A high-precision field strength monitoring method based on a zero intermediate frequency sliding window algorithm

Abstract

This invention relates to the field of vehicle-to-ground communication field strength monitoring technology, and provides a high-precision field strength monitoring method based on a zero-IF sliding window algorithm. The method includes: acquiring zero-IF baseband monitoring data of the target vehicle-to-ground communication frequency band to form a baseband monitoring dataset; performing zero-IF self-calibration on the baseband monitoring dataset to obtain calibrated baseband data and calibration residual results; constructing a multi-scale candidate sliding window set and calculating the window stability results; determining the effective field strength calculation window based on the window stability results and completing the field strength conversion to obtain the window field strength value; combining the window field strength value, calibration residual results, and baseband monitoring dataset to generate a field strength reliability score, and outputting the vehicle-to-ground communication field strength monitoring results. This invention can suppress the influence of zero-IF reception error on field strength calculation, while taking into account both short-time burst signal preservation and stable background field strength estimation, thus improving the accuracy and reliability of vehicle-to-ground communication field strength monitoring results.

Description

Technical Field

[0001] This invention relates to the field of vehicle-to-ground communication field strength monitoring technology, specifically a high-precision field strength monitoring method based on a zero-IF sliding window algorithm. Background Technology

[0002] When monitoring the field strength of vehicle-to-ground communication within the communication coverage area of ​​a railway line, the high speed of the train and the continuous change in the spatial location of the communication link cause the link power to change rapidly with the train's position, vehicle body obstruction, line structure, and propagation environment. At the same time, the multipath reflections generated by buildings, tunnel walls, elevated structures, and station facilities along the line, changes in the position of the monitoring platform or the state of the antenna, DC bias drift of the zero-IF receiver, and phase-to-amplitude imbalance all contribute to the fact that the in-phase quadrature baseband signal collected by the zero-IF receiver contains both real field strength changes and non-real reception disturbances. This makes it difficult for the field strength monitoring results to stably reflect the actual coverage status of the vehicle-to-ground communication link.

[0003] Current technologies typically employ spectrum analyzers, software-defined radio receivers, or zero-IF receiver modules to sample the target vehicle-to-ground communication frequency band. After down-converting the RF signal into an in-phase quadrature baseband signal, the mean amplitude, root mean square value, peak value, or power spectral density within a fixed-length sliding window are calculated. These values ​​are then combined with antenna coefficients, receiver gain, cable loss, and calibration coefficients to obtain the field strength value. During continuous monitoring, the field strength results are updated at fixed time intervals and compared with preset thresholds to determine if there are link anomalies, external interference, or insufficient coverage.

[0004] However, in monitoring scenarios where vehicle-to-ground communication signals change rapidly with the train's position, the line propagation environment is complex, and reception disturbances are significant, the existing fixed sliding window field strength calculation method cannot simultaneously take into account both short-term link anomaly retention and stable coverage field strength estimation. When the window length is set too long, short-term communication fading, transient interference, or link anomaly peaks will be weakened by the averaging process, causing the true anomaly peaks to be smoothed out. When the window length is set too short, DC bias residue, in-phase orthogonal imbalance residuals, and multipath fading will directly enter the field strength calculation results, causing the field strength curve to produce unrealistic jumps, making it difficult to simultaneously achieve no loss of short-term anomalies and no false alarms in stable coverage. Summary of the Invention

[0005] During continuous monitoring of the field strength of vehicle-to-ground wireless communication, the high-speed operation of trains, changes in track structure, and propagation environments such as tunnels, stations, and elevated structures can cause rapid fluctuations in the field strength of communication links. Existing fixed sliding window methods are difficult to simultaneously retain short-term abnormal peak values ​​of links and obtain stable coverage field strength. At the same time, after adopting multi-window monitoring, the same link anomaly may cross multiple adjacent windows and be repeatedly recorded or output in a segmented manner, making it difficult to accurately determine the start and end time of the anomaly, the peak position, and the duration of the anomaly. This affects subsequent communication coverage assessment, interference identification, link location analysis, and operation and maintenance.

[0006] To at least partially solve the above problems, this invention proposes a high-precision field strength monitoring method based on a zero-IF sliding window algorithm, comprising:

[0007] Acquire zero-IF baseband monitoring data of the target vehicle-to-ground communication frequency band to form a baseband monitoring dataset;

[0008] Zero-IF self-calibration was performed on the baseband monitoring dataset to obtain calibrated baseband data and calibration residual results;

[0009] A multi-scale candidate sliding window set is constructed, and the window stability results of each candidate sliding window are calculated based on the calibrated baseband data and calibration residual results.

[0010] Using the window stability results as a screening criterion, an effective field strength calculation window is determined from the multi-scale candidate sliding window set, and the calibrated baseband data in the effective field strength calculation window is subjected to field strength conversion to obtain the window field strength value.

[0011] Based on the window field strength value, calibration residual results, and baseband monitoring dataset, a field strength confidence level is generated, and the vehicle-to-ground communication field strength monitoring results containing the window field strength value and the field strength confidence level are output.

[0012] As a preferred embodiment, zero-IF self-calibration is performed on the baseband monitoring dataset to obtain calibrated baseband data, including:

[0013] After performing median filtering preprocessing on each frame of the in-phase and quadrature sampling sequences, the time-domain mean is calculated to obtain the in-phase DC bias estimate and the quadrature DC bias estimate. The square root of the ratio of the full-frame power of the de-DC in-phase and quadrature sequences is calculated to obtain the gain imbalance coefficient. The arcsine of the normalized cross-correlation coefficient of the two de-DC sequences is calculated to obtain the phase imbalance angle. A complex baseband sequence is constructed from the two de-DC sequences and a fast Fourier transform is performed. The signal half-axis and the mirror half-axis are determined according to the half-axis of the target signal frequency. The ratio of the power of the signal half-axis to the power of the mirror half-axis is calculated to obtain the image suppression ratio estimate. The original sequence is then subjected to three steps: DC bias subtraction, quadrature channel gain normalization, and phase compensation, to obtain the calibrated baseband data.

[0014] As a preferred implementation, DC bias reduction, positive traffic channel gain normalization, and phase compensation are performed, including:

[0015] Subtract the DC bias estimate from the in-phase sampling sequence and the quadrature sampling sequence respectively to obtain the in-phase de-DC sequence and the quadrature de-DC sequence; divide the quadrature de-DC sequence by the gain imbalance coefficient to obtain the gain-normalized quadrature sequence; subtract the product of the in-phase de-DC sequence and the sine of the phase imbalance angle from the gain-normalized quadrature sequence, and then divide the result by the cosine of the phase imbalance angle to obtain the phase-compensated quadrature sequence; the in-phase de-DC sequence and the phase-compensated quadrature sequence together constitute the calibrated baseband data.

[0016] As a preferred implementation, median filtering preprocessing is performed on each frame of in-phase sampling sequence and orthogonal sampling sequence, including:

[0017] Sliding median filtering is performed on the in-phase and quadrature sampling sequences with a preset window length. The median of the sampled values ​​within each sliding window is used as the filter output of the corresponding sampling point, and the DC bias estimate of the corresponding channel is calculated based on the filter output.

[0018] As a preferred implementation, a multi-scale candidate sliding window set is constructed, including:

[0019] Using the sampling timestamp sequence corresponding to the same receiving frequency point in the baseband monitoring dataset as the time axis reference, the baseband monitoring data under the same receiving frequency point is traversed along the time axis according to the short-time window length and step, and the long-time window length and step, respectively, forming a short-time candidate window subset and a long-time candidate window subset; wherein, the short-time window is used to capture sudden field strength peaks, and the long-time window is used to estimate the stable background field strength; the short-time candidate window subset and the long-time candidate window subset are merged, and each candidate window is marked with the start frame number, the end frame number and the window type identifier to form the multi-scale candidate sliding window set.

[0020] As a preferred implementation, based on the calibrated baseband data and calibration residual results, the window stability results of each candidate sliding window are calculated, including:

[0021] For each candidate window, the standard deviation of the instantaneous amplitude sequence within the window is calculated based on the calibrated baseband data as the amplitude fluctuation index, the ratio of the maximum instantaneous amplitude to the mean as the peak-to-average ratio index, and the difference between the maximum and minimum noise floor estimates for each frame as the noise floor change index. Based on the calibration residual results, the mean absolute value of the residual DC bias for each frame within the window is extracted as the DC bias residual index, the normalized combined value of the residual gain imbalance coefficient and the residual phase imbalance angle is used as the in-phase quadrature imbalance residual index, and the mean residual image rejection ratio is used as the image leakage residual index. Based on the baseband monitoring dataset, the number of gain level switching times within the window is extracted as the automatic gain control change state index, and the maximum change amplitude of the three-axis attitude angle is used as the antenna attitude change index. The above eight indices are organized according to the candidate window identifier to form the window stability result.

[0022] As a preferred implementation, the effective field strength calculation window is determined from the multi-scale candidate sliding window set using the window stability result as a screening criterion, including:

[0023] For short-term candidate windows, the peak field strength is determined by the fact that the average instantaneous amplitude within the window is higher than the preset level threshold of the average instantaneous amplitude of the adjacent long-term candidate windows. When a peak field strength exists and the DC bias residual, in-phase quadrature imbalance residual, and image leakage residual do not exceed the corresponding threshold, it is directly determined as an effective field strength calculation window. When a peak field strength exists but any one of the following exceeds the limit: DC bias residual, in-phase quadrature imbalance residual, image leakage residual, noise floor change, or antenna attitude change, the window is extended frame by frame and the above indicators are recalculated until all indicators reach within the threshold. When all indicators still exceed the threshold, it is determined as an effective field strength calculation window. If there are still indicators exceeding the limit after the extended frame number reaches the preset upper limit, it is marked as an invalid window. For long-term candidate windows, when all eight stability indicators do not exceed the corresponding threshold, it is determined as an effective field strength calculation window; otherwise, it is marked as an invalid window.

[0024] As a preferred embodiment, field strength conversion is performed on the calibrated baseband data in the effective field strength calculation window to obtain the window field strength value, including:

[0025] The instantaneous root mean square amplitude of the calibrated baseband data for all frames within the window is calculated. This is then converted into an analog voltage amplitude by combining the full-scale code value of the analog-to-digital converter with the reference voltage. The analog voltage amplitude is then corrected for gain based on the linear value of the total gain of the receiving link recorded in the automatic gain control status field, resulting in an equivalent input voltage. The antenna coefficient, cable loss compensation coefficient, and calibration correction coefficient are retrieved from the pre-stored calibration data using the receiving frequency as an index. Based on the equivalent input voltage, antenna coefficient, cable loss compensation coefficient, and calibration correction coefficient, the field strength is calculated to obtain the window field strength value.

[0026] As a preferred implementation, a field strength confidence level is generated based on the window field strength value, calibration residual results, and baseband monitoring dataset, including:

[0027] The field strength is calculated by taking the difference between the field strength of the window and that of the adjacent window of the same type, the mean absolute value of the residual DC bias, the normalized combined value of the residual gain imbalance coefficient and the residual phase imbalance angle, the mean residual image rejection ratio, the number of automatic gain control level switching, the antenna attitude change, and the timestamp interval between adjacent effective field strength calculation windows of the same type as inputs. Each of these is mapped to a score in the range of 0 to 1 according to its own preset grading threshold. The above seven scores are then weighted and summed according to preset weights to obtain the field strength confidence level.

[0028] As a preferred embodiment, it further includes:

[0029] All valid field strength calculation windows are sorted by sampling timestamp. Valid field strength calculation windows that are temporally continuous or overlapping are merged into the same monitoring event, provided that the timestamps of adjacent valid field strength calculation windows overlap or the time interval does not exceed a preset merging threshold. For all valid field strength calculation windows within the same monitoring event, the earliest start frame timestamp is used as the event start time, the latest end frame timestamp as the event end time, the maximum value among the window field strength values ​​is used as the event peak field strength, and the weighted average of all window field strength values ​​within the event is used as the event average field strength, with the weight taken from the field strength confidence level corresponding to each window. The weighted average of the field strength confidence levels of all windows within the event is used as the event-level field strength confidence level. The event start time, event end time, event peak field strength, event average field strength, and event-level field strength confidence level are combined to form the event-level field strength monitoring result and output.

[0030] Compared with the prior art, the present invention has the following advantages:

[0031] 1. By acquiring zero-IF baseband monitoring data of the target vehicle-to-ground communication frequency band and performing zero-IF self-calibration on the baseband monitoring dataset, the calibrated baseband data and calibration residual results are obtained. This enables receiver errors such as DC bias, in-phase quadrature imbalance, and image leakage to be identified and suppressed before field strength calculation, avoiding misjudging receiver link drift as a true field strength change and improving the basic accuracy of zero-IF field strength monitoring.

[0032] 2. A multi-scale candidate sliding window set is constructed, and the window stability results of each candidate sliding window are calculated based on the calibrated baseband data and calibration residual results. This makes the window selection no longer dependent on a fixed time length, but rather judged in combination with the signal fluctuation state and the receiver residual state. As a result, abnormal peaks can be preserved when short-term burst signals occur, and stable windows can be used for estimation when the background field strength is stable. This solves the problem that fixed sliding windows cannot simultaneously take into account both short-term anomaly preservation and stable field strength estimation.

[0033] 3. Based on the window field strength value, calibration residual results, and baseband monitoring dataset, generate field strength confidence level and output vehicle-to-ground communication field strength monitoring results that include both the window field strength value and the field strength confidence level. This ensures that the monitoring results include not only the field strength value but also the confidence level of that value, facilitating the subsequent differentiation between real field strength enhancement, receiver error disturbance, and spatial propagation disturbance, thereby improving the reliability of vehicle-to-ground communication radio interference identification, location analysis, and spectrum regulation. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation on the scope of this application.

[0035] Figure 1 This is an exemplary flowchart of the high-precision field strength monitoring method provided in this embodiment of the invention;

[0036] Figure 2 This is a comparison chart of the effects of the high-precision field strength monitoring method provided by the embodiments of the present invention and the prior art, wherein the gray bars represent the prior art and the black bars represent the present invention. Detailed Implementation

[0037] To make the technical means, creative features, objectives, and effects of this invention easier to understand, the invention is further described below with reference to specific embodiments. However, the following embodiments are merely preferred embodiments of this invention and not all of them. Other embodiments obtained by those skilled in the art based on the embodiments described herein without creative effort are all within the protection scope of this invention.

[0038] Example 1:

[0039] To achieve the above objectives, please refer to Figures 1 to 2 This invention provides a high-precision field strength monitoring method based on a zero-IF sliding window algorithm, the method comprising the following steps:

[0040] Baseband data acquisition: Acquire zero-IF baseband monitoring data of the target vehicle-to-ground communication frequency band to form a baseband monitoring dataset;

[0041] Zero-IF self-calibration: Perform zero-IF self-calibration on the baseband monitoring dataset to obtain calibrated baseband data and calibration residual results;

[0042] Window stability assessment: Construct a multi-scale candidate sliding window set, and calculate the window stability result of each candidate sliding window based on the calibrated baseband data and calibration residual results;

[0043] Adaptive window field strength conversion: Using the window stability result as a filtering condition, an effective field strength calculation window is determined from the multi-scale candidate sliding window set, and the calibrated baseband data in the effective field strength calculation window is converted to obtain the window field strength value.

[0044] Confidential Field Strength Output: Based on the window field strength value, calibration residual results, and baseband monitoring dataset, a field strength confidence level is generated, and the vehicle-to-ground communication field strength monitoring results containing the window field strength value and the field strength confidence level are output.

[0045] In this embodiment, the specific content of baseband data acquisition includes:

[0046] A zero-IF receiving architecture is used to acquire signals in the target vehicle-to-ground communication frequency band. The zero-IF receiving architecture refers to a signal processing system in which the receiver's local oscillator frequency is the same as the target receiving frequency, the radio frequency signal is directly down-converted to the zero center frequency after mixing, and two baseband analog signals, in-phase component and quadrature component, are output. Then, the signal is converted from analog to digital to form a discrete sampling sequence.

[0047] During the acquisition process, zero-IF downconversion and analog-to-digital conversion are performed on each preset receiving frequency point within the target vehicle-to-ground communication frequency band. Baseband sampling data of a fixed duration is continuously acquired for each receiving frequency point. The in-phase sampling sequence and quadrature sampling sequence obtained at the same frequency point within the same acquisition time period are stored together in a time-aligned manner to form a frame of baseband raw record for that frequency point. The in-phase sampling sequence is a discrete amplitude sequence obtained by uniformly sampling the in-phase component analog baseband signal at a preset sampling rate by the analog-to-digital converter. The quadrature sampling sequence is a discrete amplitude sequence acquired at the same sampling rate for the quadrature component analog baseband signal under clock control that is strictly synchronized with the in-phase component. In one embodiment, the sampling rate is set to 20MSPS, the receiving frequency points are set one by one in 8MHz increments within the target vehicle-to-ground communication frequency band, and the acquisition duration of each frame is set to 50ms to achieve a balance between time resolution and single-frame signal-to-noise ratio.

[0048] When each frame of raw baseband record is written into the baseband monitoring dataset, an auxiliary status field is simultaneously appended to form a complete data structure with self-describing capabilities. This includes: a sampling timestamp, which records the Coordinated Universal Time (UTC) corresponding to the first sampling point of the current frame, used to align data from different frequencies and different acquisition batches on a unified time axis and to provide an index for defining the timing boundaries of subsequent sliding windows; and a receiving frequency, which records the local oscillator center frequency value corresponding to the acquisition of the current frame, in Hertz, used to distinguish the frequency assignment of each frame of data in multi-frequency scanning scenarios and to determine the antenna gain during field strength conversion. The table index for gain correction coefficient; receiving bandwidth, recording the single-sided bandwidth setting value of the receiver baseband filter at the time of this frame acquisition; automatic gain control status, recording the current gain level identifier and corresponding nominal gain value of the receiver's automatic gain control circuit during this frame acquisition; receiver temperature, recording the junction temperature or case temperature value of the device read by the temperature sensor installed at the key node of the RF front end at the time of this frame acquisition; antenna attitude data, recording the three-dimensional attitude angles of the antenna at the time of this frame acquisition, including pitch angle, roll angle and yaw angle, output in real time by the inertial measurement unit or attitude sensor installed on the antenna carrier;

[0049] The in-phase sampling sequence, quadrature sampling sequence, and six types of auxiliary state fields recorded frame by frame are organized in order of frame number to form the baseband monitoring dataset; the six types of auxiliary state fields include sampling timestamp, receiving frequency, receiving bandwidth, automatic gain control status, receiver temperature, and antenna attitude data.

[0050] In this embodiment, the specific content of zero intermediate frequency self-calibration includes:

[0051] First, median filtering preprocessing is performed on the in-phase and quadrature sampling sequences of each frame in the baseband monitoring dataset. The median filtering preprocessing involves performing sliding median filtering on the in-phase and quadrature sampling sequences of each frame in the baseband monitoring dataset according to a preset window length. The median of the sampled values ​​within each sliding window is used as the filtered output of the corresponding sampling point. In one embodiment, the window length of the median filtering is set to one-tenth of the total number of sampling points in the frame. Further, the time-domain mean is calculated for the filtered sequences. The mean obtained from the in-phase channel is used as the in-phase DC bias estimate, and the mean obtained from the quadrature channel is used as the quadrature DC bias estimate.

[0052] Subtract the DC bias estimate from the in-phase sequence and quadrature sequence respectively, and then calculate the gain imbalance coefficient and phase imbalance angle. Specifically, the gain imbalance coefficient is calculated by summing the squares of the full frame sampling points for both the de-DC quadrature sequence and the in-phase sequence, taking the square root of the ratio of the two, and obtaining the result is the gain imbalance coefficient. The calculation formula is:

[0053] ,

[0054] Where n represents the DC sampling point number, and N represents the total number of sampling points within the frame. This represents the nth DC sampling point in the in-phase sequence. This represents the nth DC sampling point in the orthogonal sequence;

[0055] The phase imbalance angle is calculated by summing the products of corresponding sampling points of the in-phase sequence and the quadrature sequence after DC removal, dividing by the total number of sampling points within the frame, and then dividing by the product of the standard deviations of the two sequences to obtain the normalized cross-correlation coefficient. The arcsine value of this coefficient is the phase imbalance angle. The calculation formula is:

[0056] ,

[0057] in This represents the standard deviation of the DC in-phase sequence. This represents the standard deviation of the DC-orthogonal sequence. Represents the arcsine function;

[0058] When a single strong carrier signal dominates within a frame, the in-phase sampling sequence and the quadrature sampling sequence within the frame are divided into multiple segments in time order. The gain imbalance coefficient and phase imbalance angle of each segment are calculated separately, and the median of the calculation results of each segment is taken as the gain imbalance coefficient and phase imbalance angle of the current frame, so as to reduce the influence of a single strong signal on the amplitude and phase imbalance estimation results.

[0059] The in-phase sequence after DC removal is merged with the quadrature sequence to construct a complex baseband sequence. A Fast Fourier Transform (FFT) is performed on the complex baseband sequence to obtain a frequency domain sequence arranged by frequency index. The frequency value corresponding to each frequency index is determined based on the sampling frequency and the number of FFT points. Frequency points with a value greater than 0 are divided into positive frequency half-axis, and frequency points with a value less than 0 are divided into negative frequency half-axis. DC frequency points with a value of 0 are removed from the power statistics. The signal half-axis and mirror half-axis are determined according to the preset frequency offset direction of the target signal. The power of the signal half-axis and the mirror half-axis are statistically analyzed respectively. The image rejection ratio estimate is calculated based on the ratio of the signal half-axis power to the image half-axis power. The five parameters—in-phase DC bias estimate, quadrature DC bias estimate, gain imbalance coefficient, phase imbalance angle, and image rejection ratio estimate—together constitute the zero-IF calibration parameter set for this frame.

[0060] Using the original in-phase sampling sequence and quadrature sampling sequence as input, the DC bias estimate is first subtracted from the in-phase sequence and quadrature sequence respectively to obtain the in-phase de-DC sequence and the quadrature de-DC sequence. Further, the quadrature de-DC sequence is divided by the gain imbalance coefficient to obtain the gain-normalized quadrature sequence. Further, the product of the in-phase de-DC sequence and the sine of the phase imbalance angle is subtracted from the gain-normalized quadrature sequence, and the result is divided by the cosine of the phase imbalance angle to obtain the phase-compensated quadrature sequence. The in-phase de-DC sequence and the phase-compensated quadrature sequence output after the above operations are the calibrated baseband data.

[0061] Based on the calibrated baseband data, the calibration residual results are calculated. Specifically, the time-domain mean is calculated for each frame of the calibrated in-phase sequence and the calibrated quadrature sequence, and the obtained time-domain mean is used as the residual in-phase DC bias and the residual quadrature DC bias. The sum of squares is calculated for the calibrated in-phase sequence and the calibrated quadrature sequence after removing the residual DC bias, and the residual gain imbalance coefficient is calculated based on the ratio of the two sums of squares. The normalized cross-correlation coefficient is calculated for the calibrated in-phase sequence and the calibrated quadrature sequence after removing the residual DC bias, and the residual phase imbalance angle is calculated based on the normalized cross-correlation coefficient. The residual DC bias is then used to calculate the residual gain imbalance angle. The calibrated in-phase and quadrature sequences after biasing are constructed into a calibrated complex baseband sequence. A fast Fourier transform is performed on the calibrated complex baseband sequence to obtain a calibrated frequency domain sequence. The positive and negative frequency half-axis are determined according to the sampling frequency and the number of transform points, and the power of the positive and negative frequency half-axis is calculated respectively. The residual image rejection ratio is calculated based on the power of the positive and negative frequency half-axis. The residual in-phase DC bias, residual quadrature DC bias, residual gain imbalance coefficient, residual phase imbalance angle, and residual image rejection ratio are correlated according to the frame number to form the calibration residual result.

[0062] In this embodiment, the specific content of the window stability evaluation includes:

[0063] Using the sampling timestamp sequence corresponding to the same receiving frequency in the baseband monitoring dataset as the time axis reference, a multi-scale candidate sliding window set is constructed for each receiving frequency. When multiple receiving frequencies exist, window construction, window stability evaluation, and effective field strength calculation window screening are performed for each receiving frequency. Specifically, in one embodiment, the window length of the short-time window is set to 5 frames, the step is set to 1 frame, and the baseband monitoring data under the receiving frequency is traversed along the time axis corresponding to the same receiving frequency to form a subset of short-time candidate windows; the window length of the long-time window is set to 20 frames, the step is set to 5 frames, and the baseband monitoring data under the receiving frequency is traversed in the same way to form a subset of long-time candidate windows; the subset of short-time candidate windows and the subset of long-time candidate windows are merged to form the multi-scale candidate sliding window set, and each candidate window in the set is uniquely identified by its receiving frequency, start frame number, end frame number, and window type identifier.

[0064] For each candidate window in the multi-scale candidate sliding window set, the calibrated baseband data and calibration residual results within its coverage frame range are extracted sequentially, and the stability index is calculated according to the following eight dimensions: amplitude fluctuation, peak-to-average ratio, noise floor change, DC bias residual, in-phase quadrature imbalance residual, image leakage residual, automatic gain control change state, and antenna attitude change.

[0065] The amplitude fluctuation is calculated by summing the instantaneous amplitude sequence of the calibrated baseband data for each frame within the calculation window. The instantaneous amplitude is the square root of the sum of the squares of the in-phase and quadrature sampled values. The standard deviation is calculated by splicing the instantaneous amplitude sequences of all frames within the window, and this standard deviation is used as the amplitude fluctuation index.

[0066] The peak-to-average power ratio (PAPR) is obtained by taking the maximum value in the instantaneous amplitude sequence of all frames within the window and dividing it by the mean of all instantaneous amplitudes.

[0067] The noise floor variation is achieved by performing a fast Fourier transform on the calibrated baseband data of each frame within the window, taking the average power of the 80% of frequency points with the lowest power in the frequency domain amplitude sequence as the noise floor estimate of that frame, and calculating the difference between the maximum and minimum values ​​of the noise floor estimate sequence of each frame within the window as the noise floor variation index.

[0068] The DC bias residual is obtained by extracting the residual DC bias value of each frame within the window from the calibration residual result, calculating the absolute mean of the residual DC bias sequences of the in-phase channel and the positive traffic channel respectively, and taking the larger of the two values ​​as the DC bias residual index.

[0069] The in-phase quadrature imbalance residual is obtained by extracting the residual gain imbalance coefficient and residual phase imbalance angle of each frame in the window from the calibration residual result. The mean of the absolute value of the deviation from 1 is calculated for the residual gain imbalance coefficient sequence, and the mean of the absolute value is calculated for the residual phase imbalance angle sequence. The two are normalized and then added together as the in-phase quadrature imbalance residual index.

[0070] The image leakage residual is obtained by extracting the residual image suppression ratio of each frame within the window from the calibration residual result, and calculating the mean of the residual image suppression ratio sequence of each frame as the image leakage residual index.

[0071] The automatic gain control change state is determined by extracting the gain level identifier sequence of each frame within the window from the automatic gain control status field of the baseband monitoring dataset, counting the number of gain level switching events within the window, and using the number of switching events as the automatic gain control change state indicator; if an intra-frame switching event is recorded in the automatic gain control status field of a frame within the window, then that frame is counted as one switching event.

[0072] The antenna attitude change is calculated by extracting the pitch angle, roll angle, and yaw angle of each frame within the window from the antenna attitude data field of the baseband monitoring dataset. The difference between the maximum and minimum values ​​within the window is calculated for each of the three attitude angle sequences, and the maximum value among the three differences is taken as the antenna attitude change index. If an attitude change flag is recorded in the antenna attitude data field of any frame, a fixed penalty is superimposed on the attitude change index. In one embodiment, the fixed penalty is set to 10°.

[0073] The stability indicators of the above eight dimensions are organized according to the candidate window identifier to form the window stability result of the candidate window, which serves as the basis for subsequent selection of effective field strength calculation windows.

[0074] In this embodiment, the specific content of the adaptive window field strength conversion includes:

[0075] For each candidate window in the multi-scale candidate sliding window set, each of the eight stability indicators in its window stability results is compared with a preset threshold. Specifically, the amplitude fluctuation threshold is set to 20% of the instantaneous average amplitude within the window; the peak-to-average ratio threshold is set to 6 dB; the noise floor variation threshold is set to 3 dB; the DC bias residual threshold is set to 1% of the full scale; the in-phase quadrature imbalance residual threshold is set to 0.05; the image leakage residual threshold is set to an image rejection ratio of less than 30 dB; the automatic gain control change state threshold is set to no more than one switching time within the window; and the antenna attitude change threshold is set to 5°.

[0076] For each candidate window in the short-time candidate window subset, the presence of a field strength peak within the window is first checked. Specifically, the average instantaneous amplitude of each frame within the window is compared with the average instantaneous amplitude of the adjacent long-time candidate windows. If the average instantaneous amplitude within the short-time window is 6 dB or more higher than the average instantaneous amplitude of the adjacent long-time windows, a field strength peak is determined to exist within the short-time window. If a field strength peak exists, the three calibration residual indices—DC bias residual, in-phase quadrature imbalance residual, and image leakage residual—are further checked to see if they all exceed their corresponding thresholds. If all three calibration residual indices do not exceed the limits, the short-time candidate window is directly determined as an effective field strength calculation window, and a short-time peak window type identifier is added.

[0077] If a peak field strength exists but any one of the following exceeds the limit: calibration residual, noise floor variation, or antenna attitude variation, then the short-time candidate window is extended and filtered. Specifically, based on the starting frame of the short-time candidate window, adjacent frames are included sequentially. After each frame is included, the four indicators of amplitude fluctuation, calibration residual, noise floor variation, and antenna attitude variation are recalculated until all four indicators do not exceed the corresponding threshold or the cumulative number of extended frames reaches three times the length of the short-time window. If all four indicators reach within the threshold during the extension process, the extended window is determined as the effective field strength calculation window and an extended peak window type identifier is added. If there are still four indicators exceeding the limit after the number of extended frames reaches three times the length of the short-time window, then the peak field strength is determined to be discontinuous, the corresponding candidate window is marked as an invalid window, and it is not included in the set of effective field strength calculation windows.

[0078] For each candidate window in the long-term candidate window subset, check whether all eight stability indicators do not exceed the corresponding thresholds; if all do not exceed the thresholds, the long-term candidate window is determined as an effective field strength calculation window and a long-term background window type identifier is added; if any one exceeds the threshold, the long-term candidate window is marked as an invalid window.

[0079] For each effective field strength calculation window determined above, the equivalent input voltage is calculated. The calibrated baseband data of all frames within the window are spliced ​​to form a continuous in-phase sequence and a quadrature sequence. For each sampling point, the sum of the squares of the in-phase and quadrature sampling values ​​is calculated and the square root is taken to obtain the instantaneous amplitude sequence. The root mean square value of the instantaneous amplitude sequence is calculated, that is, the average of the squares of all instantaneous amplitudes is taken and the square root is taken to obtain the root mean square value of the amplitude. The root mean square value of the amplitude is divided by the full-scale digital code value of the analog-to-digital converter and then multiplied by the reference voltage of the analog-to-digital converter to obtain the analog voltage amplitude corresponding to the baseband digital amplitude. The analog voltage amplitude is divided by the linear value of the total gain of the receiving link to obtain the equivalent input voltage. The total gain of the receiving link is directly read from the nominal gain value recorded in the automatic gain control status field of the corresponding frame in the baseband monitoring data set.

[0080] Further, a field strength conversion is performed. Using the receiving frequency corresponding to the effective field strength calculation window as an index, the antenna coefficient corresponding to that frequency point is retrieved from the pre-stored antenna coefficient table. The unit of the antenna coefficient is one-tenth per meter, defined as the reciprocal of the ratio of the antenna open-circuit voltage to the incident field strength, and its value is provided by the antenna factory calibration data. The cable loss corresponding to that frequency point is retrieved from the pre-stored cable loss table and expressed as a linear ratio. The current effective receiver gain is read from the receiver parameter configuration and expressed as a linear ratio. The calibration correction coefficient corresponding to that frequency point is retrieved from the pre-stored calibration correction coefficient table. The calibration correction coefficient, generated by the system-level calibration process, is expressed as a linear ratio and is used to correct system-level integrated errors not covered by the antenna coefficient table and cable loss table. The equivalent input voltage is multiplied by the antenna coefficient, divided by the linear value of the cable loss, divided by the linear value of the receiver gain, and then divided by the linear value of the calibration correction coefficient to obtain the window field strength value in volts per meter. If it needs to be expressed in decibels per microvolt per meter, the above linear field strength value is multiplied by one million, the logarithm to base 10 is taken, and then multiplied by 20 to obtain the window field strength value in decibels per microvolt per meter. The field strength conversion formula E is:

[0081] ,

[0082] in The equivalent input voltage is... The antenna coefficient is defined as the ratio of the incident field strength to the antenna open-circuit voltage, and is obtained by looking up a table at the corresponding receiving frequency from the antenna's factory calibration data. Cable loss is obtained by looking up a table at the corresponding receiving frequency from the calibration data, and represents the power attenuation factor of the signal after transmission through the cable. The receiver gain is read from the receiver parameter configuration file in the current operating state. The calibration correction coefficients are generated by the system-level calibration process and obtained by looking up a table at the corresponding receiving frequency. This is the antenna attitude gain correction factor. It is set to 1 when the antenna attitude change within the window does not exceed 1°. When it exceeds 1°, it is retrieved from the antenna pattern gain correction table using the average pitch angle and average heading angle within the window as indexes.

[0083] Specifically, during calibration, the antenna pattern gain correction table is created by mounting the antenna on a three-axis turntable and scanning the antenna across a two-dimensional grid at each frequency point, using the receiving frequency as the step. The elevation angle scan range is -90° to +90° with a step of 1°, and the yaw angle scan range is 0° to 360° with a step of 1°. The antenna gain is measured for each attitude angle combination, using the nominal pointing antenna gain as the reference value (0° elevation and 0° yaw). The antenna gain for each other attitude angle combination is compared with the reference value to obtain the gain correction factor for that attitude angle combination, which is stored as a linear ratio. Finally, a lookup table with the receiving frequency point, elevation angle deviation, and yaw angle deviation as three-dimensional indexes is formed, which is the antenna pattern gain correction table.

[0084] In use, the difference between the average pitch angle and the nominal pitch angle, and the difference between the average yaw angle and the nominal yaw angle of each frame within the window are used as pitch angle deviation and yaw angle deviation, respectively. Together with the current receiving frequency, they form a three-dimensional lookup table index. When the actual deviation value falls between the grid nodes of the table, the corresponding gain correction factor is calculated using a trilinear interpolation method. The trilinear interpolation is performed linearly along the frequency dimension, pitch angle deviation dimension, and yaw angle deviation dimension, respectively, to reduce the lookup error caused by the limited table step size.

[0085] In this embodiment, the specific content of the reliable field strength output includes:

[0086] For each effective field strength calculation window, a confidence score is calculated across seven dimensions. The window field strength value change component uses the absolute value of the difference between the current effective field strength calculation window's window field strength value and the window field strength value of the immediately preceding effective field strength calculation window of the same type on the time axis. If the difference does not exceed 3... If the difference is within 3, the score for that item is 1; if the difference is within 3, the score for that item is 1. to For distances between each meter, the score decreases linearly from 1 to 0.5 according to the linear proportion of the difference within that interval; for differences exceeding... When the current window is the first valid field strength calculation window on the time axis and there is no preceding window to compare with, the score for this item is 0.8.

[0087] The DC bias residual item is calculated by reading the absolute mean of the residual DC bias of each frame within the current window from the calibration residual results, expressed as a percentage of full scale. If the value does not exceed 0.5%, the score is 1; it decreases linearly to 0.6 when it is between 0.5% and 1%; and it is 0.2 when it exceeds 1%.

[0088] The in-phase orthogonal unbalanced residual item is obtained by reading the mean absolute value of the deviation of the residual gain imbalance coefficient from 1 and the mean absolute value of the residual phase imbalance angle of each frame in the current window from the calibration residual results. The two are compared with their respective preset full score thresholds. In one embodiment, the full score threshold for the deviation of the residual gain imbalance coefficient is set to 0.02 and the full score threshold for the residual phase imbalance angle is set to 1°. When the full score threshold is exceeded, it is linearly deducted according to the excess ratio. The average of the two deduction results is used to obtain the score of the item, with a minimum of 0.2.

[0089] The image leakage residual component is calculated by reading the average residual image suppression ratio of each frame within the current window from the calibration residual results; if the average residual image suppression ratio is not less than 40... If the score is 1, then the score is 1; in 30 Up to 40 It decreases linearly to 0.6 between 30 and 40; below 30 The time score is 0.2;

[0090] The automatic gain control (AGC) change status item is calculated by counting the number of gain level switches within the current window from the AGC status field of the baseband monitoring dataset. A score of 1 is awarded when the number of switches is 0; a score of 0.7 is awarded when the number of switches is 1; a score of 0.4 is awarded when the number of switches is 2; and a score of 0.1 is awarded when the number of switches exceeds 2.

[0091] The antenna attitude change item is calculated by reading the antenna attitude change within the current window from the antenna attitude data field of the baseband monitoring dataset. A score of 1 is given when the attitude change is less than 1°; the score decreases linearly to 0.6 when the change is between 1° and 5°; and the score is 0.3 when the change exceeds 5°. If an attitude change flag is recorded in the antenna attitude data field of any frame within the window, an additional 0.2 is deducted from the above scores, with a minimum of 0.1.

[0092] For the continuity of adjacent windows, the difference between the starting frame timestamp of the current effective field strength calculation window and the ending frame timestamp of the previous effective field strength calculation window of the same type is calculated based on the sampling timestamp. The time difference is in frames. If the time difference does not exceed 2 frames, the score is 1; between 2 and 5 frames, it decreases linearly to 0.7; if it exceeds 5 frames, the score is 0.5; if there is no preceding effective field strength calculation window of the same type, the score for this item is 0.7.

[0093] The scores of the above seven sub-items are weighted and summed according to preset weights. In one embodiment, the weight of the window field strength value change sub-item is 0.25, the weight of the DC bias residual sub-item is 0.10, the weight of the in-phase orthogonal imbalance residual sub-item is 0.10, the weight of the image leakage residual sub-item is 0.10, the weight of the automatic gain control change state sub-item is 0.15, the weight of the antenna attitude change sub-item is 0.15, and the weight of the adjacent window continuity sub-item is 0.15; the sum of the seven weights is 1; the weighted sum result is the field strength confidence level, with a value range of 0 to 1. The larger the value, the higher the confidence level of the corresponding window field strength value;

[0094] Further, anomaly cause labels are generated based on the confidence scores of each sub-item and the window type identifier; when the window field strength value increases and the DC bias residual sub-item score is lower than the preset score threshold, a suspected zero-IF DC bias drift label is generated; when the scores of the in-phase orthogonal imbalance residual sub-item or the image leakage residual sub-item are lower than the preset score threshold, a suspected in-phase orthogonal imbalance or image residue label is generated; when the score of the automatic gain control change state sub-item is lower than the preset score threshold, a suspected automatic gain jump label is generated; when the scores of the antenna attitude change sub-item or the adjacent window continuity sub-item are lower than the preset score threshold, a suspected antenna attitude change or multipath disturbance label is generated; when the window field strength value increases and the corresponding sub-items of calibration residual, automatic gain control change state, and antenna attitude change all meet the confidence conditions, a true field strength enhancement label or a short-term burst interference label is generated; in one embodiment, the score threshold is 0.6;

[0095] The field strength value, field strength confidence level, and anomaly cause label of each effective field strength calculation window are bound according to the window identifier to form a reliable field strength result. All reliable field strength results are arranged in the order of sampling timestamps, and together with the corresponding receiving frequency, window type identifier, and window start and end timestamps, they are organized to form the output of the vehicle-to-ground communication field strength monitoring result.

[0096] like Figure 2 A comparison chart of the effects of a high-precision field strength monitoring method based on a zero-intermediate-frequency sliding window algorithm is presented. The horizontal axis lists the key performance indicators, and the vertical axis represents the exemplified performance scores, ranging from 0 to 100%. The higher the value, the better the performance. The aim is to intuitively demonstrate the expected improvement of the present invention in key capabilities compared to typical existing technologies.

[0097] Example 2:

[0098] In Embodiment 1 of the present invention, short-time burst signals can be preserved through zero intermediate frequency self-calibration, multi-scale candidate sliding window, window stability evaluation and adaptive window field strength conversion, and the receiver link residual, automatic gain change and antenna attitude disturbance participate in the field strength reliability determination.

[0099] However, during continuous monitoring of vehicle-to-ground wireless communication, the same train control communication signal, dispatch communication signal, vehicle-to-ground data communication signal, or short-term interference signal may span multiple adjacent candidate windows and simultaneously trigger the effective output of short-term windows, extended windows, and long-term background windows. If the monitoring results are output separately according to a single effective field strength calculation window, the same abnormal event is easily recorded repeatedly or divided into multiple time-discontinuous field strength segments, making it difficult to accurately determine the start and end time, peak position, and duration of the event in subsequent interference location, abnormal alarm statistics, and spectrum monitoring and handling.

[0100] In this embodiment, all effective field strength calculation windows are sorted from earliest to latest according to their respective start frame timestamps to form a time-series effective window list. For two adjacent effective field strength calculation windows in the time-series effective window list, the difference between the end frame timestamp of the previous window and the start frame timestamp of the next window is calculated. If the time difference does not exceed 3 frames, the two are determined to be continuous in time and marked as mergingable. If the time difference exceeds 3 frames, the two belong to different monitoring events and are marked as event boundaries. The 3-frame merging threshold is set based on the parameter settings of 1 frame for short-term windows and 5 frames for long-term windows in Embodiment 1. Window gaps within 3 frame intervals are normal sampling blanks caused by window stepping and do not represent actual signal interruption.

[0101] The list of effective time-series windows is traversed and merged according to the above merging relationship. Starting from the first window in the list, consecutive windows marked as merging relationships are successively included in the same event buffer until an event boundary marker is encountered. All windows in the event buffer are treated as a monitoring event. After clearing the event buffer, the process is repeated from the next window until the list of effective time-series windows has been traversed. This results in several non-overlapping monitoring events, each of which contains one or more effective field strength calculation windows.

[0102] When a short-term peak window, an extended peak window, and a long-term background window coexist within the same monitoring event, the conflict between window types is handled as follows: the field strength value of the long-term background window reflects the background field strength level before and after the event, does not participate in the calculation of the event's peak field strength, and is only retained as a background reference value; the field strength values ​​of the short-term peak window and the extended peak window are used together in the calculation of the event's peak field strength and the event's average field strength.

[0103] For each monitored event, event-level parameters are extracted. Specifically, the event start time is the earliest start frame timestamp of all valid field strength calculation windows participating in the peak calculation within the event, and the event end time is the latest end frame timestamp of the aforementioned window. The event duration is directly calculated from the difference between the event end time and the event start time. The event peak field strength is the maximum value among all window field strength values ​​participating in the peak calculation within the event, and the start frame timestamp of the window containing the peak is recorded as the peak time. The event average field strength is obtained by weighting the field strength confidence of each window and summing all window field strength values ​​participating in the peak calculation within the event, and then dividing by the sum of the weights. The event background field strength is the average of all long-term background window field strength values ​​within the event. If there is no long-term background window within the event, this item is marked as null. The event-level field strength confidence is calculated by weighting the field strength confidence of each window and summing the field strength confidence of all windows participating in the peak calculation within the event. The weight is the field strength confidence of each window itself, that is, windows with higher field strength confidence have a larger proportion in the event-level confidence calculation.

[0104] The event start time, event end time, event duration, event peak field strength, peak moment, event average field strength, event background field strength, and event-level field strength confidence level are organized according to the monitoring event sequence number. Together with the window field strength value sequence of each effective field strength calculation window within the event, they are output as the event details to form the event-level field strength monitoring results. For monitoring events with an event-level field strength confidence level lower than 0.5, a low confidence flag is added to the output results to prompt the subsequent interference location, abnormal alarm statistics, and spectrum supervision and handling stages to manually review the event results.

[0105] The embodiments of the present invention described above are subject to modification and change of method by those skilled in the art without departing from the embodiments and broader aspects of the present invention. The appended claims are intended to include all such modifications and changes of method that do not depart from the present invention.

Claims

1. A high-precision field strength monitoring method based on a zero intermediate frequency sliding window algorithm, characterized in that, include: Acquire zero-IF baseband monitoring data of the target vehicle-to-ground communication frequency band to form a baseband monitoring dataset; Zero-IF self-calibration was performed on the baseband monitoring dataset to obtain calibrated baseband data and calibration residual results; A multi-scale candidate sliding window set is constructed, and the window stability results of each candidate sliding window are calculated based on the calibrated baseband data and calibration residual results. The construction of the multi-scale candidate sliding window set includes: using the sampling timestamp sequence corresponding to the same receiving frequency point in the baseband monitoring dataset as the time axis reference, sliding along the time axis according to the short-time window length and step, and the long-time window length and step, respectively, to traverse the baseband monitoring data under the same receiving frequency point, forming a short-time candidate window subset and a long-time candidate window subset; wherein, the short-time window is used to capture sudden field strength peaks, and the long-time window is used to estimate the stable background field strength; merging the short-time candidate window subset and the long-time candidate window subset, and marking each candidate window with the start frame number, end frame number, and window type identifier, to form the multi-scale candidate sliding window set; Using the window stability results as a screening criterion, an effective field strength calculation window is determined from the multi-scale candidate sliding window set, and the calibrated baseband data in the effective field strength calculation window is subjected to field strength conversion to obtain the window field strength value. Based on the window field strength value, calibration residual results, and baseband monitoring dataset, a field strength confidence level is generated, and the vehicle-to-ground communication field strength monitoring results containing the window field strength value and the field strength confidence level are output.

2. The high-precision field strength monitoring method of claim 1, wherein, Perform zero-IF self-calibration on the baseband monitoring dataset to obtain calibrated baseband data, including: After performing median filtering preprocessing on each frame of the in-phase and quadrature sampling sequences, the time-domain mean is calculated to obtain the in-phase DC bias estimate and the quadrature DC bias estimate. The square root of the ratio of the full-frame power of the de-DC in-phase and quadrature sequences is calculated to obtain the gain imbalance coefficient. The arcsine of the normalized cross-correlation coefficient of the two de-DC sequences is calculated to obtain the phase imbalance angle. A complex baseband sequence is constructed from the two de-DC sequences and a fast Fourier transform is performed. The signal half-axis and the mirror half-axis are determined according to the half-axis of the target signal frequency. The ratio of the power of the signal half-axis to the power of the mirror half-axis is calculated to obtain the image suppression ratio estimate. The original sequence is then subjected to three steps: DC bias subtraction, quadrature channel gain normalization, and phase compensation, to obtain the calibrated baseband data.

3. The high-precision field strength monitoring method of claim 2, wherein, Perform DC bias reduction, positive traffic channel gain normalization, and phase compensation, including: Subtract the DC bias estimate from the in-phase sampling sequence and the quadrature sampling sequence respectively to obtain the in-phase de-DC sequence and the quadrature de-DC sequence; divide the quadrature de-DC sequence by the gain imbalance coefficient to obtain the gain-normalized quadrature sequence; subtract the product of the in-phase de-DC sequence and the sine of the phase imbalance angle from the gain-normalized quadrature sequence, and then divide the result by the cosine of the phase imbalance angle to obtain the phase-compensated quadrature sequence; the in-phase de-DC sequence and the phase-compensated quadrature sequence together constitute the calibrated baseband data.

4. The high-precision field strength monitoring method of claim 2, wherein, Median filtering preprocessing is performed on each frame's in-phase and quadrature sampling sequences, including: Sliding median filtering is performed on the in-phase and quadrature sampling sequences with a preset window length. The median of the sampled values ​​within each sliding window is used as the filter output of the corresponding sampling point, and the DC bias estimate of the corresponding channel is calculated based on the filter output.

5. The high-precision field strength monitoring method of claim 1, wherein, Based on the calibrated baseband data and calibration residual results, the window stability results for each candidate sliding window are calculated, including: For each candidate window in the multi-scale candidate sliding window set, the standard deviation of the instantaneous amplitude sequence within the window is calculated based on the calibrated baseband data as the amplitude fluctuation index, the ratio of the maximum instantaneous amplitude to the mean as the peak-to-average ratio index, and the difference between the maximum and minimum values ​​of the noise floor estimates for each frame as the noise floor change index. Based on the calibration residual results, the mean absolute value of the residual DC bias for each frame within the window is extracted as the DC bias residual index, the normalized combined value of the residual gain imbalance coefficient and the residual phase imbalance angle is used as the in-phase orthogonal imbalance residual index, and the mean residual image rejection ratio is used as the image leakage residual index. Based on the baseband monitoring dataset, the number of gain level switching times within the window is extracted as the automatic gain control change state index, and the maximum change amplitude of the three-axis attitude angle is used as the antenna attitude change index. The above eight indices are organized according to the candidate window identifier to form the window stability result.

6. The high-precision field strength monitoring method of claim 5, wherein, Using the window stability results as a filtering criterion, an effective field strength calculation window is determined from the multi-scale candidate sliding window set, including: For the short-term candidate windows in the short-term candidate window subset, the field strength peak value is determined by the fact that the average instantaneous amplitude within the window is higher than the preset level threshold of the average instantaneous amplitude of the adjacent long-term candidate windows. When a field strength peak exists and the DC bias residual, in-phase quadrature imbalance residual, and image leakage residual do not exceed the corresponding threshold, it is directly determined as an effective field strength calculation window. When a field strength peak exists but any one of the DC bias residual, in-phase quadrature imbalance residual, image leakage residual, noise floor change, or antenna attitude change exceeds the limit, the window is extended frame by frame and the above indicators are recalculated until all indicators reach within the threshold. When all indicators reach the preset upper limit, it is determined as an effective field strength calculation window. When there are still items exceeding the limit after the extended frame number reaches the preset upper limit, it is marked as an invalid window. For the long-term candidate windows in the long-term candidate window subset, when all eight stability indicators do not exceed the corresponding threshold, it is determined as an effective field strength calculation window; otherwise, it is marked as an invalid window.

7. The high-precision field strength monitoring method of claim 1, wherein, Perform field strength conversion on the calibrated baseband data in the effective field strength calculation window to obtain the window field strength value, including: The instantaneous root mean square amplitude of the calibrated baseband data for all frames within the window is calculated. This is then converted into an analog voltage amplitude by combining the full-scale code value of the analog-to-digital converter with the reference voltage. Based on the automatic gain control status field in the baseband monitoring dataset and the recorded linear value of the total gain of the receiving link, the analog voltage amplitude is corrected to obtain the equivalent input voltage. Using the receiving frequency point of the baseband monitoring dataset as an index, the antenna coefficient, cable loss compensation coefficient, and calibration correction coefficient are retrieved from the pre-stored calibration data. Based on the equivalent input voltage, antenna coefficient, cable loss compensation coefficient, and calibration correction coefficient, the field strength is converted to obtain the window field strength value.

8. The high-precision field strength monitoring method of claim 1, wherein, Based on the aforementioned window field strength value, calibration residual results, and baseband monitoring dataset, a field strength confidence level is generated, including: The field strength is calculated by taking the difference between the field strength of the window and that of the adjacent window of the same type, the mean absolute value of the residual DC bias, the normalized combined value of the residual gain imbalance coefficient and the residual phase imbalance angle, the mean residual image rejection ratio, the number of automatic gain control level switching, the antenna attitude change, and the timestamp interval between adjacent effective field strength calculation windows of the same type as inputs. Each of these is mapped to a score in the range of 0 to 1 according to its own preset grading threshold. The above seven scores are then weighted and summed according to preset weights to obtain the field strength confidence level.

9. The high-precision field strength monitoring method of claim 1, wherein, Also includes: All effective field strength calculation windows are sorted according to the sampling timestamp of the baseband monitoring dataset. Effective field strength calculation windows that are consecutive or overlapping in time are merged into the same monitoring event, based on the condition that the timestamps of adjacent effective field strength calculation windows overlap or the time interval does not exceed the preset merging threshold. For all effective field strength calculation windows within the same monitoring event, the earliest start frame timestamp is used as the event start time, the latest end frame timestamp is used as the event end time, the maximum value of the field strength value in the window is used as the event peak field strength, and the weighted average of the field strength values ​​of all windows within the event is used as the event average field strength. The weight is taken as the field strength confidence level corresponding to each window. The weighted average of the field strength confidence scores of all windows within the event is used as the event-level field strength confidence score. The event start time, event end time, event peak field strength, event average field strength, and event-level field strength confidence scores are combined to form the event-level field strength monitoring results and output them.

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