estimate the gate front backwater peak value under different gate initial flow and gate flow amplitude, can not only simply and quickly estimate the range of gate front backwater peak value, but also can more accurately determine the gate front backwater peak value.

This invention discloses a method and system for quantitatively assessing the degree of fibrosis in kidney pathological sections, belonging to the field of medical image processing technology. The method includes: performing color normalization and adaptive contrast enhancement preprocessing on Masson stained section images; classifying pixel-level tissue components using a multi-component semantic segmentation network containing a channel-space dual-path attention module; calculating the fibrosis area ratio and outputting a CI score based on the Banff grading standard; generating a fibrosis spatial distribution heatmap; and calculating the annual fibrosis progression rate through longitudinal follow-up comparison.

The invention provides a non-invasive ventilator near-end flow estimation method and system based on deep learning and a ventilator, and the method comprises the steps: preprocessing a pressure value and a flow value of the ventilator, inputting the preprocessed pressure value and flow value into a flow estimation model, and outputting a near-end flow peak value; the flow estimation model is a convolutional neural network and comprises a one-dimensional convolutional layer, a one-dimensional inverse convolutional layer, a batch standardization layer and a full connection layer. The method has the advantages that the problems that due to the fact that part of parameters of a physical model are difficult to identify, the application range of the physical model is small, and the estimation precision is difficult to guarantee are solved, the deep learning model is adopted for fitting the complex nonlinear physical relation in the noninvasive breathing ventilation process, and the estimation precision and generalization ability are improved; the problem that the generalization ability of a deep learning model is poor due to parameter discontinuity of a clinical data set is solved, the coverage range and the information amount of the data set are increased by adopting a data enhancement and model simulation method, and the parameter estimation precision of the method and the generalization ability of the method to a complex use environment are improved.

This invention relates to the field of power system fault quantification technology, and discloses a method, system, equipment, and medium for quantifying distribution network line faults under typhoon weather. It calculates the wind-induced fault rate of distribution network lines by constructing a wind field correction model that incorporates micro-topography correction factors and wind direction-line angle correction factors. With the goal of minimizing power outage losses in the distribution network, a coupled influence model of transmission network faults on the distribution network line fault rate is constructed and solved to obtain the optimal load shedding amount for each load node in the distribution network and its corresponding operational constraint boundary. Under this boundary, the random process of load shedding allocation and load transfer of faulty lines within the distribution network is simulated using a double Monte Carlo sampling method, and the probability of line cascading faults caused by transmission-distribution coupling is calculated based on the simulation results. The combined time-varying fault rate and type of distribution network lines are determined by integrating the two fault rates, achieving accurate quantification of distribution network line faults under the influence of transmission-distribution coupling.

This invention discloses an intelligent tracking method for maneuvering swarm targets based on deep neural networks. This method incorporates deep learning into a Bayesian filtering framework, extracting the motion characteristics and process noise statistical characteristics of the swarm targets from measurement data using multiple deep neural networks. It then estimates the motion state transition matrix and process noise variance matrix of the swarm targets online, and uses Bayesian filtering to accurately estimate the motion state of the swarm targets' centroid. By modeling the swarm target contour as an ellipse centered at the centroid, the high-precision centroid motion state estimation results improve the contour estimation accuracy, ultimately achieving swarm target tracking. Compared to existing swarm target tracking methods, the proposed method does not require prior information to establish a target motion model, enabling high-precision tracking of non-cooperative maneuvering swarm targets in complex environments where prior information is lacking.

The invention discloses a battery state and early fault diagnosis system based on multi-source data fusion and artificial intelligence. The battery state and early fault diagnosis system comprises a data acquisition module, a feature extraction module, an artificial intelligence processing module and an early warning and execution module. The data acquisition module is responsible for synchronously acquiring voltage, current and temperature data from a battery pack in real time to form a multi-source data set for describing the real-time running state of the battery; the feature extraction module receives an original data stream from the data acquisition module. According to the method, the limitation of a traditional method based on a simple model or threshold judgment is broken through, the strong nonlinear mapping capability of the artificial intelligence model is utilized, and the deep correlation with the internal health state and the early fault of the battery is accurately mined from the high-dimensional features; according to the method, the estimation precision of the health state of the battery is remarkably improved, early weak fault symptoms which cannot be perceived by a traditional method can be identified, the crossing from post-event alarm to pre-event early warning is realized, and the safety of the system is greatly improved.

The application belongs to the technical field of robot environment perception and three-dimensional reconstruction, and discloses a quadruped robot multi-source information fusion mapping method and device based on factor graph optimization. The foot end odometer of the quadruped robot is introduced into the factor graph optimization framework as an independent constraint factor, and in the environment with missing or degenerated laser point cloud features, the motion constraint is provided, and the robustness of the system is fundamentally improved. An adaptive noise model based on the foot end contact state is designed, the confidence of the foot end odometer factor can be dynamically adjusted according to the contact quality, strong constraints are provided during normal walking, the weight is automatically reduced when slipping, and the pollution of the system caused by the false information is effectively inhibited. The tight coupling optimization of the laser radar, the IMU and the foot end kinematics information is realized, instead of loose coupling or post-fusion, so that the multi-source sensor information is deeply complementary at the state estimation level, and the overall estimation accuracy under complex terrain and dynamic motion is significantly improved.

The application discloses a key point detection and uncertainty synchronous prediction method, comprising the following steps: constructing a key point prediction model, outputting a key point category, a key point coordinate and a coordinate uncertainty; describing the key point coordinate as a one-dimensional Gaussian distribution; constructing a coordinate prediction loss function based on the KL divergence of the Gaussian distribution of the key point coordinate true value and the predicted value, and training the key point prediction model; and inputting a to-be-detected monocular image into the key point prediction model to realize synchronous prediction of the key point coordinate and the coordinate uncertainty. The application is applied to the monocular pose estimation field, can give the key point coordinate prediction uncertainty while predicting the key point coordinate, and can set the weight of the weighted monocular pose estimation problem as the reciprocal of the key point coordinate prediction uncertainty in the process of monocular pose estimation, then solve the weighted monocular pose estimation constraint equation to obtain a high-precision pose measurement result, and significantly improve the monocular visual pose estimation precision.

The application relates to the technical field of communication, and provides a frequency offset estimation method, device, equipment, storage medium and program product. The method comprises the following steps: performing at least two different pilot sequence divisions based on a received signal, and obtaining at least two division data corresponding to each pilot sequence division; determining an initial frequency offset estimation value based on the division data for each pilot sequence division; performing period extension on each initial frequency offset estimation value to obtain at least two extended frequency offset estimation values corresponding to each pilot sequence division; determining one extended frequency offset estimation value from each extended frequency offset estimation value corresponding to each pilot sequence division to obtain an optimal extended frequency offset estimation value group; and performing weighted combination based on each extended frequency offset estimation value in the optimal extended frequency offset estimation value group to obtain a target frequency offset estimation value of the received signal. The application can realize dual improvement of estimation accuracy and estimation range of frequency offset estimation.

This invention discloses a method, apparatus, equipment, and program product for calculating the volume of a coal pile, relating to the field of three-dimensional reconstruction technology. The method includes: acquiring global three-dimensional point cloud data of a target coal yard area, and projecting the global three-dimensional point cloud data onto the bottom plane of the coal pile to obtain each two-dimensional projection point and the target height corresponding to each two-dimensional projection point; triangulating each two-dimensional projection point to obtain a target three-network structure; back-projecting the target three-network structure into three-dimensional space to obtain each unit projected triangular prism; calculating the unit prism volume of a single unit projected triangular prism according to the target calculation method corresponding to the unit projected triangular prism; and calculating the target coal pile volume of the target coal yard area based on the unit prism volumes of each unit projected triangular prism. The technical solution of this invention reduces the error of the unit prism volume and improves the calculation accuracy of the coal pile volume.

The driving range estimation method based on unit charge range and battery aging correction mainly includes four steps: online battery SOC estimation, periodic battery SOH update, unit SOC range estimation, and driving range estimation. This invention uses periodic battery SOH estimation to correct the battery SOC estimation, and combines unit SOC range estimation to achieve accurate driving range estimation. It has the advantages of simple model structure, low computational load, high estimation accuracy, and easy implementation in vehicle BMS. Under normal driving conditions, the maximum driving range estimation error of this invention is within 3km, which meets the operating requirements of electric vehicles.

The application discloses a kind of based on low height angle optimization PPP-RTK positioning method and system, the method utilizes the average visible height angle of entire observation period in region as the attribute of satellite, all satellites in region are divided into high angle satellite group and low angle satellite group, in clock difference estimation stage, for high angle satellite group, take conventional estimation strategy, and for low angle satellite group, then take enhanced estimation strategy, improve the clock difference estimation precision of low height angle time, to improve user positioning precision, also can avoid the strategy jitter caused by satellite instantaneous height angle frequent change, guarantee the stability and robustness of clock difference estimation.

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 application discloses a kind of vehicle-mounted integrated navigation system wheel speed meter error online estimation and compensation method, this method under the premise of not changing original integrated navigation algorithm architecture, wheel speed meter double-loop update mode is designed, wheel speed meter error is estimated online using low-dimensional filter in error estimation loop, compensate wheel speed meter output in filter loop in combination with error estimation result, improve the precision of integrated navigation of inertial navigation system / wheel speed meter.The method realizes wheel speed meter error online estimation based on wheel speed meter double-loop update mode and dimensionality reduction filter, does not affect the original integrated navigation algorithm architecture, small amount of calculation, high estimation accuracy, after error estimation and compensation, the integrated navigation positioning performance under the condition that GNSS signal is blocked can be improved, and higher engineering application value is shown.

The application discloses a battery pack state of charge estimation method considering inconsistency of single bodies, and first establishes a battery pack inconsistency compensation model, then compensates the inconsistency degree by using virtual measurement noise, next obtains joint posterior probability of the virtual noise mean and variance and state quantity by using variational Bayesian inference, and finally realizes accurate battery pack SOC estimation by using an unscented Kalman filtering algorithm for recursion. The inconsistency compensation model of the application can reduce the influence of the inconsistency of single bodies on the SOC estimation precision while not significantly increasing the calculation amount, and has the characteristics of high estimation precision and good real-time performance.

The application provides an improved sparse learning out-of-grid direction-of-arrival estimation method based on variational Bayesian. The method is characterized in that: the original data received by a hydrophone array is preprocessed, a real value transformation is used to convert a vectorized covariance matrix signal in a complex number field to a real number field, and the idea of variational sparse Bayesian learning and grid evolution is combined to make the grid evolve from an initial uniform grid to a non-uniform grid adaptively in an iteration process. The evolution process includes grid updating and grid fission. The evolved grid points are gradually close to the real source position through the alternately iterative grid updating process and grid fission process. Compared with the traditional compressed sensing method, the method has higher DOA estimation accuracy, reduces the operation complexity, optimizes the operation efficiency, improves the resolution capacity of the source, and has higher application value in actual engineering, especially in the case of few snapshots and low signal-to-noise ratio.

The application discloses a kind of estimation method and system of vegetation canopy combustible moisture content based on meteorology and remote sensing data, comprising the following steps: first, combustible moisture content and various meteorological data and remote sensing data and other several kinds of subsidiary data are selected as combustible moisture content estimation data.Meteorological data includes air temperature, relative humidity, rainfall and wind speed.Remote sensing data includes two vegetation indexes: enhanced vegetation index and normalized vegetation index.Subsidiary data includes: root zone soil moisture, vapor pressure difference, drought index, fire weather factor.Then the long time sequence characteristics of meteorological data are extracted.The size of time window is determined first, and the experimental results show that the correlation coefficient of most sites is relatively high under the time window of 90-210 days.90 days, 150 days and 210 days of time window are selected respectively to extract the time characteristics of four kinds of meteorological data.Secondly, the samples of experimental area are divided into five vegetation classifications, which are closed shrub, sparse shrub, multi-tree tropical grassland, tropical savanna and grassland.Finally, the data sets of the five different vegetation types are sequentially adjusted to obtain the respective estimation model.

The invention discloses a multi-target positioning method and device based on a wireless signal, a medium and a product, and relates to the field of wireless sensing, and the method comprises the steps: carrying out the static noise filtering of a reflected signal; performing FFT operation on the filtered reflection signal on a fast time axis to obtain distance dimension data, and performing FFT operation on a slow time axis based on information in each distance gate corresponding to the distance dimension data to obtain a distance Doppler map; clutter suppression is carried out on the distance Doppler map through a CFAR algorithm, and a target distance index is recorded; constructing a two-dimensional data block based on the distance dimension data of the plurality of channels and the target distance index; on the basis of the two-dimensional data blocks, angle information of multiple targets is obtained through a self-adaptive second-order difference multi-signal classification algorithm; constructing a distance-azimuth spectrogram based on the angle information and the target distance index; therefore, when the multiple targets move in the range smaller than the radar angle resolution, accurate positioning and counting of the targets can be achieved.

The present application relates to the technical field of soil parameter remote sensing estimation, and particularly relates to a microwave soil equivalent temperature physical estimation method considering thermal sampling depth, comprising: obtaining soil temperature and humidity profile, surface temperature and soil texture data; combining dielectric constant model to calculate attenuation coefficients at different depths; determining thermal sampling depth based on the attenuation coefficients and dividing into multiple discrete layers; calculating average temperature using top and bottom temperatures of each layer according to soil heat conduction characteristics; calculating contribution weight of each layer to equivalent temperature based on attenuation coefficients and thickness of each layer; and obtaining microwave soil equivalent temperature by weighted sum of average temperature and weight of each layer. The present application has clear physical meaning, high estimation precision and is suitable for multiple frequencies, and overcomes the problem of limited precision or unclear physical meaning in the prior art.