45 results about "Least mean square error" patented technology

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.

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 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 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 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.

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.

This invention provides a method, apparatus, and terminal device for estimating transmitted signals, belonging to the field of wireless communication technology. The method includes determining a received signal vector, a channel matrix, and a noise variance; inputting the received signal vector, channel matrix, and noise variance into a signal estimation model to obtain an estimated transmitted signal value output by the signal estimation model; wherein, the signal estimation model is obtained by optimizing a minimum mean square error detector based on a Fourier transform algorithm and a pre-determined weighting factor, and the weighting factor is used to adjust the convergence step size. This invention uses the Fourier transform algorithm to replace matrix inversion in the minimum mean square error detector, avoiding explicit matrix operations, reducing computational complexity, and can be used in M-MIMO systems with large-scale antennas. Furthermore, adjusting the convergence step size based on the weighting factor is beneficial for stable convergence and prevents severe degradation of bit error rate performance under high-dimensional and high-order modulation conditions.

The invention discloses an IQ orthogonal calibration method, device and equipment and a storage medium, and the method comprises the steps: collecting the orthogonal mismatch state information of a current receiving signal and the operation state of a minimum mean square error calibration algorithm, and constructing a state vector; inputting the state vector into a depth Q network to determine a step length adjustment action, and updating a step length parameter of a minimum mean square error calibration algorithm based on the step length adjustment action; based on the step length parameter, calibrating the current received signal through a minimum mean square error calibration algorithm to obtain a calibrated signal and an error signal; determining a reward value according to the error signal, and storing the state vector, the step length adjustment action and the reward value as experience samples in an experience playback pool; and periodically updating the current network parameters of the deep Q network by using the experience samples in the experience playback pool until the error converges. According to the method, the convergence speed of IQ orthogonal calibration is improved, the steady-state residual error is reduced, and the adaptability to environment change is improved.

The application discloses a kind of FSI dynamic ranging methods based on high-order linear prediction-minimum mean square error, mainly solve the problem of slow speed of existing FSI ranging system to the distance to be measured solution, insufficient dynamic measurement capability.Solution includes: 1) construct to generate the optical frequency scanning interference ranging system required for distance solution interference signal;2) set the sweep rate of laser, so that it carries out optical frequency continuous tuning output, after interference, generates the sinusoidal interference signal that changes continuously on time domain after detection;3) the data acquisition device in FSI system carries out real-time acquisition to sinusoidal interference signal, and sample point is transmitted to measurement module;4) measurement module utilizes high-order linear prediction-minimum mean square error cascade algorithm to carry out real-time prediction to sampling data, and utilizes the interference signal frequency obtained to complete the real-time solution of distance.The application can make the distance solution speed of FSI system significantly improve, effectively improve the ranging rate.

The invention belongs to the technical field of wireless communication, and discloses a cellular-removed large-scale MIMO distributed dual-time-scale hybrid precoding method, which comprises the following steps of: constructing a user average channel covariance matrix at each access point (AP); carrying out eigenvalue decomposition on the user average covariance matrix and extracting a main eigenvector to construct an initial simulation precoding matrix; performing constant modulus constraint processing on the initial analog precoding matrix to obtain an analog precoding matrix; a user sends an orthogonal pilot frequency, each AP carries out instantaneous channel state information estimation by adopting a minimum mean square error criterion, and a downlink channel is obtained based on the reciprocity of uplink and downlink channels; each AP designs a digital precoding vector according to a downlink channel; the AP allocates power to the user according to the power constraint; system spectral efficiency is calculated to assess method performance. According to the method, the expandability of the system is effectively improved while the channel estimation overhead and the operation complexity are reduced, and the method is suitable for efficient implementation of the honeycomb-removed large-scale MIMO system.

The application discloses a channel estimation method and related device, wherein the method comprises: performing minimum mean square error (MMSE) channel estimation based on the product of the first amplitude factor and the first noise power of the first dimension, the first channel autocorrelation matrix of the first dimension, and the first diagonal matrix to obtain the channel estimation value matrix of the first dimension; wherein the channel estimation value matrix of the first dimension is obtained by performing MMSE channel estimation in the non-last step of the multi-dimensional step-by-step MMSE channel estimation; the first amplitude factor is a positive number less than or equal to 1; and performing MMSE channel estimation based on the channel estimation value matrix of the first dimension, the second channel autocorrelation matrix of the last dimension, the second noise power and the second diagonal matrix to obtain the multi-dimensional channel estimation value matrix. The method can improve the performance of the multi-dimensional step-by-step MMSE channel estimation.

The invention discloses an AFDM peak-to-average ratio suppression method based on pilot frequency position optimization and pre-chirp. The AFDM peak-to-average ratio suppression method comprises the following steps: executing pilot frequency and data overall cyclic shift in a DAF domain for a pilot frequency frame; performing IDAFT on the processed pilot frequency frame to convert the pilot frequency frame into a time domain signal, calculating the PAPR of the time domain signal, and finding out a group of arrangement modes with the lowest PAPR as output; candidate pre-chirp parameters provided by the data frame are selected, and IDAFT is executed to convert the candidate pre-chirp parameters into time domain signals; performing amplitude limiting processing on a time domain signal of the data frame, performing optimization by using an iterative ACE algorithm, calculating the PAPR of the updated signal, and selecting a group with the lowest PAPR as the output of the data frame; and a receiving end respectively demodulates the pilot frequency frame and the data frame by using a maximum likelihood-minimum mean square error detector. According to the invention, the PAPR of the transmission signal of the AFDM system is effectively suppressed, and the transmission reliability of the system is improved.

The embodiment of the application provides an estimation method of continuous-discrete maximum correlation entropy distributed cubature Kalman filtering, which comprises the following steps: establishing a continuous-discrete target tracking model in a multi-sensor network; based on a maximum correlation entropy criterion, a 1.5-order ITO Taylor expansion, a cubic cubature rule, an average consensus method and a minimum mean square error criterion, calculation is performed to obtain a continuous-discrete maximum correlation entropy distributed cubature Kalman filtering method; and for the continuous-discrete target tracking model, target tracking is performed according to the continuous-discrete maximum correlation entropy distributed cubature Kalman filtering method to obtain accurate target state information. The method can accurately estimate the parameters of an aircraft target and improve reliability.

The invention provides a multi-sensor target fusion method and system based on sequential filtering, and belongs to the technical field of intelligent driving, and the method comprises the steps: obtaining a fusion track of a plurality of target sensors, and calculating a track state at a current moment according to a track state at a previous moment; adopting a greedy algorithm to respectively match the plurality of target sensors with the current time track; and updating the track state by adopting sequential filtering according to the matched measurement value. According to the method, the greedy algorithm is adopted for matching, the time complexity is far smaller than that of a KM algorithm and the like, but an approximate global optimal matching result can be achieved in most cases, and the calculation amount can be reduced to a great extent. According to the method, sequential filtering is adopted to update the flight path state, the method serves as a recursive filtering algorithm, measurement data are comprehensively considered, meanwhile, the method is easy to implement, optimal estimation of the system state can be provided based on the minimum mean square error criterion, and in addition, filtering parameters and measurement weight values can be adjusted according to the actual situation.

The application discloses an anti-retransmission interference capturing method based on fast iterative minimum mean square error filtering and relates to the technical field of satellite navigation and positioning. In the small-delay retransmission interference scene, iterative multi-parameter joint estimation is carried out on the basis of two-dimensional correlation, the parameter estimation precision is improved through RMMSE filtering, the identification difficulty of target signals and retransmission interference signals is reduced, and the signal detection probability is improved. The system used by the anti-retransmission interference capturing method based on fast iterative minimum mean square error filtering comprises the following modules: a global two-dimensional correlation and peak detection module, a single-path signal parameter estimation initialization module, a single-path signal iterative multi-parameter joint estimation module, an interference reconstruction and elimination module, a local two-dimensional correlation and peak detection module, a double-path signal parameter estimation initialization module, a double-path signal iterative multi-parameter joint estimation module and a signal detection and decision module.

An inertial-based method and system for detecting bending strain in a pipeline, a device, and a medium. The method comprises: acquiring position information of a pipeline of a preset length, collected by an IMU (S1); performing a wavelet transformation on the position information to obtain an initial target position information (S2); performing sliding filtering on the initial target position information through a sliding window according to a preset sequence and window width, and determining a data window at each sliding of the sliding window on the initial target position information (S3); determining a fit coefficient by the minimum mean squared error depending on the respective data windows (S4).Using a numerical value at the center of each data window as a filter value, determining a filter sequence based on the respective filter values ​​and the fit coefficient, and using the filter sequence as the second target position information (S5); determining a pipe bending strain force based on the second target position information (S6); detecting the pipe bending strain based on the pipe bending strain force (S7). This method removes noise twice, thereby improving the accuracy of the position information calculated by an inertial measurement unit (IMU) and solving the problem of noise affecting the position information calculated by the IMU.

The application relates to the technical field of communication, and specifically provides a reconfigurable intelligent surface (RIS) assisted ocean communication method and system based on LR-BCD, which comprises the following steps: constructing an RIS assisted ocean communication system model; constructing an ocean double-path channel model based on the ocean double-path propagation theory and in combination with a sea surface reflection coefficient; extracting low-rank characteristics of the channel by using singular value decomposition (SVD), projecting high-dimensional beamforming and phase shift optimization problems into a low-dimensional subspace; constructing a low-dimensional and rate maximization problem, and converting the problem into a convex subproblem by using a weighted minimum mean square error (WMMSE) criterion; alternately updating a low-dimensional precoding matrix and a phase shift vector by using a block coordinate descent (BCD) algorithm; and finally reconstructing the optimized low-dimensional variables back to a high-dimensional space and performing discretization and quantization. The application utilizes the sparsity of the ocean channel, significantly reduces the calculation complexity of large-scale RIS optimization through dimension reduction processing, and meanwhile, maintains the system and rate performance equivalent to that of a full-rank algorithm.

This invention belongs to the field of wireless communication, specifically relating to a STAR-RIS-assisted 6G integrated sensing and communication beamforming design method. The method includes constructing a STAR-RIS-assisted integrated sensing and communication system model; under constraints of transmit power, sensing performance, and multi-user service quality, establishing an optimization problem aimed at maximizing multi-user communication and speed by jointly designing the base station transmit beamforming and the transmission-reflection coefficients of STAR-RIS; transforming the optimization problem into an easily tractable equivalent form based on a weighted minimum mean square error equivalent transformation; decoupling the optimization problem based on the equivalent form into two independent sub-problems using an alternating optimization method; solving the sub-problems using a successive convex approximation method and a semi-definite relaxation method, and iteratively updating the algorithm until convergence; this invention effectively improves system communication performance while ensuring sensing performance and multi-user communication quality.

The application 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 application adopts a minimum mean square error-based adaptive noise cancellation method, can track and eliminate non-stationary noise interference in real time, automatically optimizes filter parameters in an unknown channel environment, and enables a signal detection system to adapt to different background noise conditions. By using a cyclic spectrum analysis method, the cyclic stationary characteristics of the direct spread signal are extracted, and robust signal detection is realized in a low signal-to-noise ratio environment. The existence of the signal is determined through a characteristic spectrum peak on a non-zero cycle frequency, and the carrier frequency and 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 robustness and reliability of detection 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 robustness of detection is improved.

The application provides a target tracking system anti-eavesdropping distributed tracking detection method under low communication resources, and belongs to the technical field of distributed tracking detection. The application establishes a random dynamic model of a target tracking system under low communication resources; then, a time-varying distributed filter under the least mean square error is designed according to the dynamic model; then, a filter gain matrix of each sensor node in the system at a time point is designed; the filter gain matrix is substituted into the filter to obtain the state estimation of each sensor node in the system at the time point; and the estimation error covariance upper bound of each sensor node in the system at the time point is calculated; let, and repeat until the condition is met. The application solves the problem that the existing distributed tracking detection method is difficult to simultaneously process the target tracking system anti-eavesdropping distributed tracking detection problem with a periodic scheduling mechanism and an encryption and decryption mechanism, thereby improving the tracking performance of the tracking system.

The invention relates to the technical field of communication, and particularly provides a reconfigurable intelligent surface-assisted ocean communication method and system based on LR-BCD, and the method comprises the steps: constructing an RIS-assisted ocean communication system model; constructing an ocean double-path channel model based on an ocean double-path propagation theory in combination with a sea surface reflection coefficient; singular value decomposition (SVD) is utilized to extract low-rank features of a channel, and high-dimensional beam forming and phase shift optimization problems are projected to a low-dimensional subspace; constructing a low-dimensional and rate maximization problem, and converting the problem into a convex subproblem through a weighted minimum mean square error (WMMSE) criterion; utilizing a block coordinate descent (BCD) algorithm to alternately iteratively update a low-dimensional precoding matrix and a phase shift vector; and finally, reconstructing the optimized low-dimensional variable back to a high-dimensional space and carrying out discretization quantization. According to the method, the sparsity of an ocean channel is utilized, the calculation complexity of large-scale RIS optimization is remarkably reduced through dimension reduction processing, and meanwhile the system and rate performance equivalent to that of a full-rank algorithm is kept.

The application discloses a high energy efficiency transmission method in a cell-free massive URLLC system, which comprises the following steps: (1) an AP performs minimum mean square error channel estimation according to an uplink pilot signal to obtain an estimated channel state information vector, and returns the vector to a CPU; (2) according to the obtained estimated channel state information, the CPU controls an AP downlink transmission power strategy and a user association strategy with the maximum system energy efficiency as a target; (3) the AP performs downlink data transmission under the control of the CPU, and calculates a signal-to-interference-and-noise ratio and a reachable rate of a user end; (4) the user feeds back the reachable rate to the AP, the CPU further constructs and solves an energy efficiency optimization problem to jointly optimize the power control strategy and the user association strategy, and feeds back the updated strategy to the AP. The application meets the user communication reliability and real-time requirement, and improves the system energy efficiency.

The invention belongs to the field of wireless communication, and particularly discloses a high-dimensional MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) implementation method and system based on space diversity. At a sending end, an input binary sequence is subjected to symbol mapping through a high-dimensional signal constellation diagram, space-time coding is carried out on a mapped high-dimensional signal coordinate component, fast Fourier inversion is carried out on each space-time code word, signals are converted from a frequency domain to a time domain, cyclic prefixes are added, and a high-dimensional signal is obtained. Signals are transmitted to a wireless channel through different antennas. At a receiving end, the time domain signal of each receiving antenna is subjected to cyclic prefix removal, then Fourier transform is carried out, the time domain signal is converted into a frequency domain signal, and an MIMO detector adopts a maximum likelihood or minimum mean square error algorithm, and after space-time decoding and high-dimensional signal de-mapping are carried out, an originally sent binary sequence is recovered. According to the invention, the spectrum efficiency can be improved and the system reliability can be enhanced.

The present application relates to the technical field of remote sensing, in particular to a kind of leaf life span inversion method based on PROSAIL model, minimum mean square error matching is carried out in combination with remote sensing observation spectrum, and the average leaf life span of pixel can be quantitatively estimated on the scale of region and time series, important input is provided for ecosystem carbon budget simulation, by introducing the functional trait of leaf life span, the present application can depict the spatio-temporal distribution and variation law of canopy leaf age structure.Compared with the traditional method of ground investigation, the present application can obtain leaf life span information on regional scale and long time series, greatly improve the spatial coverage and time continuity of leaf age structure detection, provide new key input for ecosystem age structure, carbon cycle model parameterization and vegetation resilience analysis.

The application discloses a kind of under combined constraint based on directional diagram matching anti-interference waveform design method, establishes the MIMO cognitive radar system of antenna layout using uniform linear array;Directional diagram model is constructed;Under the condition that waveform constant modulus constraint and similarity constraint are satisfied, the problem model of directional diagram matching design based on minimum mean square error criterion is established;For optimization problem under combined constraint, the similarity constraint is simplified using Lagrange multiplier method, and only the optimization problem with constant modulus constraint is obtained;The geometric characteristics of non-convex constraint are excavated, the constant modulus constraint is associated with Riemannian manifold in nature, so that the constrained non-convex optimization problem is converted into an unconstrained convex optimization problem on the manifold;Based on optimization algorithm, the optimization problem model on the manifold is solved, and the anti-interference waveform is obtained.The application can generate optimal transmit waveform to resist specific interference, significantly improve the efficiency of transmit beam directional diagram matching, and obtain transmit waveform with better blur characteristics.

The application discloses a kind of water unmanned vehicle trajectory tracking method and system based on distributed fusion strategy, belong to automatic control field.First, the state estimation problem of water unmanned vehicle based on multiple radars is constructed;Then, by means of radar measurement vector, the mean and variance of random variable after transformation by nonlinear radar measurement model are approximated using unscented transformation, and a local nonlinear filter is designed under the least mean square error criterion;Further, the distributed fusion estimation value is obtained by fusing local estimates based on distributed fusion strategy, and the real-time tracking of unmanned vehicle motion trajectory is realized.The simulation results show that the application realizes the real-time tracking of water unmanned vehicle trajectory.

The application provides a homomorphic encryption-based soft decryption method, which comprises the following steps: a receiving end extracts log-likelihood ratio of each bit from a modulation symbol; first moment and second moment of a ciphertext element are calculated; Gaussian mean and variance are obtained by using linear property of homomorphic encryption to propagate to a scaling domain; considering periodic aliasing of a plaintext modulus, candidate plaintext posterior probability is calculated to obtain minimum mean square error posterior mean and variance; the posterior mean is taken as a soft decryption output, and the posterior variance is taken as uncertainty information input to a decoder for image reconstruction. The application replaces traditional hard decision decryption with continuous MMSE posterior estimation, converts discrete decryption error into continuous signal degradation, concentrates on real plaintext in high signal-to-noise ratio, provides sub-integer precision, tends to be uniform in low signal-to-noise ratio, estimates smooth prior mean, completely avoids catastrophic error of all-or-nothing, and makes reconstruction quality change smoothly with channel signal-to-noise ratio, and threshold collapse no longer occurs.

This invention discloses a distributed multiple access system for a symbiotic radio network based on reconfigurable holographic devices, comprising: a background network (BS / UE) and an IoT network consisting of K reconfigurable holographic devices (RHDs) and IoT receivers (IRs); each RHD integrates an antenna, a load modulator, and a reconfigurable holographic surface (RHS), which converts a reference wave carrying IoT information into a target wave and radiates it into free space by activating subwavelength radiating elements, overcoming the size limitations of traditional RIS / phased arrays; the IoT receiver (IR) employs distributed decoding based on random code-based code division multiple access (CDMA), utilizing matched filter (MF) or minimum mean square error (MMSE) receivers combined with asymptotic signal-to-interference-plus-noise ratio (SINNR) theory to eliminate the need for real-time random code feedback; the radiation pattern is optimized based on the weighted minimum mean square error (WMMSE) algorithm to maximize RHD and data rate. The advantages of this invention are: device size supports integration of micro-IoT devices; IoT SINNR is improved by more than 20dB; and it enables massive IoT device access.

The invention provides a wireless sensor network node positioning method based on dual communication radiuses and He horse optimization, which improves the problems of error accumulation caused by rough hop count quantization and uneven anchor node distribution of the traditional DV-Hop algorithm and easy local optimum of coordinate calculation, and obtains refined hop count information by using a dual communication radius mechanism, thereby improving the positioning accuracy of the wireless sensor network node based on the dual communication radiuses and the He horse optimization. The hop distance estimation error is suppressed in combination with the minimum mean square error criterion, global search optimization is carried out on unknown node coordinates through a Hemma optimization algorithm, and the adaptation relation between node positions and network topology characteristics is extracted, so that higher-precision node positioning is realized. The method specifically comprises the following steps that: an anchor node firstly broadcasts beacons with a small radius r and then broadcasts beacons with a standard communication radius R to divide'near areas / far areas' so as to refine hop counts; reconstructing average hop distance calculation based on a minimum mean square error criterion to obtain an optimal hop distance; constructing a fitness function with the goal of minimizing the sum of the square differences of the estimated distance from the unknown node to the anchor node and the real distance, and converting positioning into an optimization problem; the population is initialized through a river horse optimization algorithm, parameters and individual positions are iteratively updated through the three stages of position updating, defense and development until the preset number of iterations or fitness convergence is achieved, and the river horse position with the lowest fitness is output to serve as the optimal estimation coordinate of the unknown node. According to the method, the node positioning precision and robustness of the wireless sensor network are effectively improved, the method is suitable for the Internet of Things sensor network deployed in a large-scale and low-cost mode, and reliable support is provided for network topology construction, target tracking and the like.

This invention discloses an adaptive filtering and noise reduction system based on a variable step-size minimum mean square error algorithm, comprising a main signal link and a closed-loop control module. The main signal link integrates a low-noise amplifier, an analog-to-digital converter, and an adaptive filter, responsible for precise signal amplification, efficient analog-to-digital conversion, and flexible filtering. The closed-loop control system, through the close collaboration of a noise detection unit, a spectrum analysis unit, and a parameter adjustment unit, captures and analyzes the noise spectrum characteristics in real time, and then dynamically optimizes the filtering parameters to achieve the best noise reduction effect. The parameter adjustment unit of this invention employs a variable step-size minimum mean square error algorithm. Leveraging its unique real-time amplitude feedback mechanism for the error signal, combined with the nonlinear characteristics of the hyperbolic tangent function, it cleverly achieves dynamic adjustment of the step size, thus achieving an ideal adaptive balance between rapid convergence and low steady-state error.