Permanent magnet mechanical antenna fast fading parameter estimation method based on dynamic vehicle vibration

Abstract

The invention belongs to the technical field of wireless communication and signal processing, and particularly relates to a vibration fast fading parameter estimation method of a vehicle-mounted permanent magnet mechanical antenna (PMMA) communication system based on dynamic vibration characteristics. Comprising the following steps: step 1, antenna vibration amplitude modeling; step 2, channel modeling; step 3, channel parameter estimation; and step 4, adjusting the transmission rate according to the estimated channel parameters. According to the method, in the dynamic unmanned vehicle PMMA communication, the probability prior distribution of the vehicle is innovatively designed, and the distributed parameters are estimated through the EM algorithm, so that the quantitative evaluation of the vibration condition of the vehicle is realized, the vibration condition can be evaluated by a receiver, corresponding processing is performed, and the communication stability can be improved by using further measures.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication and signal processing technology, and specifically relates to a vibration fast-fading parameter estimation method for a vehicle-mounted permanent magnet mechanical antenna (PMMA) communication system based on dynamic vibration characteristics. The method is particularly suitable for high-reliability magnetic induction communication in complex electromagnetic environments such as underground tunnels and urban canyons. Background Art

[0002] With the development of unmanned driving technology, vehicle-mounted communication systems are increasingly being used in complex electromagnetic environments (such as underground and disaster sites). Permanent magnetomechanical antennas (PMMA) are an ideal choice due to their excellent penetration performance in the low-frequency band and their miniaturization. However, existing technologies have the following defects: (1) Traditional RF channel models (such as Rayleigh fading) cannot accurately describe the unique mechanical vibration fading characteristics of PMMA; (2) Existing vehicle-mounted magnetic induction communication systems do not consider the rapid channel changes caused by dynamic vibration; (3) The parameter estimation method under the quasi-static channel assumption deteriorates sharply when the vehicle is in motion. The time-varying channel problem caused by vibration is an obstacle to the further development of underground unmanned equipment. Currently, there is no fast fading estimation solution for vibration.

[0003] With the rapid development of autonomous driving technology, the demand for in-vehicle communication systems in complex electromagnetic environments, such as underground tunnels and disaster relief, is becoming increasingly urgent. Traditional RF communications face severe signal attenuation in these scenarios. For example, at a depth of 30 meters underground, electromagnetic wave signal attenuation can reach over 80dB, resulting in a sharp decrease in communication range and reliability. Permanent magnet mechanical antennas (PMMA), due to their excellent low-frequency penetration and compact size, are an ideal solution for this problem. Compared to traditional coil antennas, PMMA antennas achieve longer communication ranges while maintaining the same power consumption and exhibit enhanced immunity to electromagnetic interference.

[0004] However, in practical applications, the vehicle-mounted PMMA system faces a key problem that has long been ignored: rapid channel fading caused by vehicle vibration. Since PMMA relies on mechanical movement to generate electromagnetic fields, its communication performance is extremely sensitive to changes in antenna posture. When the vehicle is driving on uneven roads, mechanical vibrations will cause random deviations in the antenna posture, which in turn will cause sudden fluctuations in the channel gain. This fast time-varying characteristic makes the traditional channel model based on the quasi-static assumption completely invalid - when the vibration variance is When it is >0.3, the system bit error rate deteriorates by more than 4 times compared with the static environment, which seriously restricts the communication reliability.

[0005] In existing technologies, traditional radio frequency communication channel models (such as Rayleigh fading) cannot accurately describe the unique mechanical vibration fading characteristics of PMMA; and although magnetic induction-based vehicle communication solutions take into account the penetration issue, they generally ignore the impact of dynamic vibration. This technological gap has seriously restricted the application and development of underground unmanned equipment.

[0006] While the fast fading model proposed in this paper can use moment estimation to estimate parameters, it still suffers from low accuracy and instability. Currently, no publicly available solution can simultaneously meet the following key requirements: real-time tracking of vibration-induced fast fading (delay <5ms) and accurate characterization of the unique mechanical-electromagnetic coupling characteristics of PMMA. This technical bottleneck makes the communication reliability of existing vehicle-mounted PMMA systems in complex terrain difficult to meet the requirements of industrial-grade applications, and innovative solutions are urgently needed. Summary of the Invention

[0007] In response to the problem of rapid channel fading in vehicle-mounted permanent magnet mechanical antenna (PMMA) systems in dynamic vibration environments in the existing technology, the present invention aims to provide a method for estimating the fast fading parameters of permanent magnet mechanical antennas based on dynamic vehicle vibration. Based on the boundary chi-square distribution model that characterizes the fast fading characteristics of the channel caused by vibration, a real-time channel estimator based on the EM hybrid algorithm (Expectation-Maximization Algorithm) is developed to achieve sub-second fast fading parameter tracking, solve the parameter estimation problem of fast fading caused by vibration, and adjust the communication strategy in real time according to the fast fading parameters.

[0008] Technical Solution A method for estimating fast fading parameters of a permanent magnet mechanical antenna based on dynamic vehicle vibration comprises the following steps: Step 1: Antenna vibration amplitude modeling; Step 2: channel modeling; Step 3: channel parameter estimation; Step 4: Adjust the transmission rate according to the estimated channel parameters.

[0009] Beneficial effects In dynamic unmanned vehicle PMMA communication, an innovative probability prior distribution of the vehicle is designed, and the distribution parameters are estimated through the EM algorithm, thereby realizing a quantitative evaluation of the vehicle vibration condition. This is beneficial for the receiver to evaluate the vibration condition and perform corresponding processing, and is conducive to the use of further measures to improve the stability of communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a flow chart of the channel parameter estimation and adaptive rate adjustment method of the present invention; Figure 2is a schematic diagram of a PMMA antenna according to an embodiment of the present invention; Figure 3 is a schematic diagram of a transmitting end used in an embodiment of the present invention; Figure 4 is a schematic diagram of a demodulation process used in an embodiment of the present invention; Figure 5 is a schematic diagram of a receiving end used in an embodiment of the present invention; Figure 6 Schematic diagram of a transceiver model used in an embodiment of the present invention; Figure 7 is a schematic diagram of a vehicle vibration model used in an embodiment of the present invention; Figure 8 This is a performance comparison chart used in the embodiments of the present invention. DETAILED DESCRIPTION

[0011] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0012] Example 1 Figure 1 The figure is a processing flow chart of the method of the present invention.

[0013] The method of the present invention is applicable to a vehicle-mounted permanent magnet mechanical antenna communication system (PMMA system), including a transmitting end and a receiving end. The PMMA antenna at the transmitting end is as follows: Figure 2 As shown, the system consists of two core components: a drive motor and a permanent magnet. The drive motor is typically a servo motor, and its closed-loop control system enables precise speed control, meeting the requirements of low-frequency communications. The permanent magnet is made of high-remanence neodymium iron boron material. The receiver uses a magnetic sensor or coil to detect the time-varying magnetic field.

[0014] Figure 3 This paper demonstrates the typical hardware architecture of the PMMA communication and positioning system transmitter. The system operates in two modes: communication and positioning. In communication mode, for an FSK modulation system, the baseband signal is modulated by a microcontroller, and the motor speed is adjusted at a specific frequency by the motor controller, thereby precisely controlling the rotational speed of the permanent magnet and generating a magnetic field signal with a time-varying frequency. The main energy consumption in this mode comes from the permanent magnet speed control and motor friction losses. In positioning mode, the system transmits a fixed-frequency pilot signal for channel parameter estimation and position calculation, operating in a similar manner to the communication mode.

[0015] In order to transmit control instructions back, communication with the receiving end is required. For the receiving circuit, in order to minimize the coil impedance, the coil impedance needs to be balanced. The circuit impedance is set to: in is the center frequency of FSK communication, is the inductance of the receiving coil. After setting the circuit parameters, you can prepare for communication. Using FSK modulation for communication, the mathematical expression of the binary frequency shift keying signal can be expressed as: Where, is the signal amplitude, and Corresponding to the angular frequencies of the two code elements respectively, and is the initial phase. The transmitter controls the motor speed according to the need to transmit code elements to transmit control information back. In order to optimize the transmission efficiency of the target, a transmission model under eddy current loss is introduced, and its optimal frequency can be expressed as: in, is the distance between the transmitter and receiver, estimated by the positioning algorithm, is the conductivity, is the magnetic permeability in vacuum.

[0016] Figure 5 The hardware architecture of the receiver is shown, primarily consisting of an antenna module, amplifier, filter, and analog-to-digital converter. The filter suppresses out-of-band noise. The antenna module, serving as the receiving core, consists of three perpendicular receiving units. Each unit forms an LC resonant circuit with a coil and matching capacitors. To compensate for the additional impedance introduced by the coil to the time-varying signal, matching capacitors are used to reduce the overall circuit impedance.

[0017] At the receiving end, the signal is first filtered to remove power frequency interference, and a digital notch filter is used to suppress such noise, which can be constructed as follows: By adjusting the extreme shrinkage factor , which can achieve an optimal balance between noise suppression depth and signal distortion. Afterwards, a bandpass filter is used to filter out noise in other frequency bands. The filter can be designed based on a second-order IIR bandpass filter, and its transfer function can be constructed as: After passing through the filter, demodulation is performed. The demodulation of FSK signals mainly adopts non-coherent demodulation. Non-coherent demodulation adopts envelope detection technology. After the received 2FSK signal is separated into different frequency components by a bandpass filter, the signal amplitude information is extracted by the envelope detector, and then the original data is restored by low-pass filtering and sampling judgment. The specific process is as follows Figure 4 shown.

[0018] Each module exchanges data via a specific interface protocol, forming a complete closed-loop control system. The system uses an embedded processor to implement core algorithms, ensuring real-time performance. This modular design enables the system to effectively detect the impact of vehicle vibration on communication performance, significantly improving communication reliability in complex environments.

[0019] Example 2 After the communication links are established between the transmitting and receiving ends, the present invention innovatively adds a channel estimation module and adopts a parameter estimation method based on the boundary chi-square distribution model.

[0020] A method for estimating fast fading parameters of a permanent magnet mechanical antenna based on dynamic vehicle vibration is implemented as follows: (e.g. Figure 1 ) To address the problem of rapid channel fading caused by vehicle vibration during operation, the proposed vehicle-mounted permanent magnet mechanical antenna communication system introduces a vibration perception model at the physical level for the first time. It also develops a channel modeling and parameter estimation scheme that incorporates vibration statistical priors. The system, consisting of a transmitter and receiver, employs a modular architecture, ensuring excellent scalability and robustness.

[0021] Step 1: Antenna vibration amplitude modeling In order to describe the impact of fast fading caused by vibration on communication quality, a communication model of rotating permanent magnets is first established to obtain the vehicle communication quality and angle factor. The relationship is as follows: The channel gain is expressed as: , consists of two parts: distance factor and angle factor ; The distance factor Defined as: Indicates the effect of distance on gain; Angle Factor Defined as: Indicates the effect of angle on gain.

[0022] In order to further analyze the probability distribution of the angle factor, a vehicle vibration model is established based on the Gaussian road surface assumption as follows: Vehicle vibration amplitude It has a mean of zero and a variance of The random variable represents the normal distribution of the antenna center and the monitoring point (radial distance from the antenna center axis). To simplify the subsequent analysis, and Normalized, defined as and .

[0023] Specifically, the process of establishing the rotating permanent magnet communication model is as follows: When the observation point is far enough away ( , is the length of the magnet), a rotating permanent magnet can be considered as a magnetic dipole. The magnetic field generated by a rotating permanent magnet is given by: in, is the volume of the permanent magnet; is the remanence of the magnet; is the distance between the source point and the field point; is the polar angle, that is, the angle between the line connecting the measurement point and the origin and the positive direction of the z-axis; is the azimuth, that is, the angle between the line connecting the measurement point and the origin and the positive direction of the x-axis; 、 、 are the unit vectors corresponding to the lower corner parameters respectively.

[0024] The rotating permanent magnet generates a time-varying magnetic field, and the receiving coil receives the transmitted signal by detecting the induced current. The power of a typical sinusoidal modulation transmitter is proportional to the square of the modulation bandwidth and the magnet design parameters. Therefore, for a permanent magnet antenna operating at a fixed modulation frequency, the transmitted power is constant and is recorded as In order to calculate the received power , using polar coordinates, such as Figure 6 When the distance between the transmitter and receiver is much smaller than the signal wavelength, the system can be considered to be in the near field, where the electromagnetic field exhibits strong spatial variations. According to Faraday's law of electromagnetic induction ,in is the magnetic flux through the coil, is the number of coil turns: From this, the received power can be calculated, and the channel gain can be derived as: in, is the resistance of the coil, is the angle between the antenna's rotation axis and the line connecting the transmitter and receiver, and It is the angle between the coil normal and the line connecting the transmitter and receiver.

[0025] The channel gain of a permanent magnet antenna is mainly affected by the distance between the transmitter and receiver and the angle between the two ends. This is because the magnetic field strength decreases with distance, and the direction of the antenna relative to the receiver affects the efficiency of signal coupling. In order to facilitate the subsequent analysis of the impact of mechanical motion on communication, it is necessary to distinguish the effects of angle and distance on power gain. Therefore, Decomposed into two parts: distance factor and angle factor , where the distance factor Defined as: Indicates the effect of distance on gain, angle factor Defined as: Indicates the effect of angle on gain.

[0026] Therefore, the channel gain can be expressed as: In an ideal two-dimensional communication scenario, the coil is directly aligned with the antenna to achieve the highest signal strength. In this case, and ,lead to The maximum value of However, if the antenna is angularly deviated due to mechanical vibration ,but ,and This shows that the power gain of the channel is affected by the change of the antenna angle. For unmanned vehicles with a short wheelbase, when the vibration is severe, 10-30°, which will cause a significant loss in received power. Therefore, in order to study the probability distribution of the channel, it is necessary to first model the vibration of the antenna.

[0027] After establishing the communication model, we need to obtain the distribution of angle deviation to establish the probability distribution of fast communication fading. The present invention establishes a vibration model of the vehicle based on the probability distribution model of the vehicle.

[0028] like Figure 7 As shown in Figure 1, the installation of a permanent magnet antenna on a moving vehicle will inevitably be affected by the vibration of the vehicle body. In order to accurately evaluate the impact of vibration on communication performance, an accurate vibration model needs to be established. is the vehicle wheelbase, is the vehicle vibration angle. Based on the basic principles of vehicle engineering, the vehicle vibration system (VVS) can be simplified into a linear system, where the vibration of the front and rear wheels of the vehicle is the system input, and the vibration of the superstructure is the system output. Considering that vehicle vibration is the result of the superposition of multiple independent factors such as terrain roughness, wheel slip and mechanical vibration, according to the central limit theorem, the vibration amplitude of the front and rear wheels of the vehicle can be simplified into a normal distribution with a mean of zero. After the vehicle vibration enters the linear VVS as input, its output should also obey the Gaussian distribution, which means that the vehicle vibration amplitude (Gaussian Road Disturbance Response, GRDR) has a mean of zero and a variance of The random variable represents the normal distribution of the antenna center and the monitoring point (radial distance from the antenna center axis). To simplify the subsequent analysis, and Normalized, defined as and The establishment of this vibration model lays the foundation for the subsequent analysis of signal fading characteristics caused by vibration.

[0029] Step 2: Antenna fast fading modeling Based on the rotating permanent magnet communication model and vehicle vibration model established in step 1, the Gaussian vibration probability distribution of the road surface is converted into the probability distribution of the communication channel gain, which is in the form of fast fading. The angle gain is further proposed. The probability distribution followed is used to describe the fast fading phenomenon of communication when the vehicle is moving.

[0030] The resulting angular gain The PDF is: The probability distribution function is: .

[0031] The specific process is as follows: Based on the aforementioned vibration model, the electromagnetic field emitted by a permanent magnet mechanical antenna is inevitably affected by vehicle vibration. Accurately assessing the extent of this influence requires in-depth analysis of the statistical characteristics of rapid fading, as these characteristics directly determine the performance of the communication system. This paper innovatively proposes a truncated chi-square distribution model, based on the vibration characteristics of vehicle-mounted antennas, that accurately describes the statistical patterns of the fading process.

[0032] Vehicle response to road surface disturbances and vehicle speed It can be regarded as a Gaussian white noise input with a mean of zero, that is: In actual scenarios, The value of is limited to a certain range. Exceeding this range may cause the vehicle to roll over, thus making the model invalid. Although this extreme case is rare, in order to ensure the universality of the study, when the vibration amplitude of the communication device is too large, it is assumed that the antenna direction remains at the failure threshold. , normalized for .therefore, can be rewritten as: In order to get PDF that needs to be analyzed PDF.

[0033] The probability distribution of can be divided into two parts: ,Right now When , the chi-square distribution is used to represent Distribution under normal conditions. ,Right now When the probability distribution is an impulse function, its amplitude is equal to the probability distribution from The integral to infinity represents all cases where the vehicle's vibration angle exceeds the limit. Typically, these cases are extremely rare.

[0034] In summary, when hour, The probability density of is: in, represents the chi-square distribution, that is: when hour, The probability density of is: in, for: The probability of taking other values is zero.

[0035] therefore, The PDF can be expressed as: in, represents an impulse function.

[0036] The angle of the antenna will significantly affect the signal strength at the receiving end. The present invention defines the channel angle gain To describe this effect, based on this model, the probability distribution of mechanical vibration can be converted into the probability distribution of signal. In uplink transmission, assuming that the distance At this point, the normal vector of the coil is aligned with the antenna. At this point, due to the vibration of the vehicle body, the vehicle's antenna deviates from the vertical direction by an angle This deviation of the antenna will significantly affect the channel gain, thereby affecting the communication quality. Therefore, in order to study the channel gain change caused by vibration, it is only necessary to analyze the angle factor The distribution of . and When the angle factor for: The PDF of is given. Since the function is truncated, additional measures must be taken to deal with the truncation points. Considering that when the antenna displacement is Exceed When the communication stops, it means that becomes 0. Therefore, Rewritten as: Therefore, when hour, when hour, To summarize, the PDF is: The probability distribution function is: Based on the fast fading gain distribution derived above, a channel parameter estimation method based on pilot signal can be further proposed. In practical applications, the influence of large-scale fading can be balanced by the characteristics of large-scale fading. For the convenience of discussion, it is assumed that in addition to the angle coefficient All factors other than the above have been balanced in advance, and the system model can be expressed as ,in To receive the signal, To transmit the signal, is the signal gain, is Gaussian white noise. Considering the signal gain and power gain The relationship is , and in the pilot communication phase, it is assumed that the vehicle does not roll over (i.e. ), the channel model can be simplified as: in, Represents the signal received by the sampling coil, is the signal emitted by the transmitter, is Gaussian white noise, is the signal gain, whose PDF is: To normalize the PDF, for: Step 3: Channel parameter estimation Based on the above modeling, the present invention uses a pilot signal as an estimation trigger. The transmitter periodically transmits a fixed-frequency pilot signal, and the receiver extracts the observed quantity after receiving the signal. , represent the amplitudes of the transmitted and received signals respectively.

[0037] Step 3.1 Gain compensation method based on position estimation and initial calibration fitting Before parameter estimation, the pilot signal is normalized to remove the signal interference caused by large-scale fading and improve the efficiency of parameter estimation. In order to eliminate the large-scale path fading caused by the distance change between antennas in the magnetic communication channel (i.e., the distance factor The present invention proposes a gain compensation method based on position estimation and initial calibration fitting, which specifically includes the following steps: 1) When the system is initially deployed, record the relative spatial position between the transmitting antenna and the receiving coil And the received signal strength at this time , recorded as the initial power reference point. Set the distance at this time to , the path attenuation factor of this point can be recorded as . 2) During the communication process, use the inertial measurement unit (IMU), wheel speed meter, SLAM system or other positioning methods to obtain Relative position , calculate the current transmit-receive distance .

[0038] 3) The magnetic field strength meets , the path loss effect can be removed by normalization, and the normalized received power is defined as: This indicator is approximately equal to the . Using the normalized Sequences are modeled by time series or probability statistics to extract the The channel fast fading characteristics caused by the

[0039] Step 3.2 Channel fast fading estimation method based on EM algorithm To overcome the uncertainty introduced by noise interference and channel variations, the present invention introduces the EM algorithm for parameter optimization in the estimation phase. Using the previously established channel gain boundary chi-square distribution as a priori, the following posterior estimation model is established: Calculate the expectation through the expectation step (E step), update the parameters through the maximization step (M step), and iteratively update the noise variance and channel scaling factor , including two steps, the parameters to be estimated and After the initial value is set to 0, the iteration of the expectation step and the maximization step begins: The expected step is used to calculate the posterior probability, and the maximum step is used to update the estimated parameters, namely: The estimation process continues to iterate until convergence. The final output channel gain estimate is and noise statistical characteristics, which are used to guide the adaptive gain control and bit error rate compensation strategy at the receiving end to ensure the reliability and stability of the communication system in a dynamic vibration environment.

[0040] Step 4: Transmission rate adjustment According to the estimated parameters, the vehicle transmitter is controlled by instructions to adjust the transmission rate to reduce the bit error rate. As an indicator of the degree of decline, The larger the value is, the more severe the fast fading is. The control basis is: in, The maximum bit rate supported by the system. This maximum bit rate should be designed according to the actual situation of the system. The system updates the transmission rate every 60 seconds. The average value is used as the basis for adjustment. Thus, the needs of the present invention are achieved.

[0041] The accuracy of the algorithm of the present invention is significantly better than other algorithms. A simulation environment was built to compare the method of the present invention (EM algorithm) with the moment estimation method and the baseline method based on the zero noise assumption. The results are as follows Figure 8 As shown. Among them, the evaluation index is the normalized mean square error (NRMSE), and the calculation formula is as follows: As can be seen from the figure, the accuracy of the EM algorithm proposed in the present invention is more than 10 times higher than the baseline performance of moment estimation and estimation based on the zero noise assumption.

[0042] Example 3 The PMAA system mainly consists of a transmitter and a receiver, and supports switching between communication mode and positioning mode. In communication mode, the microcontroller at the transmitter receives the input binary baseband signal and maps it to different motor speeds. Specifically, logic "0" and "1" correspond to frequencies and The microcontroller controls the motor drive voltage to precisely adjust the speed, which in turn drives the permanent magnet to rotate, radiating magnetic signals at the required frequency to achieve frequency shift keying (FSK) modulation. The system defaults to the optimal communication frequency. , the frequency and the transmitting and receiving distance and environmental material parameters (conductivity and magnetic permeability ) and can be dynamically adjusted by the system preset model.

[0043] In positioning mode, the transmitter operates at a fixed frequency. The rotating permanent magnet forms a stable pilot magnetic field for the receiver to perform channel estimation and position information extraction. The receiver is equipped with a three-axis antenna, and each axis coil and matching capacitor form a parallel resonant circuit. The matching capacitor meets To ensure minimum impedance and maximum sensitivity at the target frequency. The received signal is first filtered by a notch filter to remove power frequency interference, and then filtered by a digital limiter to suppress strong interference signals. and The signal passes through two independent second-order IIR bandpass filters, followed by an envelope detector to extract the amplitude. After low-pass filtering and analog-to-digital conversion, it is input into the demodulation unit to recover the original bits. The system also supports reverse communication, allowing the receiver to adjust the local motor speed and send control commands to the transmitter via magnetic signals, forming a closed-loop control loop.

[0044] The transmitter periodically sends a pilot signal with a constant frequency, and the receiver records its received power and with the reference position The received power under Compare and realize channel normalization. Assume that the current position of the vehicle is , the current distance is , according to the static magnetic field model, the normalized channel gain is calculated as: The system will As channel samples, used to estimate channel fluctuation parameters in real time The parameter estimation algorithm is deployed on an embedded processor and uses the maximum likelihood or minimum mean square error criteria to perform optimal fitting based on the known model distribution, thereby achieving robust modeling and dynamic tracking of fast fading characteristics.

[0045] Finally, run the EM algorithm. The fast fading channel parameter estimation method based on the EM algorithm is as follows: Input: Receive signal , transmit signal , threshold Output: Noise variance , signal gain variance 1. Randomly initialize parameters , set the iteration counter 2. While (parameter changes have not converged) do: S1. For each sample , calculate the posterior probability S2. Calculate expectation and S3. Update noise variance S4. Update signal gain variance S5. If the parameter changes , marked as convergence S6. Iteration counter 3. End While 4. Return the final estimate and .

[0046] Get the estimated value through EM algorithm After that, the transmission rate is updated every 60 seconds using a sliding window. The average estimate is taken: As the current vibration intensity. Finally, through the current communication environment, pre-set After obtaining the estimated parameters, the bit rate is set by the estimated parameters . Transmit control instructions to adjust the communication rate.

[0047] Although specific embodiments of the present invention have been described above, those skilled in the art will appreciate that these are merely illustrative and that the scope of the present invention is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, and such changes and modifications are intended to fall within the scope of the present invention.

Claims

1. A method for estimating fast fading parameters of a permanent magnet mechanical antenna based on dynamic vehicle vibration, characterized in that: The following steps are involved: Step 1: Antenna vibration amplitude modeling; Step 2: channel modeling; Step 3: channel parameter estimation; Step 4: Adjust the transmission rate according to the estimated channel parameters.

2. The method for estimating fast fading parameters of a permanent magnet mechanical antenna based on dynamic vehicle vibration according to claim 1, wherein: Step 1 is as follows: First, a communication model of rotating permanent magnets is established to obtain the vehicle communication quality and angle factor. relationship; as follows: The channel gain is expressed as: , consists of two parts: distance factor and angle factor ; The distance factor The effect of distance on gain is defined as: ； Angle Factor The effect of angle on gain is defined as: ； Based on the Gaussian road surface assumption, the vehicle vibration model is established as follows: Vehicle vibration amplitude It has a mean of zero and a variance of The normal distribution of the random variable characterizes the relationship between the antenna center and the monitoring point (radial distance from the antenna center axis). To simplify the subsequent analysis, and Normalized, defined as and .

3. The method for estimating fast fading parameters of a permanent magnet mechanical antenna based on dynamic vehicle vibration according to claim 1, wherein: Step 2 is as follows: Based on the rotating permanent magnet communication model and vehicle vibration model established in step 1, the Gaussian vibration probability distribution of the road surface is converted into the probability distribution of the communication channel gain, which is in the form of fast fading. The angle gain is further proposed. The probability distribution followed is used to describe the fast fading phenomenon of communication when the vehicle is moving; The resulting angular gain The PDF is: The probability distribution function is: 。 4. The method for estimating fast fading parameters of a permanent magnet mechanical antenna based on dynamic vehicle vibration according to claim 1, wherein: Step 3 is as follows: The transmitter periodically transmits a fixed-frequency pilot signal, and the receiver extracts the observed quantity after receiving the signal. , represent the amplitudes of the transmitted and received signals respectively; Step 3.1 Gain compensation method based on position estimation and initial calibration fitting Before parameter estimation, the pilot signal is normalized to remove signal interference caused by large-scale fading and improve the efficiency of parameter estimation; The gain compensation method based on position estimation and initial calibration fitting specifically includes the following steps: 1) When the system is initially deployed, record the relative spatial position between the transmitting antenna and the receiving coil And the received signal strength at this time , recorded as the initial power reference point; set the distance at this time to , the path attenuation factor of this point can be recorded as ; 2) During the communication process, the inertial measurement unit IMU, wheel speed meter, and SLAM system positioning method are used to obtain the Relative position , calculate the current transmit-receive distance ; 3) The magnetic field strength meets , the path loss effect can be removed by normalization, and the normalized received power is defined as: This indicator is approximately equal to the ; Use the normalized Sequences are modeled by time series or probability statistics to extract the The channel fast fading characteristics caused by Step 3.2 Channel fast fading estimation method based on EM algorithm To overcome the uncertainty introduced by noise interference and channel changes, the present invention introduces the EM algorithm to perform parameter optimization in the estimation stage. Using the previously established channel gain boundary chi-square distribution as a priori, the following posterior estimation model is established: Calculate the expectation through the expectation step (E step), update the parameters through the maximization step (M step), and iteratively update the noise variance and channel scaling factor , including two steps, the parameters to be estimated and After the initial value is set to 0, the iteration of the expectation step and the maximization step begins: The expectation step calculates the posterior probability, and the maximization step updates the estimated parameters, that is: The estimation process continues to iterate until convergence; the final output channel gain estimate is and noise statistical characteristics, which are used to guide the adaptive gain control and bit error rate compensation strategy at the receiving end to ensure the reliability and stability of the communication system in a dynamic vibration environment.

5. The method for estimating fast fading parameters of a permanent magnet mechanical antenna based on dynamic vehicle vibration according to claim 4, wherein: The fast fading channel parameter estimation method based on the EM algorithm has the following specific steps: Input: Receive signal , transmit signal , threshold Output: Noise variance , signal gain variance 1) Randomly initialize parameters , set the iteration counter 2) While (parameter changes have not converged) do: S1. For each sample , calculate the posterior probability S2. Calculate expectation and S3. Update noise variance S4. Update signal gain variance S5. If the parameter changes , marked as convergence S6. Iteration counter 3) End While 4) Return the final estimate and .

6. The method for estimating fast fading parameters of a permanent magnet mechanical antenna based on dynamic vehicle vibration according to claim 1, wherein: Step 4 is as follows: According to the estimated parameters, the vehicle transmitter is controlled by instructions to adjust the transmission rate to reduce the bit error rate; As an indicator of the degree of decline, The larger the value is, the more severe the fast fading is. The control basis is: in, The maximum bit rate supported by the system should be designed according to the actual situation of the system. The system updates the transmission rate every 60 seconds. The average value is used as the basis for adjustment.

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