A wireless signal testing method and system for an unmanned vehicle

Abstract

The application belongs to the technical field of wireless communication network, and discloses a wireless signal test method and system for unmanned vehicles; the method comprises the following steps: synchronously collecting electromagnetic signals inside and outside the vehicle to generate an electromagnetic observation data set; constructing a calibration observation matrix based on vehicle posture, position information, road side reference signals and communication link states, extracting a candidate interference cluster set and constructing a power curve, calculating a Pearson correlation coefficient and a Doppler frequency shift to identify an interference type label; determining the positions of interference sources of each interference cluster according to the corresponding interference source positioning algorithm of the interference type label; combining all the interference source positions, corresponding labels and power curves, and fusing communication link performance indicators and vehicle state information to comprehensively evaluate the communication reliability of the whole vehicle wireless link; the application realizes accurate identification and tracing of wireless link interference of unmanned vehicles, and improves the accuracy and real-time performance of communication reliability evaluation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication network, more particularly, the present application relates to a wireless signal testing method and system for unmanned vehicle. BACKGROUND

[0002] With the rapid development of Internet of Vehicles technology and automatic driving system, higher requirements are put forward for the stability and reliability of wireless communication link during the running of vehicles. Especially in complex electromagnetic environment, such as urban multi-source interference and significant multipath effect scene, the whole vehicle wireless link is easily affected by multi-source interference signals inside and outside the vehicle, resulting in communication performance fluctuation, link interruption and even control failure, which seriously affects the stability and safety of the automatic driving system.

[0003] In the prior art, although some schemes propose to use vehicle-mounted communication modules to combine environmental signals for interference identification and link detection, most of them are based on single signal source or static observation data, lack joint modeling and dynamic processing capability of the electromagnetic environment inside and outside the vehicle, and are difficult to realize the type differentiation and spatial positioning of the interference source. In addition, the existing scheme does not construct a fusion mechanism of "interference feature-link state-vehicle attitude", ignores the difference of communication link demand in vehicle working condition and business scene, is easy to cause misjudgment, and is difficult to support the stable communication demand of unmanned vehicle in variable environment. The above problems limit the applicability and precision level of the existing wireless signal test system in the automatic driving scene.

[0004] Therefore, it is urgent to establish a high-precision and high-reliability wireless signal test method to support dynamic perception, interference identification and link quality evaluation of the whole vehicle communication environment, and guarantee the communication safety and running efficiency of unmanned vehicle in actual road environment. In view of this, the present application proposes a wireless signal testing method and system for unmanned vehicle to solve the above problems. SUMMARY

[0005] In order to overcome the above defects of the prior art and achieve the above purpose, the present application provides the following technical scheme: a wireless signal testing method for unmanned vehicle, comprising:

[0006] Synchronously collecting electromagnetic signals generated or received by electronic units in the vehicle and external electromagnetic signals of the vehicle to generate electromagnetic observation data set;

[0007] Based on the vehicle attitude, position information, road side reference signal and communication link state, a calibration observation matrix is constructed, and a candidate interference cluster set is extracted, and a candidate interference cluster power curve is constructed combined with each candidate interference cluster, the Pearson correlation coefficient and Doppler shift are calculated, and the interference type label including external dynamic interference, internal structural interference and internal random radiation noise is further identified;

[0008] obtaining the position of the interference source corresponding to each candidate interference cluster according to the interference source positioning algorithm based on the interference type label of each candidate interference cluster;

[0009] Based on all the interference source positions, corresponding interference type labels and candidate interference cluster power curves, combined with real-time acquired communication link performance indicators, vehicle attitude and position information, the communication reliability of the whole vehicle wireless link is comprehensively evaluated.

[0010] Further, the communication link performance indicators include average signal-to-noise-plus-interference ratio, reference signal received power, delay jitter and burst packet loss rate; the method for comprehensively evaluating the communication reliability of the whole vehicle wireless link includes:

[0011] The communication link performance indicators, vehicle attitude and position information, all the interference source positions, corresponding interference type labels and candidate interference cluster power curves are input into the communication reliability evaluation model to obtain the corresponding communication reliability score;

[0012] Based on the vehicle attitude and position information, the type of the road on which the vehicle is driving is extracted; based on the communication link performance indicators, the type of the communication link service is extracted;

[0013] Based on the type of the road on which the vehicle is driving and the type of the communication link service, the scene adaptive score threshold is dynamically set;

[0014] It is judged whether the communication reliability score is greater than the scene adaptive score threshold, if the result of the judgment is yes, the communication of the whole vehicle wireless link is reliable; if the result of the judgment is no, the communication of the whole vehicle wireless link is unreliable.

[0015] Further, the method for obtaining the position of the interference source corresponding to each candidate interference cluster according to the interference source positioning algorithm includes:

[0016] For each candidate interference cluster in the candidate interference cluster set, when the interference type label is the dynamic interference outside the vehicle, the position of the corresponding interference source is confirmed for the dynamic interference outside the vehicle; when the interference type label is the structural interference inside the vehicle, the position of the corresponding interference source is confirmed for the structural interference inside the vehicle; when the interference type label is the random radiated noise inside the vehicle, the position of the corresponding interference source is confirmed for the random radiated noise inside the vehicle.

[0017] Further, the method for confirming the position of the corresponding interference source for the dynamic interference outside the vehicle includes:

[0018] Based on the complex baseband sampling vector obtained by the antenna array arranged at the preset position outside the vehicle body, W frames of complex baseband sampling vector samples are extracted within a preset sliding window, and a spatial covariance matrix is constructed; the spatial covariance matrix is subjected to eigenvalue decomposition, and a noise subspace is divided out;

[0019] Based on the center frequency and bandwidth of the candidate interference cluster, an array steering vector sequence is generated by traversing in the range of [δ1°, δ2°] with δ1° as the initial value and a preset angle step; the reciprocal of the projection norm of each array steering vector on the noise subspace is obtained to obtain the corresponding multiple signal classification algorithm pseudo-spectrum value; the angle at which the peak value of the multiple signal classification algorithm pseudo-spectrum value is located is the main path real angle of arrival of the interference signal of the candidate interference cluster;

[0020] The main path real angles of arrival in adjacent three observation time windows are subjected to Kalman filtering smoothing to obtain a smoothed angle of arrival sequence; the smoothed angle of arrival sequence is subjected to least square linear extrapolation in combination with vehicle posture and position information to obtain the interference source position coordinates of the dynamic interference outside the vehicle.

[0021] Further, the method for confirming the corresponding interference source position of the in-vehicle structural interference includes:

[0022] All instantaneous in-band power values of the electromagnetic interference cluster are obtained, and an instantaneous in-band power value set is constructed;

[0023] Based on all instantaneous in-band power value combinations in the instantaneous in-band power value set, an electric field amplitude ratio matrix is constructed;

[0024] It is judged whether the instantaneous in-band power value exceeds a preset instantaneous in-band power value threshold, and if so, the corresponding timestamp is recorded as the first wave arrival time, and all first wave arrival times are constructed as a first wave arrival time set; a first wave arrival time difference matrix is constructed based on the first wave arrival time set;

[0025] According to the electric field amplitude ratio matrix and the three-dimensional coordinates corresponding to each instantaneous in-band power value, a logarithmic transformation method is used to obtain a logarithmic power decay, and a three-dimensional near-field field strength gradient is calculated based on a spatial difference method;

[0026] A power error function is constructed according to the three-dimensional near-field field strength gradient;

[0027] A delay error function is constructed according to the first wave arrival time difference matrix and the sound velocity;

[0028] A comprehensive error function is constructed based on the power error function and the delay error function;

[0029] With the minimization of the comprehensive error as the target, the comprehensive error function is iteratively solved, and when the comprehensive error converges to the minimum, the corresponding spatial coordinates are the interference source position coordinates corresponding to the in-vehicle structural interference.

[0030] Further, the method for confirming the corresponding interference source position of the in-vehicle random radiation noise includes:

[0031] all the instantaneous in-band power values are obtained, and it is judged in turn whether each instantaneous in-band power value exceeds a preset instantaneous in-band power value threshold value, if yes, the corresponding instantaneous in-band power value is added to a candidate abnormal power value set;

[0032] For each candidate abnormal power value in the candidate abnormal power value set, an abnormal space region construction and residual iteration convergence operation are performed to obtain a corresponding candidate interference source position and residual mean square error;

[0033] All candidate interference source positions and residual mean square errors are constructed into a candidate interference source position set, and a candidate interference source position with the minimum residual mean square error is selected from the candidate interference source position set as an interference source position coordinate of the in-vehicle random radiation noise.

[0034] Further, the method for obtaining the candidate interference source position and the residual mean square error comprises:

[0035] S1: constructing an abnormal space region with a three-dimensional coordinate corresponding to the candidate abnormal power value as a spherical center and a preset initial radius, setting a residual mean square error convergence threshold value, a radius decrement and a maximum iteration number;

[0036] S2: estimating all the instantaneous in-band power values in the abnormal space region by a spatial difference algorithm to generate a first instantaneous in-band power value set;

[0037] S3: calculating a residual mean square error based on the candidate abnormal power value and the first instantaneous in-band power value set;

[0038] S4: if the residual mean square error is less than or equal to the residual mean square error convergence threshold value, determining that the three-dimensional coordinate corresponding to the candidate abnormal power value is a candidate interference source position, and terminating the current loop;

[0039] If the residual mean square error is greater than the residual mean square error convergence threshold value, if the current iteration number is less than the maximum iteration number, the spherical radius of the abnormal space region is reduced by the radius decrement to obtain a new abnormal space region, and returning to S2 for execution, if the current iteration number is greater than or equal to the maximum iteration number, the current candidate abnormal power sample is discarded.

[0040] Further, the method for obtaining the candidate interference cluster set comprises:

[0041] Independent component analysis is used on the calibration observation matrix, and the maximum statistical independence is taken as an objective function for iterative solution to obtain K signal components; the short-time Fourier transform is used on the K signal components respectively for spectral density evaluation to obtain energy distribution of the signal components on a time-frequency plane;

[0042] The sparse representation method is used to extract a sparse activation region set corresponding to the energy distribution, and a density clustering algorithm is used to aggregate the sparse activation region set to generate a candidate interference cluster set;

[0043] The method for constructing the candidate interference cluster power curve comprises:

[0044] For each candidate interference cluster in the candidate interference cluster set, the center frequency, frequency width, peak power, envelope power change rate, and modulation structure coefficient of the candidate interference cluster are extracted, and a candidate interference cluster power curve is constructed.

[0045] In each time slice, a frequency domain window is constructed based on the center frequency and the frequency width, the power spectrum density extracted in the frequency domain window is integrated to obtain an integrated power, the integrated power is corrected by applying the modulation structure coefficient to obtain an instantaneous in-band power value, and the instantaneous in-band power value is smoothed and extrapolated by combining the envelope power change rate to obtain a candidate interference cluster power sequence, and the candidate interference cluster power sequence is fitted to construct a candidate interference cluster power curve.

[0046] Further, the method for identifying the interference type label comprises:

[0047] If the Pearson correlation coefficient of the candidate interference cluster is higher than a preset upper limit threshold of the Pearson correlation coefficient, and the Doppler frequency shift and the vehicle radial velocity are in a linear correlation relationship within a preset observation time window, the interference type label is determined as an external dynamic interference;

[0048] If the fluctuation amplitude of the spectral center frequency of the candidate interference cluster is lower than a preset fluctuation amplitude threshold within a preset observation time window, and the deviation between the Doppler frequency shift and zero is within a set difference tolerance range, or the Doppler frequency shift exhibits a periodic oscillation behavior within the preset observation time window, and the Pearson correlation coefficient is lower than a preset lower limit threshold of the Pearson correlation coefficient, the interference type label is determined as an internal structural interference.

[0049] For the candidate interference cluster that does not satisfy the determination conditions of the external dynamic interference and the internal structural interference, the interference type label is determined as internal random radiation noise.

[0050] A wireless signal testing system for an unmanned vehicle, which implements a wireless signal testing method for the unmanned vehicle, comprising:

[0051] A multi-source signal acquisition module is configured to synchronously acquire electromagnetic signals generated or received by electronic units in the vehicle and external electromagnetic signals to generate an electromagnetic observation data set.

[0052] The interference classification and identification module constructs a calibration observation matrix based on vehicle posture, position information, road side reference signals and communication link state, extracts a candidate interference cluster set, constructs a candidate interference cluster power curve combined with each candidate interference cluster, calculates the Pearson correlation coefficient and Doppler shift, and further identifies the interference type label including the outside dynamic interference, the inside structural interference and the inside random radiation noise;

[0053] The interference source positioning module obtains the interference source position corresponding to each candidate interference cluster according to the interference source positioning algorithm according to the interference type label of each candidate interference cluster.

[0054] The signal intelligent diagnosis module combines the real-time acquired communication link performance index, vehicle posture and position information, and comprehensively evaluates the communication reliability of the whole vehicle wireless link based on all the interference source positions and the corresponding interference type label and candidate interference cluster power curve.

[0055] Compared with the prior art, the technical effects and advantages of the wireless signal test method and system of the unmanned vehicle are as follows:

[0056] The application provides a wireless signal test method and system for unmanned vehicles, which has multiple technical advantages such as multi-source data fusion, high-precision interference identification and positioning, intelligent communication reliability evaluation, and effectively improves the wireless communication safety guarantee capability in the automatic driving environment. Specifically, the application constructs a unified electromagnetic observation data set through the synchronous acquisition and structured preprocessing of the inside and outside electromagnetic signals of the vehicle, providing a data basis for subsequent interference analysis; the application constructs a calibration observation matrix and extracts candidate interference clusters combined with vehicle posture and position information, road side reference signals and communication link state, realizes the accurate characteristic expression of the interference cluster through spectral density evaluation, density clustering and power curve modeling methods. In the aspect of interference type identification, the application introduces the dual criteria of Pearson correlation coefficient and Doppler shift, realizes the effective classification of outside dynamic interference, inside structural interference and inside random radiation noise, and significantly enhances the discrimination ability of the system to the complex electromagnetic interference environment.

[0057] Further, the present application designs matched interference source positioning algorithms for different types of interference, wherein the vehicle external dynamic interference is based on the MUSIC algorithm and Kalman filtering, combined with vehicle attitude information to realize angle of arrival tracking and position inversion; the vehicle internal structural interference is based on field strength gradient and propagation delay to construct a power-delay joint error function and solve it by minimization, improving the spatial tracing accuracy of near-field interference; the vehicle internal random radiation noise uses a residual mean square error iterative optimization strategy, starting from the local abnormal energy peak value, and quickly converging to the optimal interference source position. The present application can realize accurate mapping of interference characteristics and physical entities by designing matched interference source positioning algorithms for different types of interference, and has good interference tracing ability and system closed-loop optimization basis.

[0058] On this basis, the signal intelligent diagnosis module further fuses the interference source position, interference type label and power curve characteristics, combines the communication link real-time performance index, vehicle attitude and position information, constructs a high-dimensional fusion feature vector, and scores and evaluates the vehicle communication reliability based on a communication reliability evaluation model. The present application introduces a scene adaptive scoring threshold mechanism, effectively avoiding the misjudgment and omission problem under the fixed threshold, and improving the system's ability to adapt to multiple scenes and multiple business types. The present application finally realizes accurate identification and tracing of wireless link interference of unmanned vehicles, and improves the accuracy and real-time performance of communication reliability evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 It is a wireless signal test system schematic diagram of an unmanned vehicle according to embodiment 1 of the present application;

[0060] Figure 2 It is a wireless signal test method flow chart of an unmanned vehicle according to embodiment 2 of the present application;

[0061] Figure 3 It is a method flow chart for comprehensive evaluation of the communication reliability of the whole vehicle wireless link according to embodiment 1 of the present application;

[0062] Figure 4 It is a method flow chart for confirming the corresponding interference source position for the vehicle internal structural interference according to embodiment 1 of the present application;

[0063] Figure 5 It is a method flow chart for obtaining the candidate interference source position and the residual mean square error according to embodiment 1 of the present application. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present application will be described in detail, clearly and completely below with reference to the drawings in the embodiments of the present application. It should be particularly noted that the specific embodiments described below are only used to better illustrate and describe the technical solutions of the present application, and are intended to enable those skilled in the art to better understand and implement the present application, and should not be understood as limiting the protection scope of the present application. Those skilled in the art can modify, adjust or equivalently replace the present application according to the content disclosed in the present application without departing from the spirit and essence of the present application, and these should be regarded as the protection scope of the present application.

[0065] Embodiment 1:

[0066] Please refer to Figure 1 The embodiment discloses a wireless signal test system of an unmanned vehicle, which comprises a multi-source signal acquisition module, an interference classification and identification module, an interference source positioning module and a signal intelligent diagnosis module. Each module is connected through wired and / or wireless connection to realize data transmission.

[0067] The multi-source signal acquisition module is used for synchronously collecting electromagnetic signals generated or received by electronic units in the vehicle and external electromagnetic signals of the vehicle, and generating an electromagnetic observation data set.

[0068] The acquisition method of the electromagnetic observation data set comprises:

[0069] A broadband digital intermediate frequency receiver configured in the vehicle interior is used to collect electromagnetic signals generated or received by each electronic unit in the vehicle during operation in real time, so as to obtain an in-vehicle electromagnetic signal. The in-vehicle electromagnetic signal includes but is not limited to electromagnetic interference signals radiated or conducted by in-vehicle millimeter wave radars, laser radars, cellular vehicle networking communication units, direct current voltage converters (DC-DC), vehicle computing platforms and in-vehicle cable wiring coupling paths in a working state.

[0070] A road side wireless monitoring substation is deployed outside the vehicle, or corresponding measurement probes are arranged at preset positions outside the vehicle body, so as to collect interference source signals in the electromagnetic environment around the vehicle, and obtain an external electromagnetic signal. The external electromagnetic signal includes but is not limited to base station signals of a cellular network downlink, vehicle-mounted communication signals emitted by other adjacent vehicles, radar pulse interference signals existing in the environment, and background wireless interference signals generated by wireless local area network devices (Wi-Fi) or industrial, scientific and medical frequency band (ISM frequency band) devices. The preset positions outside the vehicle body are, for example, the roof, the bumper and the outer edge of the door.

[0071] It should be noted that the cellular vehicle-to-everything communication unit is abbreviated as C-V2X communication unit, which is used to realize the information interconnection between vehicles and external environment, and supports various communication types such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P) and vehicle-to-network (V2N) based on cellular network. The cellular vehicle-to-everything communication unit includes a radio frequency front end, an electrical baseband processing unit, a protocol stack processing unit, and a data interaction interface with the vehicle control domain, and has multi-band and multi-protocol concurrent access capability. Preferably, in the present application, the cellular vehicle-to-everything communication unit supports the 5G new radio cellular vehicle-to-everything communication standard, and the cellular vehicle-to-everything communication unit can use a high-integration 5G-V2X communication module. The cellular vehicle-to-everything communication unit provides technical support for realizing high-faith wireless channel testing by integrating 5G-V2X communication capability while supporting multi-modal information interaction.

[0072] The preprocessing operations including signal filtering, down-conversion, automatic gain control and timestamp alignment are performed on the in-vehicle electromagnetic signals and the out-of-vehicle electromagnetic signals respectively, and the data is encapsulated based on a unified format to form a structured electromagnetic observation data set. The data set serves as the input basis for subsequent interference source feature separation, propagation path modeling and channel evaluation analysis, and provides high-precision data support for system-level interference modeling and dynamic simulation testing.

[0073] It should be noted that in the process of processing the in-vehicle electromagnetic signals and the out-of-vehicle electromagnetic signals, the above preprocessing operations are used to improve the collection quality of the original signals, enhance the consistency and comparability of the data, and ensure the accuracy of subsequent signal fusion and interference analysis. It should be understood that the technical means of the preprocessing operations are all well-known operation processes in the field of electromagnetic signal collection and processing by those skilled in the art, and the specific implementation manner can be adjusted according to actual application requirements and hardware conditions, which will not affect the implementability and integrity of the overall technical scheme of the present application.

[0074] The interference classification and identification module constructs a calibration observation matrix based on the vehicle attitude, position information, road side reference signal and communication link state, extracts a candidate interference cluster set, calculates the Pearson correlation coefficient and Doppler shift based on the candidate interference cluster power curve constructed by each candidate interference cluster, and further identifies the interference type label including out-of-vehicle dynamic interference, in-vehicle structural interference and in-vehicle random radiation noise.

[0075] The construction method of the calibration observation matrix includes:

[0076] Based on the roll angle, pitch angle, yaw angle collected by the vehicle inertial measurement unit (IMU) and the global navigation satellite system (GNSS) trajectory coordinates, an attitude-position vector is established for each time slice; through interpolation alignment, the attitude-position vector is mapped to the same sampling index as the electromagnetic signal sampling frame to obtain a calibration observation matrix containing three-dimensional information of time sequence, attitude and position.

[0077] The downlink reference signal power transmitted by the road side wireless monitoring substation, the channel quality indicator (CQI) and the link state of the uplink and sidelink of the cellular vehicle networking communication unit are added to the calibration observation matrix after time synchronization.

[0078] The method for obtaining the candidate interference cluster set comprises:

[0079] Independent component analysis is used for the calibration observation matrix, and the K signal components are obtained by iterative solving with the maximum statistical independence as the objective function; the energy distribution of the K signal components on the time-frequency plane is obtained by performing spectral density evaluation on the K signal components based on the short-time Fourier transform;

[0080] The sparse activation region set corresponding to the energy distribution is extracted by using the sparse representation method, and the density clustering algorithm is used for class aggregation of the sparse activation region set to generate the candidate interference cluster set. In the preferred embodiment of the present application, the density clustering algorithm is preferably DBSCAN, and the density clustering algorithm is used for class aggregation of the sparse activation region set, which can judge the similarity according to the bandwidth, duration and energy characteristics, and determine the sparse activation regions with a similarity greater than the corresponding similarity threshold as the same class.

[0081] The method for constructing the candidate interference cluster power curve comprises:

[0082] For each candidate interference cluster in the candidate interference cluster set, the center frequency, frequency width, peak power, envelope power change rate and modulation structure coefficient of the candidate interference cluster are extracted, and the candidate interference cluster power curve is constructed;

[0083] In each time slice, a frequency domain window is constructed based on the center frequency and the frequency width, the integral power is obtained by integrating the power spectral density extracted in the frequency domain window, the modulation structure coefficient is applied to correct the integral power to obtain the instantaneous in-band power value, and the instantaneous in-band power value is smoothed and extrapolated by combining the envelope power change rate to obtain the candidate interference cluster power sequence, and the candidate interference cluster power sequence is fitted to construct the candidate interference cluster power curve.

[0084] Illustratively, the frequency domain window is wherein ZXPL is the center frequency, PK is the frequency width, and β dB PK is defined as the difference between the upper and lower limits when the power spectral density decreases by β decibels, and in the present application, β is set to 3.

[0085] It should be noted that the frequency domain window of the present application can completely cover the main energy of the candidate interference cluster when performing short-time Fourier transform, ensuring that the integral power neither misses the main lobe energy nor introduces candidate adjacent cluster interference, which helps to keep the power curve consistent with the true signal envelope, and provides accurate input for subsequent Pearson correlation calculation. The envelope power change rate refers to the rate of change of the envelope curve of the instantaneous in-band power value of the candidate interference cluster over time within the observation time window, which is used to quantify the dynamic intensity of the signal in amplitude.

[0086] The method of smoothing and extrapolating the instantaneous in-band power value in combination with the envelope power change rate comprises:

[0087] If the instantaneous in-band power value change rate between adjacent time slices is greater than the preset envelope power change rate threshold, it is considered that the power mutation in the adjacent time slice is affected by instantaneous jump, noise spike or non-steady-state frame superposition, and the truncation operation needs to be performed, that is, the instantaneous in-band power value change rate is limited to not higher than the envelope power change rate threshold, and the instantaneous in-band power value of the current time slice is recalculated using linear interpolation to realize smooth transition.

[0088] When the instantaneous in-band power value of a certain time slice exceeds the peak power of the candidate interference cluster, it is determined that there is an abnormal amplitude peak or an unexpected pulse in the time slice, that is, the power is out of limit. For the abnormal amplitude peak or the unexpected pulse to affect the overall power curve trend of the candidate interference cluster, an exponential decay function is constructed with the peak power as the starting value, and exponential decay compensation processing is performed based on the exponential decay function to obtain the modified instantaneous in-band power value, so that the out-of-limit power gradually decreases to a reasonable interval of continuity in the subsequent time slice, thereby ensuring that the power curve of the candidate interference cluster remains physically consistent and mathematically smooth in the entire time domain.

[0089] The exponential decay function is:

[0090]

[0091] Wherein, P corr (t curr ) represents the modified instantaneous in-band power value at time slice t curr , P max is the peak power, and e is a constant; α is the exponential decay coefficient, which determines the speed of the decay process, and the larger the α value, the faster the decay process, which can quickly suppress abnormal peaks. t curr represents the time slice being calculated in the entire compensation section. t start represents the time slice at which the power out-of-limit is first detected, and the exponential decay starts from this time slice and proceeds backward.(t curr -t start) represents the time difference between the current sampling time slice and the abnormal occurrence time slice, the greater the time difference value, the more negative the exponential term of the exponential function, the stronger the attenuation. Guaranteeing the continuous decline in time, avoiding the signal discontinuity and energy abnormal diffusion phenomenon caused by transient power peak.

[0092] It should be noted that the setting of the exponential attenuation coefficient can be set according to the scene, for example, the reference example table of the exponential attenuation coefficient is shown in Table 1.

[0093] Table 1 Reference example table of exponential attenuation coefficient

[0094] Interference cluster type Duration Decay time Exponential decay coefficient Out-of-vehicle V2X downlink frame interference 10 ms 3 ms 0.77 ms-1 High-frequency radar pulse group 5 ms 1.5 ms 1.53 ms-1 In-vehicle DC-DC power supply harmonic interference 40 ms 8 ms 0.29 ms-1

[0095] The calculation method of the Pearson correlation coefficient and the Doppler shift includes:

[0096] The Pearson correlation coefficient is calculated based on the candidate interference cluster power curve and the road side reference power curve, and the Doppler shift is calculated based on the spectral center frequency of the candidate interference cluster, the radial speed of the vehicle in the interference direction and the electromagnetic wave propagation speed.

[0097] The method for calculating the Pearson correlation coefficient based on the candidate interference cluster power curve and the road side reference power curve includes:

[0098]

[0099] Wherein, PRXS is the Pearson correlation coefficient. Both the candidate interference cluster power curve and the road side reference power curve are divided into N discrete samples, that is, the candidate interference cluster power curve is divided into N candidate interference cluster powers, and the road side reference power curve is divided into N road side reference powers. n is the index variable of the summation formula. x n represents the nth candidate interference cluster power, represents the mean value corresponding to the N candidate interference cluster powers. y n represents the nth road side reference power, represents the mean value corresponding to the N candidate interference cluster powers.

[0100] The calculation method of the Doppler shift includes:

[0101]

[0102] Wherein, Δf d (t curr ) represents the Doppler shift at time slice t curr , represents the offset of the actual frequency of the interference signal relative to the center frequency of the candidate interference cluster, with the unit of Hz, and the positive and negative of the Doppler shift represents that the interference source is close to (positive offset) or far away from (negative offset) the vehicle. V r(t curr ) represents the instantaneous speed (radial velocity) of the interference source along the propagation direction at time slice t curr , unit: m / s, V r (t curr ) is calculated by the inner product of the velocity vector of the vehicle and the incident direction vector of the interference. BS represents the propagation speed of electromagnetic wave in free space. ZXPL represents the spectral center frequency of the current candidate interference cluster.

[0103] It should be noted that in the present application, the Pearson correlation coefficient of the candidate interference cluster power curve and the road side reference power curve is used to determine whether the electromagnetic signal interference has an energy coupling relationship with the external communication transmission behavior; the Doppler shift is used to verify whether the electromagnetic signal interference is from a spatial signal source with a relative speed difference. The combination of the Pearson correlation coefficient and the Doppler shift improves the accuracy and reliability of the vehicle exterior interference identification.

[0104] The identification method of the interference type label includes:

[0105] If the Pearson correlation coefficient of the candidate interference cluster is higher than the preset upper limit threshold of the Pearson correlation coefficient, and the Doppler shift and the radial velocity of the vehicle are in a linear correlation relationship within a preset observation time window, the interference type label is determined as vehicle exterior dynamic interference; specifically, the vehicle exterior dynamic interference is from an external communication link or an environmental radar transmission source with a relative motion relationship with the vehicle, and has significant spatial propagation characteristics and energy synchronization characteristics. In the embodiments of the present application, the upper limit threshold of the Pearson correlation coefficient is preferably set to be ≥0.6.

[0106] If the fluctuation amplitude of the spectral center frequency of the candidate interference cluster is lower than the preset fluctuation amplitude threshold within the preset observation time window, and the deviation between the Doppler shift and zero is within the set deviation tolerance range, or the Doppler shift presents a periodic oscillation behavior within the preset observation time window, and the Pearson correlation coefficient is lower than the preset lower limit threshold of the Pearson correlation coefficient, it is indicated that the instantaneous in-band power value change has no significant correlation with the external environmental signal of the vehicle, and the interference type label is determined as vehicle interior structural interference. Exemplarily, the vehicle interior structural interference usually presents stable frequency, periodic fluctuation or fixed modulation behavior, and typical sources include harmonic interference of direct current voltage converter, fixed frequency switching noise of vehicle-mounted computing platform or other internal electromagnetic transmission sources. In the embodiments of the present application, the lower limit threshold of the Pearson correlation coefficient is preferably set to be ≤0.3.

[0107] For the candidate interference cluster that does not meet the vehicle external dynamic interference and vehicle internal structural interference judgment condition, the interference type label is determined as vehicle internal random radiation noise. The vehicle internal random radiation noise usually exhibits irregular, non-periodic, unstable amplitude-frequency characteristics in the frequency spectrum and time domain, and it is difficult to establish a significant corresponding relationship with a specific physical source, which belongs to the form of unstructured noise source in the vehicle environment, and the exemplary sources include broadband stray interference generated in the electromagnetic compatibility (EMC) shielding insufficient area, etc.

[0108] It should be noted that the fluctuation amplitude threshold can be set as the difference between the maximum value and the minimum value of the instantaneous in-band power value. The difference tolerance range can be set to ≤20 kHz.

[0109] The interference source positioning module obtains the corresponding interference source position of each candidate interference cluster according to the interference source positioning algorithm based on the interference type label of each candidate interference cluster.

[0110] The method for obtaining the corresponding interference source position of each candidate interference cluster according to the interference source positioning algorithm comprises:

[0111] For each candidate interference cluster in the candidate interference cluster set, when the interference type label is vehicle external dynamic interference, the corresponding interference source position of the vehicle external dynamic interference is confirmed; when the interference type label is vehicle internal structural interference, the corresponding interference source position of the vehicle internal structural interference is confirmed; when the interference type label is vehicle internal random radiation noise, the corresponding interference source position of the vehicle internal random radiation noise is confirmed.

[0112] The method for confirming the corresponding interference source position of the vehicle external dynamic interference comprises:

[0113] Based on the complex baseband sampling vector obtained by the antenna array arranged at the preset position outside the vehicle body, W frames of complex baseband sampling vector samples are extracted within a preset sliding window, and a spatial covariance matrix is constructed; the spatial covariance matrix is subjected to eigenvalue decomposition, and a noise subspace is divided out;

[0114] Based on the center frequency and bandwidth of the candidate interference cluster, an array steering vector sequence is generated in the range of [δ1°, δ2°] with δ1° as the initial value and according to a preset angle step; the reciprocal of the projection norm of each array steering vector on the noise subspace is obtained, and a corresponding multiple signal classification algorithm pseudo-spectrum value is obtained; the angle at which the peak value of the multiple signal classification algorithm pseudo-spectrum value is located is the real arrival angle of the main path of the interference signal of the candidate interference cluster;

[0115] The real arrival angles in the adjacent three observation time windows are subjected to Kalman filtering smoothing, and a smoothed arrival angle sequence is obtained; the smoothed arrival angle sequence is subjected to least square linear extrapolation in combination with the vehicle posture and position information, and the interference source position coordinates of the vehicle external dynamic interference are obtained.

[0116] Exemplarily, in the present application, [δ1°, δ2°] is preferably set as [-90°, 90°], and the preset angle step in the present application can be set as 0.2°. The array steering vector is a(θ, ZXPL, PK), θ is the estimated main path angle of arrival corresponding to the preset angle step when traversing, ZXPL is the center frequency, and PK is the frequency width. The length of the preset sliding window can be set as 2s or 2ms.

[0117] It should be noted that, in the present application, the method for confirming the position of the corresponding interference source for the outside dynamic interference is the core of the multiple signal classification algorithm, also known as the MUSIC algorithm. The essence of the MUSIC algorithm is a high-resolution angle of arrival estimation algorithm based on the principle of characteristic subspace separation. Specifically, first, the complex baseband sampling vector collected by the antenna array in the sliding time window is estimated for the covariance matrix, and then the signal subspace and the noise subspace are distinguished through eigenvalue decomposition. Since the eigenvectors of the noise subspace are theoretically orthogonal to the array steering vector corresponding to any signal angle of arrival, the noise subspace can be used to construct the multiple signal classification algorithm pseudo-spectrum value. When the array steering vector is scanned with angle as the parameter and projected onto the noise subspace, as long as the scanning angle is consistent with the actual wave direction, the array steering vector will be completely orthogonal to the noise subspace, resulting in a sharp peak value of the multiple signal classification algorithm pseudo-spectrum value at the angle.

[0118] As shown in Figure 4 , the method for confirming the position of the corresponding interference source for the inside structural interference includes:

[0119] All the instantaneous in-band power values of the electromagnetic interference cluster are obtained, and are constructed into an instantaneous in-band power value set;

[0120] The instantaneous in-band power values in the instantaneous in-band power value set are sequentially numbered; the corresponding electric field amplitude ratios are calculated for all the instantaneous in-band power value combinations in the instantaneous in-band power value set, and the electric field amplitude ratios are added to the electric field amplitude ratio matrix with the instantaneous in-band power value combination corresponding number pair as the index position; the electric field amplitude ratio matrix is used to represent the relative field intensity difference distribution.

[0121] determining whether the instantaneous in-band power value exceeds a preset instantaneous in-band power value threshold, and if so, recording the corresponding timestamp as a first-wave arrival time, and constructing all first-wave arrival times as a first-wave arrival time set; numbering the first-wave arrival times in the first-wave arrival time set in order; calculating the corresponding first-wave arrival time difference for all combinations of first-wave arrival times in the first-wave arrival time set, and taking the index position of the first-wave arrival time difference corresponding to the number pair as the index position of the first-wave arrival time difference matrix, and adding the first-wave arrival time difference to the first-wave arrival time difference matrix. The first-wave arrival time difference matrix is used to reflect the propagation time characteristics of the electromagnetic interference. For example, the instantaneous in-band power value threshold can be set by experts in the field according to the mean value of the instantaneous in-band power value and the variance of the instantaneous in-band power value.

[0122] According to the electric field amplitude ratio matrix and the three-dimensional coordinates corresponding to each instantaneous in-band power value, the log transformation method is used to obtain the log power attenuation, and then the three-dimensional near-field field strength gradient is calculated based on the spatial difference method;

[0123] It should be noted that, according to the electric field amplitude ratio information, the relative amplitude relationship is first converted into a log power attenuation expression form that is more consistent with the law of wireless propagation through a log transformation method, thereby avoiding the interference of nonlinear amplification of the original ratio in the numerical scale on the positioning accuracy. Based on the log field strength distribution obtained after the log power attenuation, further combined with the three-dimensional coordinates corresponding to each instantaneous in-band power value, the finite difference method is used to perform difference calculation on the field strength distribution in three-dimensional space to obtain a three-dimensional near-field field strength gradient vector field. The three-dimensional near-field field strength gradient can effectively represent the change trend of the electromagnetic interference field in the vehicle space, especially in the area close to the interference source, the direction of the gradient will be steadily pointed to the spatial direction with the fastest power rise.

[0124] According to the three-dimensional near-field field strength gradient, a power error function is constructed;

[0125] According to the first-wave arrival time difference matrix and the sound speed, a delay error function is constructed;

[0126] Based on the power error function and the delay error function, a comprehensive error function is constructed;

[0127] Taking the minimization of the comprehensive error as the goal, the comprehensive error function is iteratively solved, and when the comprehensive error converges to the minimum, the corresponding spatial coordinates are the coordinates of the interference source corresponding to the structural interference in the vehicle.

[0128] The calculation method of the electric field amplitude ratio includes:

[0129]

[0130] Where, FDB b，grepresents the electric field amplitude ratio of the bth and the gth in-band instantaneous power values in the set of in-band instantaneous power values, the electric field amplitude ratio is used to represent the relative field strength difference, GL b represents the bth in-band instantaneous power value in the set of in-band instantaneous power values, GL g represents the gth in-band instantaneous power value in the set of in-band instantaneous power values. The number of in-band instantaneous power values in the set of in-band instantaneous power values is denoted as Q, and the value ranges of b and g are both 1 to Q.

[0131] The power error function is:

[0132]

[0133] wherein F1(p) represents the power error function, p represents the spatial coordinates of the interference source to be solved, and i and j are both index variables of the summation formula. represents the preset reference log field strength at the i th log power attenuation from p, represents the preset reference log field strength corresponding to the j th log power attenuation from p. represents the actual log power attenuation obtained at the i th log power attenuation, represents the actual log power attenuation obtained at the j th log power attenuation. μ represents the corresponding weight coefficient.

[0134] It should be noted that the construction idea of the power error function is: for any two log power attenuations, the path loss difference between the i th log power attenuation and the j th log power attenuation is obtained in combination with the preset reference log field strength corresponding to the log power attenuation; the log power attenuation difference between the i th log power attenuation and the j th log power attenuation is calculated according to the actually measured log power attenuation; the residual is obtained by comparing the path loss difference with the log power attenuation difference; all the residuals are squared and weighted and summed to form the power error function.

[0135] Exemplarily, The setting method of is: d i represents the spatial distance from p to the i th log power attenuation. SHYZ is a loss factor, which can be set between 2.0 and 3.5. log 10 (·) is a logarithmic function. μ can be set according to the average value of the overall spatial distance, for example, when the average value of the spatial distance is 0.3 meters, μ can be set to 1.87, and when the average value of the spatial distance is 0.5 meters, μ can be set to 0.99.

[0136] The delay error function is:

[0137]

[0138] Where F2(p) is the delay error function, d j (p) represents the spatial distance from p to the j-th logarithmic power decay point. c s This represents the speed of sound, typically 340 m / s. This represents the arrival time difference of the first wave at position (i, j) in the first wave arrival time difference matrix. v is the corresponding weighting coefficient; for example, v can be set based on the inverse square of the overall spatial distance average, such as... This represents the average spatial distance.

[0139] It should be noted that the construction idea of ​​the delay error function is as follows: for any logarithmic power attenuation pair, if the candidate point p correctly describes the location of the interference source, then the theoretical time difference calculated according to "distance difference / speed of sound" is... It should be compared with the arrival time difference of the first wave recorded in the first wave arrival time difference matrix. Highly consistent; and The smaller the difference between the two, the closer the coordinates of the candidate point p are to the actual location of the interference source. Therefore, the squared residuals of all log power attenuation pairs are summed according to their weights to form the delay error function.

[0140] The comprehensive error function is:

[0141] F3(p) = (F1(p)) 2 +(F2(p)) 2 ;

[0142] Wherein, F3(p) is the comprehensive error function. F1(p) reflects the fitting error of the spatial position of the interference source in the electromagnetic intensity field, and F2(p) reflects the fitting error of the spatial position of the interference source in the propagation time field.

[0143] Methods for identifying the location of interference sources for random radiated noise inside a vehicle include:

[0144] The instantaneous in-band power values ​​of all electromagnetic interference clusters are obtained. Each instantaneous in-band power value is then checked to see if it exceeds a preset instantaneous in-band power value threshold. If it does, the corresponding instantaneous in-band power value is added to the candidate abnormal power value set. The instantaneous in-band power value threshold in this method is the same as the instantaneous in-band power value threshold in the method for identifying the location of the corresponding interference source for structural interference inside the vehicle.

[0145] For each candidate abnormal power value in the candidate abnormal power value set, perform abnormal space region construction and residual iterative convergence operations to obtain the corresponding candidate interference source location and residual mean square error.

[0146] The candidate interference source position and the residual mean square error are constructed into a candidate interference source position set; and a candidate interference source position with the minimum residual mean square error is selected from the candidate interference source position set as the interference source position coordinate of the random radiation noise in the vehicle.

[0147] As shown in Figure 5 , the method for obtaining the candidate interference source position and the residual mean square error comprises:

[0148] S1: constructing an abnormal space region with the three-dimensional coordinate corresponding to the candidate abnormal power value as the center of the sphere and with a preset initial radius, setting a residual mean square error convergence threshold, a radius decrement and a maximum iteration number;

[0149] For example, in the preferred embodiment of the present application, the residual mean square error convergence threshold is set to 0.15 dB2, dB2 representing the square of the error in decibels; the radius decrement is set to 0.1 m; and the maximum iteration number is set to 5.

[0150] S2: estimating all the instantaneous in-band power values in the abnormal space region by a spatial difference algorithm, such as inverse distance weighting or Kriging interpolation, to generate a first set of instantaneous in-band power values;

[0151] S3: calculating the residual mean square error based on the candidate abnormal power value and the first set of instantaneous in-band power values;

[0152] S4: if the residual mean square error is less than or equal to the residual mean square error convergence threshold, determining that the three-dimensional coordinate corresponding to the candidate abnormal power value is the candidate interference source position, and terminating the current loop;

[0153] If the residual mean square error is greater than the residual mean square error convergence threshold, and if the current iteration number is less than the maximum iteration number, the radius of the abnormal space region is reduced by the radius decrement to obtain a new abnormal space region, and the process returns to S2 for execution; if the current iteration number is greater than or equal to the maximum iteration number, the current candidate abnormal power sample is discarded.

[0154] The method for calculating the residual mean square error comprises:

[0155]

[0156] Wherein, MSE f is the residual mean square error corresponding to the fth candidate abnormal power value in the candidate abnormal power value set, the total number of candidate abnormal power values in the candidate abnormal power value set is denoted as F, and the value range of f is 1 to F; R f represents the total number of instantaneous in-band power values in the abnormal space region corresponding to the fth candidate abnormal power value, and r is an index variable of the summation formula. represents the rth instantaneous in-band power value in the abnormal spatial region corresponding to the fth candidate abnormal power value. is the fth candidate abnormal power value.

[0157] It should be noted that the interference source positioning module accurately solves the position coordinates of the interference source corresponding to each candidate interference cluster through special positioning algorithms matched for vehicle external dynamic interference, vehicle internal structural interference and vehicle internal random radiation noise; through the position coordinates of the interference source corresponding to the candidate interference cluster, the present application can establish a one-to-one correspondence between the abstract electromagnetic interference features and the specific vehicle structure or external environment entity, realize the physical traceability of the interference cause; at the same time, the positioning accuracy directly determines the convergence speed of the comprehensive error and the judgment accuracy of the communication reliability evaluation model, and then affects the credibility of the whole vehicle wireless link risk evaluation. Therefore, the interference source positioning module not only connects the two core links of interference identification and intelligent diagnosis in the technical process, but also guarantees the accurate attribution and efficient closed-loop optimization of the interference problem of the unmanned vehicle wireless signal test system in the effect.

[0158] The signal intelligent diagnosis module comprehensively evaluates the communication reliability of the whole vehicle wireless link based on all the interference source positions and the corresponding interference type labels and candidate interference cluster power curves, in combination with the real-time acquired communication link performance indicators, vehicle attitude and position information.

[0159] The communication link performance indicators include average signal-to-noise-plus-interference ratio, reference signal received power, delay jitter and burst packet loss rate.

[0160] The average signal-to-noise-plus-interference ratio represents the ratio of the useful signal power to the sum of the interference and noise power in a unit time window in the wireless link, and is one of the core indicators for measuring the link quality. The present application reads the signal-to-noise ratio data reported by the physical layer through the modem or communication chip integrated in the vehicle communication unit in real time, and performs sliding average processing on the signal-to-noise ratio data in a preset time interval to obtain a stable average signal-to-noise-plus-interference ratio. This indicator can reflect the overall degree of interference and noise suffered by the current link.

[0161] The reference signal received power is an important parameter for evaluating the signal coverage strength in a cellular communication system, representing the linear power value of the reference signal received by the terminal, and the cellular communication system is, for example, 5G. The reference signal received power is reported by the vehicle terminal communication unit periodically, and the system acquires it through interface calling. In the interference analysis, the reference signal received power is compared with the candidate interference cluster power curve, which can further analyze the influence degree of interference on effective signal reception.

[0162] Latency jitter refers to the transmission latency fluctuation of consecutive data packets in a communication link, which can reflect the timing stability of the link. Latency jitter is extracted through the transmission layer unit in the communication protocol stack. Specifically, for multiple groups of data packets successfully received within a unit time window, the single packet round-trip time is counted, a latency sequence is constructed, and the latency jitter is calculated.

[0163] The burst packet loss rate is used to measure the frequency of consecutive data packet loss in a unit of time or a unit of data flow, which is a direct reflection of the stability and reliability of the link. In this application, the burst packet loss rate is obtained by analyzing the data transmission and reception records of the link layer or the transmission layer. The system counts the number of packet losses based on a sliding time window and calculates the proportion of consecutive packet losses to obtain the burst packet loss rate.

[0164] As shown in Figure 3 , the method for comprehensively evaluating the communication reliability of the whole vehicle wireless link includes:

[0165] Inputting the communication link performance indicators, vehicle attitude and position information, positions of all interference sources, corresponding interference type labels, and candidate interference cluster power curves into a communication reliability evaluation model to obtain the corresponding communication reliability score.

[0166] Based on the vehicle attitude and position information, the type of the road on which the vehicle is driving is extracted. Based on the communication link performance indicators, the type of the communication link service is extracted. For example, the type of the road on which the vehicle is driving is urban, highway, and rural. The type of the communication link service is high throughput and low latency. The type of the road on which the vehicle is driving can be represented by a numerical value, for example, urban is represented by 1, highway is represented by 2, and rural is represented by 3. The type of the communication link service can also be represented by a numerical value, for example, high throughput is represented by 1, and low latency is represented by 2.

[0167] Based on the type of the road on which the vehicle is driving and the type of the communication link service, a scene-adaptive score threshold is dynamically set.

[0168] The method for setting the scene-adaptive score threshold includes:

[0169]

[0170] wherein PFYZ dyn is the scene-adaptive score threshold, PFYZ base is the initial value of the score threshold, DLLX is the numerical value corresponding to the type of the road on which the vehicle is driving, and YWLX is the numerical value corresponding to the type of the communication link service. and are the corresponding weight coefficients. For example, in this application, PFYZ can be set to 2, and PFYZ is set to 3. The score threshold value is initially set to 60. The value range of the communication reliability score is [0, 100].

[0171] In actual scenarios, the vehicle scene (city, highway, and countryside), the communication link service type (high throughput / low latency), and the interference intensity distribution are often different, and the use of a fixed threshold value is prone to cause “misjudgment” or “omission”.

[0172] It is determined whether the communication reliability score is greater than the scene-adaptive score threshold value. If the determination result is yes, the whole vehicle wireless link communication is reliable. If the determination result is no, the whole vehicle wireless link communication is unreliable.

[0173] The training method of the communication reliability evaluation model comprises the following steps:

[0174] A communication reliability evaluation dataset is constructed in advance. The communication reliability evaluation dataset comprises PG group communication reliability evaluation data and communication reliability scores corresponding to the PG group communication reliability evaluation data, PG being a positive integer. The communication reliability evaluation data comprises communication link performance indicators, vehicle attitude and position information, interference source positions, corresponding interference type labels, and candidate interference cluster power curves. The communication reliability evaluation dataset is divided into a training set and a verification set. The training set is used for communication reliability evaluation model parameter learning, and the verification set is used for real-time monitoring of the generalization performance and overfitting degree of the communication reliability evaluation model.

[0175] A node output type graph neural network architecture is adopted as the communication reliability evaluation model. The communication reliability evaluation data is standardized and vectorized and then input into the node output type graph neural network architecture. The node output type graph neural network architecture comprises an input layer, hidden layers, and an output layer. Each hidden layer uses a nonlinear activation function to extract features, and the output layer uses a Sigmoid activation function to obtain a probability distribution corresponding to each communication reliability score. The communication reliability score corresponding to the maximum probability is finally taken as the prediction result of the communication reliability evaluation model. In the training process, a cross-entropy loss function is used as the optimization objective, a gradient descent type optimization algorithm is used to update the network weights, and an early stopping strategy is set: when the prediction accuracy on the verification set reaches or exceeds a preset threshold value, it is determined that the communication reliability evaluation model has converged and the training is terminated.

[0176] It should be noted that the signal intelligent diagnosis module converges all interference source position coordinates, corresponding interference type labels and candidate interference cluster power curves, simultaneously accesses vehicle real-time communication link performance indicators and vehicle attitude and position information, and constructs a high-dimensional feature vector describing the "interference environment-link state-vehicle working condition". In combination with the pre-trained communication reliability evaluation model, the feature vector is inferred to obtain the communication reliability comprehensive evaluation result of the whole vehicle wireless link, which significantly improves the communication safety redundancy and operation reliability of the unmanned vehicle in the complex electromagnetic environment, thereby fully embodying the intelligent, real-time and engineering applicability of the technical scheme of the application.

[0177] Embodiment 2:

[0178] Please refer to Figure 2 The embodiment provides a wireless signal test method of an unmanned vehicle, which comprises the following steps of:

[0179] Synchronously collecting electromagnetic signals generated or received by electronic units in the vehicle and external electromagnetic signals of the vehicle to generate an electromagnetic observation data set;

[0180] Based on the vehicle attitude, position information, road side reference signal and communication link state, a calibration observation matrix is constructed, and a candidate interference cluster set is extracted, and a candidate interference cluster power curve constructed by each candidate interference cluster is combined to calculate a Pearson correlation coefficient and a Doppler shift, and further to identify interference type labels including external dynamic interference, internal structural interference and internal random radiation noise;

[0181] According to the interference source positioning algorithm, the interference source positions corresponding to each candidate interference cluster are obtained according to the interference type labels of each candidate interference cluster;

[0182] Based on all the interference source positions and corresponding interference type labels and candidate interference cluster power curves, in combination with the real-time acquired communication link performance indicators, vehicle attitude and position information, the communication reliability of the whole vehicle wireless link is comprehensively evaluated.

[0183] The above merely describes specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0184] Finally: the above merely describes preferred embodiments of the present application and is not used to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for testing wireless signals of an unmanned vehicle, characterized in that, include: Electromagnetic signals generated or received by electronic units inside the vehicle, as well as electromagnetic signals from outside the vehicle, are collected synchronously to generate an electromagnetic observation dataset. Based on vehicle attitude, position information, roadside reference signals and communication link status, a calibration observation matrix is ​​constructed, and a candidate interference cluster set is extracted. Combined with the candidate interference cluster power curve constructed for each candidate interference cluster, the Pearson correlation coefficient and Doppler frequency shift are calculated to further identify interference type labels including external dynamic interference, in-vehicle structural interference and in-vehicle random radiated noise. For each candidate interference cluster, the interference type label is used to obtain the location of the interference source corresponding to each candidate interference cluster based on the interference source localization algorithm; Based on the locations of all interference sources, their corresponding interference type labels, and the power curves of candidate interference clusters, combined with real-time acquired communication link performance indicators, vehicle attitude, and location information, the communication reliability of the vehicle's wireless link is comprehensively evaluated. The communication link performance indicators include average signal-to-noise ratio plus interference, reference signal received power, delay jitter, and burst packet loss rate. Methods for comprehensively evaluating the communication reliability of the vehicle's wireless link include: The communication link performance indicators, vehicle attitude and location information, the locations of all interference sources, and the corresponding interference type labels and candidate interference cluster power curves are input into the communication reliability assessment model to obtain the corresponding communication reliability score. Vehicle road type is extracted based on vehicle attitude and location information; communication link service type is extracted based on communication link performance indicators. Dynamically set scenario-adaptive scoring thresholds based on vehicle road type and communication link service type; Determine whether the communication reliability score is greater than the scenario adaptive scoring threshold. If the result is yes, the vehicle wireless link communication is reliable; if the result is no, the vehicle wireless link communication is unreliable.

2. The wireless signal testing method for an unmanned vehicle according to claim 1, characterized in that, Methods for obtaining the location of the interference source corresponding to each candidate interference cluster based on the interference source localization algorithm include: For each candidate interference cluster in the candidate interference cluster set, when the interference type label is external dynamic interference, the location of the corresponding interference source is identified for external dynamic interference; when the interference type label is internal structural interference, the location of the corresponding interference source is identified for internal structural interference; when the interference type label is internal random radiated noise, the location of the corresponding interference source is identified for internal random radiated noise.

3. The wireless signal testing method for an unmanned vehicle according to claim 2, characterized in that, Methods for identifying the location of external dynamic interference sources include: Based on the complex baseband sampling vectors obtained by the antenna arrays arranged at preset positions outside the vehicle body, W frames of complex baseband sampling vector samples are extracted within a preset sliding window, and a spatial covariance matrix is ​​constructed; the spatial covariance matrix is ​​decomposed by eigenvalues ​​to divide the noise subspace. Based on the center frequency and bandwidth of the candidate interference clusters, Within the range Using the initial value, the array steering vector sequence is generated by traversing according to the preset angle step size; the reciprocal of the projection norm of each array steering vector on the noise subspace is taken to obtain the corresponding pseudospectral value of the multi-signal classification algorithm; the angle where the peak value of the pseudospectral value of the multi-signal classification algorithm is located is the true angle of arrival of the principal path of the interference signal of the candidate interference cluster. The true angles of arrival of the principal diameter within three adjacent observation time windows are smoothed using Kalman filtering to obtain a smoothed angles of arrival sequence. The smoothed angles of arrival sequence is then extrapolated using least squares linear extrapolation based on vehicle attitude and position information to obtain the coordinates of the location of the interference source of the external dynamic interference.

4. The wireless signal testing method for an unmanned vehicle according to claim 2, characterized in that, Methods for identifying the location of structural interference sources inside a vehicle include: Obtain all instantaneous in-band power values ​​of the electromagnetic interference cluster and construct a set of instantaneous in-band power values; The electric field amplitude ratio matrix is ​​constructed based on all combinations of instantaneous power values ​​in the instantaneous power value set; Determine whether the instantaneous power value exceeds the preset instantaneous power value threshold. If it does, record the corresponding timestamp as the first wave arrival time and construct a set of all first wave arrival times. Construct a first wave arrival time difference matrix based on the first wave arrival time set. Based on the electric field amplitude ratio matrix and the three-dimensional coordinates corresponding to the power values ​​in each instantaneous band, the logarithmic power attenuation is obtained by the logarithmic transformation method, and the three-dimensional near-field field strength gradient is calculated based on the spatial difference method. Construct a power error function based on the three-dimensional near-field intensity gradient; A delay error function is constructed based on the first wave arrival time difference matrix and the speed of sound. A comprehensive error function is constructed based on the power error function and the delay error function; With the goal of minimizing the overall error, the overall error function is solved iteratively. When the overall error converges to the minimum, the corresponding spatial coordinates are the coordinates of the location of the interference source corresponding to the structural interference inside the vehicle.

5. The wireless signal testing method for an unmanned vehicle according to claim 2, characterized in that, Methods for identifying the location of interference sources for random radiated noise inside a vehicle include: Obtain all instantaneous in-band power values ​​of the electromagnetic interference cluster, and sequentially determine whether each instantaneous in-band power value exceeds the preset instantaneous in-band power value threshold. If it exceeds, add the corresponding instantaneous in-band power value to the candidate abnormal power value set. For each candidate abnormal power value in the candidate abnormal power value set, perform abnormal space region construction and residual iterative convergence operations to obtain the corresponding candidate interference source location and residual mean square error. All candidate interference source locations and residual mean square errors are used to construct a candidate interference source location set; the candidate interference source location with the smallest residual mean square error is selected from the candidate interference source location set as the interference source location coordinates of random radiated noise inside the vehicle.

6. The wireless signal testing method for an unmanned vehicle according to claim 5, characterized in that, Methods for obtaining candidate interference source locations and residual mean square errors include: S1: Construct an anomaly space region with the three-dimensional coordinates corresponding to the candidate anomaly power value as the center of the sphere and a preset initial radius, and set the residual mean square error convergence threshold, radius reduction amount and maximum number of iterations; S2: Estimate all instantaneous in-band power values ​​within the abnormal spatial region using the spatial difference algorithm, and generate the first instantaneous in-band power value set; S3: The residual mean square error is calculated based on the candidate anomaly power values ​​and the set of power values ​​within the first instant in the band; S4: If the residual mean square error is less than or equal to the residual mean square error convergence threshold, then the three-dimensional coordinates corresponding to the candidate abnormal power value are determined as the location of the candidate interference source, and the current loop is terminated. If the residual mean square error is greater than the residual mean square error convergence threshold; if the current iteration number is less than the maximum iteration number, then reduce the radius of the sphere in the abnormal space region by the radius reduction amount to obtain a new abnormal space region, and return to S2 for execution; if the current iteration number is greater than or equal to the maximum iteration number, then discard the current candidate abnormal power sample.

7. The wireless signal testing method for an unmanned vehicle according to claim 1, characterized in that, Methods for obtaining the candidate interference cluster set include: Independent component analysis is used on the calibration observation matrix, and the objective function is to maximize statistical independence to obtain K signal components. The spectral density of the K signal components is evaluated based on short-time Fourier transform to obtain the energy distribution of the signal components in the time-frequency plane. Using sparse representation methods, a set of sparse activation regions corresponding to energy distributions is extracted, and a density clustering algorithm is used to aggregate the sparse activation regions into categories to generate a set of candidate interference clusters. Methods for constructing candidate interference cluster power curves include: For each candidate interference cluster in the candidate interference cluster set, the center frequency, bandwidth, peak power, envelope power change rate and modulation structure coefficient of the candidate interference cluster are extracted, and the power curve of the candidate interference cluster is constructed. Within each time slice, a frequency domain window is constructed based on the center frequency and bandwidth. The power spectral density extracted within this frequency domain window is integrated to obtain the integrated power. The integrated power is corrected by applying modulation structure coefficients to obtain the instantaneous in-band power value. The instantaneous in-band power value is then smoothed and extrapolated by combining the envelope power change rate to obtain the candidate interference cluster power sequence. The candidate interference cluster power sequence is then fitted to construct the candidate interference cluster power curve.

8. The wireless signal testing method for an unmanned vehicle according to claim 1, characterized in that, Methods for identifying interference type labels include: If the Pearson correlation coefficient of the candidate interference cluster is higher than the preset upper limit threshold of the Pearson correlation coefficient, and the Doppler frequency shift and the vehicle radial velocity are linearly correlated within the preset observation time window, then the interference type label is determined to be external dynamic interference. If the center frequency of the spectrum of the candidate interference cluster fluctuates less than the preset fluctuation threshold within the preset observation time window, and the deviation between the Doppler frequency shift and zero is within the set difference tolerance range, or the Doppler frequency shift exhibits periodic oscillation behavior within the preset observation time window, and the Pearson correlation coefficient is lower than the preset lower limit threshold of the Pearson correlation coefficient, then the interference type label will be determined as in-vehicle structural interference. For candidate interference clusters that do not meet the criteria for determining external dynamic interference and internal structural interference, the interference type label is determined to be internal random radiated noise.

9. A wireless signal testing system for an unmanned vehicle, used to implement the wireless signal testing method for an unmanned vehicle as described in any one of claims 1-8, characterized in that, include: The multi-source signal acquisition module is used to synchronously acquire electromagnetic signals generated or received by the electronic units inside the vehicle, as well as electromagnetic signals from outside the vehicle, to generate an electromagnetic observation dataset. The interference classification and identification module constructs a calibration observation matrix based on vehicle attitude, location information, roadside reference signals and communication link status, and extracts a set of candidate interference clusters. Combining the power curves of each candidate interference cluster, it calculates the Pearson correlation coefficient and Doppler frequency shift, and further identifies interference type labels including external dynamic interference, in-vehicle structural interference and in-vehicle random radiated noise. The interference source localization module, based on the interference type label of each candidate interference cluster, obtains the location of the interference source corresponding to each candidate interference cluster according to the interference source localization algorithm; The intelligent signal diagnostic module comprehensively evaluates the communication reliability of the vehicle's wireless link based on the locations of all interference sources, their corresponding interference type labels, and the power curves of candidate interference clusters, combined with real-time acquired communication link performance indicators, vehicle attitude, and location information.

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