130 results about "Least mean square error" patented technology

The invention relates to a beam forming design method of an active RIS-assisted ISAC system, and belongs to the field of communication perception integration. A multi-user large-scale multiple-input single-output system model assisted by communication and perception integration and a system model based on an ISAC system assisted by an active RIS are constructed, and a beam forming vector and a reflection coefficient of the active RIS are respectively optimized by adopting an alternating optimization algorithm. According to the method, the propagation blockage problem is solved, the system performance is further improved, the multiplicative fading effect problem of the passive RIS is solved by using the active RIS, the weighted minimum mean square error algorithm, the continuous convex approximation algorithm and the positive semidefinite relaxation algorithm are combined, the system effectiveness is improved to the maximum extent, and the system performance is improved to the maximum extent. The system utility is the sum of the weighted sum rate of the system and the target detection power, the target detection capability is remarkably enhanced while the weighted sum rate of the user is improved, and a new solution is provided for performance optimization of the ISAC system.

The invention discloses a low signal-to-noise ratio direct spread signal detection method based on noise cancellation, and belongs to the field of communication spectrum sensing. The self-adaptive noise cancellation method based on the minimum mean square error is adopted, non-stationary noise interference can be tracked and eliminated in real time, filtering parameters are automatically optimized in an unknown channel environment, and a signal detection system can adapt to different background noise conditions. And stable signal detection is realized in a low signal-to-noise ratio environment by utilizing a cyclic spectrum analysis method and extracting the cyclic stability characteristic of the direct spread signal. The existence of the signal is judged through the characteristic spectrum peak on the non-zero cyclic frequency, and the carrier frequency and the pseudo code rate are further estimated, so that more accurate signal identification and parameter extraction are realized, the influence of noise uncertainty on the detection performance is avoided, and the detection robustness and reliability are improved. Welch smoothing processing and short-time Fourier transform are combined, the variance of spectrum estimation is reduced when the cyclic spectrum is calculated, and the detection robustness is improved.

The invention discloses a method and system for testing the electrical performance of a semiconductor chip, and the method comprises the steps: building a mapping relation between dielectric loss and loss frequency through obtaining the dielectric loss data, internal material characteristics and dynamic load power factors of the chip under each frequency band, recognizing a loss abnormal region, and analyzing the influence of the loss abnormal region on the performance of the chip; correcting test data by adopting a minimum mean square error algorithm, and optimizing the impedance of the power supply network and the dynamic switching characteristic of a functional module; calculating the breakdown voltage distribution of each layer in the chip, identifying a high-voltage risk region, optimizing the voltage distribution by using a gradient descent algorithm, and dynamically adjusting the working voltage and current; power factor fluctuation is analyzed through a sliding window algorithm, an abnormal area is identified, a power factor is optimized through a PID algorithm, and the working state and transient response of a chip in a dynamic load are improved. According to the method, the problems of nonlinearity of high-frequency dielectric loss, non-uniform breakdown voltage distribution and unstable power factor response under a dynamic load are solved, and comprehensive and accurate evaluation and optimization of chip performance are realized.

The invention discloses a multi-path cooperative communication method, belongs to the technical field of wireless communication, and is used for solving the problems of inaccurate path selection, unreasonable power distribution and inaccurate signal combination caused by dynamic change of a channel state in multi-path communication. The method comprises the following steps: acquiring channel state information of at least three communication paths in real time through a transmitting end, wherein the channel state information comprises signal strength, frequency offset and noise power spectral density; dynamically dividing and distributing the data stream to each path based on the path quality factor, and adjusting the transmitting power by combining the signal strength and the noise difference value; a receiving end carries out time domain alignment by adopting a sliding window cross-correlation algorithm, and carries out phase compensation based on a minimum mean square error criterion; path reallocation is triggered when the continuous bit error rate exceeds a threshold. According to the method, the multi-path resource utilization rate can be optimized, and the communication stability and the data transmission efficiency are improved.

The invention provides a method for assisting a movable antenna to realize secure communication by STAR-RIS, and relates to the technical field of wireless communication. According to the method, the safety and the rate of the system are maximized by jointly optimizing the beam forming of the base station, the position of the movable antenna and the STAR-RIS coefficient matrix. The method specifically comprises the following steps: constructing a downlink MIMO system and establishing a joint optimization problem; decomposing the joint optimization problem into three sub-problems by adopting an alternate optimization framework; respectively utilizing a particle swarm optimization algorithm and a weighted minimum mean square error method to be combined with a Majoritization-Minimalization algorithm to solve each sub-problem, and respectively utilizing the particle swarm optimization algorithm and the weighted minimum mean square error method to be combined with the Majoritization-Minimalization algorithm to solve each sub-problem; and alternately executing related steps until system security and rate convergence to obtain a solution of the joint optimization problem. By the adoption of the method, the safety communication performance of the system is remarkably improved, full-space coverage and precise beam regulation are achieved, the algorithm convergence is high, the solving efficiency is high, and the method is suitable for a downlink system with multiple users and multiple eavesdropping nodes.

The invention belongs to the technical field of artificial intelligence, and discloses an energy data acquisition encryption and privacy protection method and system. The power grid frequency deviation FREQ of a power line carrier communication system is obtained at the time of power grid data acquisition, and deviation information at different time points is combined into a random number sequence by using a hash function; a specific pulse signal is sent at a data acquisition point to obtain PLC channel pulse response CIR, a minimum mean square error estimation algorithm is used for processing, and key features such as the maximum pulse amplitude are extracted to serve as channel feature values. And then, inputting the random number sequence and the channel characteristic value into a deep reinforcement learning model trained by a large amount of historical data to generate an encryption key. And finally, encrypting the acquired energy data by adopting an AES algorithm based on the key, transmitting the energy data through an OFDM technology, and meanwhile, generating the key again according to the change of a channel characteristic value by setting a self-adaptive security mechanism. According to the method, the safety and privacy of energy data acquisition and transmission are remarkably improved.

The invention relates to the technical field of signal demodulation, and discloses an OFDM (Orthogonal Frequency Division Multiplexing) signal demodulation method of a signal analyzer. According to the invention, through joint optimization of a synchronous detection and frequency offset compensation mechanism, the symbol positioning precision in a low signal-to-noise ratio environment is improved, and subcarrier interference caused by a cyclic prefix pseudo peak value and frequency offset phase ambiguity is effectively suppressed; a time-frequency two-dimensional adaptive interpolation strategy is adopted to enhance the robustness of channel estimation to non-uniform pilot frequency distribution and a fast time-varying channel, and data subcarrier response distortion is reduced; in combination with minimum mean square error equalization and dynamic noise reduction processing, inter-symbol interference and a noise accumulation effect are greatly weakened, and the bit error rate performance of a high-noise scene is improved; the error control technology is updated through a closed-loop channel, a demodulation error propagation path is blocked, and cooperative improvement of system demodulation stability and precision in a complex multi-path environment is achieved.

A method includes determining estimated features comprising second order statistics based on at least one received signal. The method also includes classifying, using a machine learning network, each channel of the at least one received signal into a channel profile based on the estimated features. The method also includes obtaining multiple minimum mean square error (MMSE) channel estimation weights from a database based on the estimated features, the database storing (i) representative MMSE estimation weights and (ii) channel cluster representatives indexed by the estimated features. The method also includes applying a respective MMSE channel estimation weight for each channel.

The invention discloses an OTFS joint channel estimation and data detection method and model based on deep learning in a satellite scene. The method comprises the steps of constructing a data set, designing a pilot frequency design scheme, constructing a joint channel estimation and data detection deep learning model, repeatedly training the deep learning model, obtaining channel state information and data symbols through prediction data, comparing a network prediction result with the corresponding channel state information and data symbol labels, and determining the channel state information and the data symbols. Evaluating the output of the deep learning model through the minimum mean square error and the cross entropy loss; the obtained optimized deep learning model is used for obtaining channel state information and data symbols through verification data. According to the method, OTFS received signals are learned through the deep learning network so as to complete channel estimation and data detection tasks at the same time, a pilot frequency design scheme and a data slicing processing scheme are adopted at the same time, high performance is kept, the accuracy of channel estimation and data detection is remarkably improved, and the overhead of pilot frequency resources is reduced at the same time.

The invention discloses a time-frequency domain joint channel equalization method for a single-carrier NOMA (Non-Orthogonal Multiple Access) system, and solves the problems that a single carrier is poor in frequency selective fading resistance and is not matched with NOMA power multiplexing signals in the prior art. The method comprises the following steps of: at a receiving end of a single-carrier NOMA system, converting a received signal of each frame into a frequency domain by using FFT (Fast Fourier Transform), and carrying out data separation to obtain a section of unique word sequence and an effective data sequence; estimating channel state information in a frequency domain by utilizing prior information and a section of unique word sequence to obtain a channel estimation result corresponding to each symbol, and performing minimum mean square error frequency domain equalization on a section of effective data sequence according to the result to obtain a corresponding frequency domain signal; performing fine adjustment on the corresponding frequency domain signal in the time domain by using a time domain soft decision equalization algorithm of the power distribution factor to obtain an equalized time domain signal; according to the method, more accurate channel compensation is realized, and the decoding performance and the anti-interference capability of the system are improved.

The invention discloses an archive room full life cycle management system based on a radio frequency identification technology, and relates to the field of archive management, the optimal weight vector of each reader-writer antenna array is obtained through a minimum mean square error algorithm, the transmitting phase and amplitude of each node signal are accurately adjusted according to the optimal weight vector, and in the spatial dimension, the optimal weight vector of each reader-writer antenna array is obtained. Through cooperative control and spatial filtering of beams transmitted by a plurality of distributed reader-writer nodes, beam main lobe energy is converged to a target passive RFID electronic tag, a radiation zero point is formed in the direction of a metal file cabinet, meanwhile, spatial power superposition and directional interference suppression are realized, multipath reflection interference in a metal environment is reduced, and the reliability of the metal file cabinet is improved. The signal-to-noise ratio of the label is fundamentally improved, the response rate of the label is ensured to be improved to 98% or above through a dynamic frequency modulation mechanism, and the problem of signal multipath reflection caused by a metal file cabinet is effectively solved.

The invention discloses a joint beam hopping and precoding scheduling method based on an RSMA multicast satellite system, and relates to the technical field of satellite communication, and the method comprises the steps: building a multicast multi-beam satellite communication system model based on simulation parameters of a satellite communication system; the model comprises a plurality of antenna feed sources and clusters; determining the total provided capacity of different clusters in each time slot through time slot allocation and a beam hopping mode; determining the optimization problem of the model according to the total provided capacity of different clusters; the optimization variables of the optimization problem are the optimal solutions of the precoding vector, the common rate allocation and the CBH strategy vector, which are solved based on a weighted minimum mean square error method. And solving the optimal solution of the CBH strategy vector based on an integer programming method. And determining an optimized multicast multi-beam satellite communication system model by integrating the optimal precoding vector, the common rate allocation and the CBH strategy vector. According to the invention, the interference between beams in the cluster is reduced, and the communication reliability is improved.

The invention provides a distributed state monitoring and fault diagnosis method for a DC micro-grid. Belongs to the technical field of DC micro-grids, and solves the technical problem that the existing state monitoring method in practical application cannot accurately monitor the DC micro-grid state with load disturbance, process uncertainty and measurement noise at the same time. According to the technical scheme, the method comprises the following steps: a, establishing a distributed state space model of each power generation unit in the DC micro-grid; b, designing a time-invariant parameter matrix of the distributed observer by using a full-rank decomposition technology, and determining a time-varying parameter matrix of the distributed observer under the meaning of a minimum mean square error; and c, constructing a distributed residual generator based on local state monitoring. The method has the beneficial effects that the difficulty of high false alarm rate caused by fault propagation is overcome, the communication calculation amount is reduced, the risk of network attack is reduced, and the system has higher reliability and expandability.

The invention discloses a multi-target tracking method based on asynchronous weighted time sequence optimization, a program, equipment and a storage medium, and belongs to the field of underwater multi-UUV cooperative detection of multiple targets. The method comprises the following steps: firstly, setting a tolerance time threshold value related to a target speed and an allowable error, approximating asynchronous data at a short time interval as synchronous data, identifying and screening active and passive sonar information from the same target by virtue of an association algorithm, and eliminating random clutters; then, the target detection precision is improved through a minimum mean square error weighted fusion method; and finally, inputting the optimized detection data into a probability hypothesis density filter to complete updating estimation of the multi-target state and number. According to the algorithm, the multi-target state estimation precision is improved, the excessive estimation phenomenon of the number of the targets is effectively reduced, the problem of time sequence asynchronization of active and passive sonar detection information in a multi-target detection environment can be solved, and the challenges of high-density clutter aliasing on multi-target state estimation precision reduction and excessive estimation of the number of the targets can be solved.

The invention discloses a nuclear subspace optimization method for full-wave inverse scattering high-precision imaging, and relates to the field of machine learning and full-wave inverse scattering in order to solve the problem that an existing subspace-based optimization method is insufficient in precision in the field of full-wave inverse scattering imaging. According to the method, the robustness framework of a traditional subspace optimization method and the self-adaptive learning advantage of a kernel method are combined, iteration of an optimization method of an original subspace is transferred into a regeneration kernel Hilbert space, and evolution in the iteration process is learned with the minimum mean square error as the learning criterion. According to the kernel-based subspace optimization method, a mature and interpretable framework of a subspace optimization method is combined with the adaptive learning ability of a kernel method, a robust and universal algorithm is provided for full-wave inverse scattering in the fields of medical imaging, geophysical exploration, nondestructive testing and the like, and an accurate reconstruction result can be provided.

The invention discloses a communication and perception fused MIMO-OTFS channel estimation method, and relates to the field of channel estimation. Perception information acquired by a radar is used as an initial support set of an improved algorithm, initial estimation is completed by combining with an optimal support set of delay, Doppler and angle domains, and then accurate estimation is performed by using a linear minimum mean square error method. The influence of noise on the system can be effectively relieved, the pilot frequency overhead is remarkably reduced, and the estimation precision is improved.

The invention provides an elastic wave CT (Computed Tomography) signal denoising method for improving the inversion precision of karst cave exploration, which belongs to the technical field of engineering exploration and comprises the following steps of: performing normalization preprocessing on a received signal, suppressing random noise by adaptive filtering, converting a time domain signal into an F-K domain and identifying a surface wave, filtering a surface wave signal by a Gaussian band elimination filter, and converting the F-K domain signal back to the time domain. The method comprises the following steps of: highlighting a take-off characteristic of a weak P wave covered by a strong surface wave through normalization; using a minimum mean square error adaptive filter to accurately filter random noise; subsequently converting to an F-K domain, and smoothly attenuating the surface wave through a Gaussian band elimination filter by utilizing the frequency wave number distribution difference of the surface wave and the P wave; the method realizes layered removal of random noise and surface waves, finally outputs high-purity direct P-wave signals, greatly improves P-wave travel time pickup and wave velocity calculation precision, and remarkably improves inversion precision of an elastic wave CT exploration method in detection of geologic bodies such as karst caves and fracture zones.

The invention particularly relates to a model-free deflection angle estimation method based on a radial basis function neural network. The method comprises the following steps: acquiring measurement data of a master inertial navigation system and a slave inertial navigation system; based on the distribution characteristics of the measurement data in the training set, updating the clustering centers of the hidden layer radial basis function to obtain the number of the clustering centers; constructing a radial basis function neural network based on the measurement data of the training set and the number of the clustering centers, and training the radial basis function neural network by adopting a minimum mean square error algorithm to obtain a trained radial basis function neural network; and inputting measurement data in the test set into the trained radial basis function neural network to obtain a predicted deflection angle, performing calculation compensation on the pose amount of the master-slave inertial navigation system by combining a transfer alignment error equation according to the predicted deflection angle, and outputting a final alignment result based on a Kalman filtering model of a model-free deflection angle. The dynamic deformation angle modeling compensation method solves the problems that in an existing dynamic deformation angle modeling compensation method, model parameters are difficult to determine, and transfer alignment is caused when the parameters are time-varying.

The invention relates to the technical field of low earth orbit (LEO) satellite communication, in particular to a deep expansion large-scale MIMO LEO satellite precoding method based on a graph neural network (GNN). The method comprises the following steps: firstly, constructing a power-limited energy efficiency maximization precoding problem of large-scale MIMO LEO satellite communication; secondly, converting an original problem into a series of iterative solution sub-problems through a Dinkelbach algorithm, and converting the sub-problems into convex problems by using a weighted minimum mean square error (WMMSE) method to obtain an iterative update expression of a user precoding vector; the GNN is constructed for the complex matrix inversion in the user precoding vector updating process to perform depth expansion, and the Taylor expansion is applied to approach the matrix inversion operation in the GNN framework, so that the calculation efficiency is further improved. According to the method, higher energy efficiency of the system can be realized, the complexity of solving the optimization problem is remarkably reduced, and the operation speed is increased.

The invention relates to a dynamic self-adaptive communication, sensing and calculation integrated method for an air-space-ground integrated network. The method comprises the following steps: S1, constructing an air-space-ground integrated network model fusing a low earth orbit satellite, an unmanned aerial vehicle and ground users / edge nodes; s2, establishing a cross-layer communication-perception-calculation integrated joint optimization model, and respectively forming two modes of binary unloading and partial unloading according to whether a task can be segmented or not; s3, for a binary unloading mode, realizing distributed solution and generating an unloading decision and a beam / power / bandwidth / computing resource scheme by using a dynamic adaptive prediction alternating direction multiplier (DAP-ADMM); and S4, aiming at a partial unloading mode, adopting a weighted minimum mean square error alternative optimization method, introducing a receiving equalizer, error weight and other auxiliary variables, executing block coordinate descent iteration, and outputting an unloading proportion, power distribution and a beam forming result. According to the DA-ISCC architecture and the optimization algorithm thereof provided by the invention, cross-domain calculation unloading with low time delay, high throughput and low overhead can be realized in a high-dynamic space-air-ground integrated network environment, and the DA-ISCC architecture and the optimization algorithm thereof can be widely applied to emergency communication, low-altitude logistics, ocean monitoring and other scenes.

A DNN-based OTFS time-frequency domain situation awareness and channel estimation method models the frequency domain selective fading channel in wireless communication as an autoregressive process, and then converts the estimation problem into an autoregressive coefficient estimation process. Training data is obtained through processes such as discrete sampling of the channel impulse response function, DFT transformation, feature matrix representation, and channel prior information generation. The preprocessing of the training data is completed, and a data and model driven neural network is designed. The neural network is trained with a large amount of prior channel data. The optimal frequency domain correlation parameter estimation under the minimum mean square error of the network is solved by offline training and online estimation, and then the corresponding channel response is obtained. The algorithm can achieve lower bit error rate and complexity under the same signal-to-noise ratio.

The invention discloses a communication, sensing and computing resource allocation device in a space multilayer network, and the method comprises the following steps: constructing an air-space-ground communication, sensing and computing integrated system, integrating three functions of sensing, communication and computing by an unmanned aerial vehicle of an air-base layer, and performing communication and sensing interference management between the unmanned aerial vehicle and the ground layer through an NOMA-MIMO technology, the tasks can be further unloaded to the low-orbit satellites at the sky level; in order to minimize the weighted energy consumption of the system, a joint precoding, beam forming and resource allocation iterative optimization algorithm is provided; according to the algorithm, an original problem is decomposed into a precoding design sub-problem, a beam forming design sub-problem, an unloading proportion sub-problem and a computing resource allocation sub-problem, and the sub-problems are solved through fractional programming based on quadratic transformation, a weighted minimum mean square error and a continuous convex approximation method. The method shows remarkable superiority in the aspect of optimizing the weighted total energy consumption of the system, and solves the problems of common inductance computing resource allocation, interference management and the like in the spatial multilayer network.

The present application discloses a joint beam hopping and precoding scheduling method for an RSMA multicast satellite system, which relates to the field of satellite communication technologies. The method includes constructing a multicast multi-beam satellite communication system model based on satellite communication system simulation parameters; the model includes a plurality of antenna feeds and clusters; through time slot allocation and beam hopping patterns, determining the total provided capacity of different clusters within each time slot; based on the total provided capacity of different clusters, determining the optimization problem of the model; the optimization variables of the optimization problem are precoding vectors, common rate allocation, and CBH strategy vectors, and solving the optimal solutions of the precoding vectors and common rate allocation based on the weighted minimum mean square error method. Solving the optimal solution of the CBH strategy vector based on the integer programming method. Combining the optimal precoding vectors, common rate allocation, and CBH strategy vectors to determine the optimized multicast multi-beam satellite communication system model. The present application reduces the interference between beams within a cluster and improves the reliability of communication.

The application discloses a fluid antenna assisted ISAC system joint beam forming and position optimization method based on a quantum particle swarm optimization algorithm. Specifically, an ISAC multi-user system model equipped with a two-dimensional fluid antenna is established, and a beam forming optimization problem is constructed; a weighted least mean square error algorithm is used to convert a communication and rate target function, so that a complex fractional problem is converted into an integral problem, and solving is simplified; an alternating optimization algorithm is used to decompose the original joint optimization problem into multiple sub-problems; an auxiliary variable and a receiving beam former are alternately solved, an iterative solution of a beam forming vector is solved by using a successive convex approximation algorithm, and a fluid antenna position problem is solved by using a quantum particle swarm optimization algorithm. The method of the application effectively improves the communication and rate under the restrictions of the fluid antenna position, the minimum perceived beam pattern gain and the maximum base station transmission power, and is superior to a traditional ISAC system equipped with a fixed antenna.

The invention discloses a DOA (direction of arrival) estimation method and a DOA estimation system for a dual hybrid structure assisted by collaborative learning. The DOA estimation method specifically comprises the following steps: constructing a dual MIMO (multiple input multiple output) receiver H2AD-FD system model in which an all-digital FD sub-array and a heterogeneous hybrid H2AD structure are combined; generating a plurality of candidate angle sets by performing DOA estimation on each group in the H2AD structure; for the FD sub-array, a rough estimation value of the radiation source direction is calculated by adopting a multilayer deep learning algorithm or a Root-MUSIC method; taking the rough estimation value of the FD sub-array as a true solution class initial sample point, and establishing a relationship between the FD mode and the H2AD mode so as to deduce a true solution set of the H2AD mode; and an optimal weighted fusion algorithm is designed based on a minimum mean square error principle, two parts of true solutions are synthesized, and a final DOA estimated value is obtained. According to the invention, low-time-delay and high-time-efficiency accurate direction finding is realized.

The invention discloses a self-calibration optimized image denoising method using convexity, which belongs to the technical field of image processing, is used for image denoising under the condition that the noise level is unknown, and comprises the following steps: generating a noisy image and a secondary noisy image pair, and constructing a conditional denoising network; based on a linear minimum mean square error estimation theory, deducing a denoising estimator and calculating a variance of an estimation error, and introducing a noise ratio parameter to carry out normalization analysis; determining an optimal synthetic noise variance through derivation or a ternary search algorithm by using convexity of an error variance function; performing empirical formula calibration on the optimal synthetic noise variance; and training and executing the denoising network by using the calibrated optimal variance, and outputting a final denoised image. According to the method, the robust training process is constructed under the condition that the noise level is unknown, noise statistical characteristics can be effectively learned, and the limitation that the noise level needs to be known in advance in a traditional method is overcome.

The invention discloses an adaptive non-uniform power normalized minimum mean square error underwater acoustic communication equalization method based on a dynamic leakage factor, and belongs to the technical field of underwater acoustic communication. The method comprises the following steps: acquiring a baseband sampling signal x; a weight vector w0 is initialized to be equal to 0, and an input signal vector u0 is a zero vector with the length L; a baseband sampling signal x is used as an input signal, a training sequence is used as an expected signal d, and self-adaptive dynamic updating of a filter weight is realized by adjusting a leakage factor in real time, so that self-adaptive adjustment in a signal equalization process is completed. According to the method, a dynamic adjustment strategy of a leakage factor is introduced, and combined optimization of weight attenuation and power normalization is adopted, so that the equalization precision and the algorithm convergence speed are effectively balanced, finally, channel distortion is accurately compensated and equalized, and the communication quality and stability of an underwater acoustic communication system in a complex marine environment are improved.

The present application discloses a beam design method for active reconfigurable smart reflector (active-RIS) assisted dual function radar and communication (DFRC), in which the radar serves a plurality of communication users while perceiving a target, while active RIS is used for communication enhancement. According to the method, on the premise of ensuring certain radar detection power, transmitting waveform with constant modulus and limited power budget, the WSR of a communication user is maximized by jointly designing transmitting beam forming of a radar and reflected beam forming of an active RIS (Radio Information System); and an efficient alternating optimization algorithm based on weighted minimum mean square error and fractional programming is provided to solve the proposed height non-convex optimization problem. A simulation result shows that compared with a passive RIS, the adding of the active RIS effectively improves the weighting sum rate of system communication.

The embodiment of the present invention provides a method and device for linear resonant motor parameter identification. The LRA parameter identification uses the zero-input response discrete-time system model of the second-order linear resonant system to construct a second-order forward prediction error filter, and uses the augmented Wienerhof equation to solve the coefficients of the discrete-time system model to obtain the minimum mean square error estimate, which has the advantages of strong anti-noise ability and good robustness. Through the estimated discrete-time system model, the impulse response invariant method can directly calculate the poles, free oscillation frequency and damping coefficient of the continuous-time system without iterative calculations.

The application discloses a kind of super-network auxiliary data model double-drive MIMO-OFDM signal detection method, comprising: a signal detection network with a set of adjustable parameters, and a trainable super-network.First, using linear least mean square error algorithm obtains channel estimation, will estimated channel matrix H LMMSE As the input of super-network, adjustable parameter Ω hyper Provided to signal detection network;Then, channel matrix H LMMSE And received signal vector y are real value decomposition, together with the equivalent noise variance information derived and input to signal detection network, signal detection network is unfolded by the expectation propagation algorithm enhanced by graph neural network, in combination with real domain channel matrix real domain received signal vector environmental information and the parameter Ω hyper Provided by super-network, give the estimation to sending symbol vectorFinally, demapping is carried out to estimated symbol vector, and the estimation to original sending bit is obtainedThe application has high detection performance, consumes less time, and has strong generalization, and can adapt to scene change.