IMU-assisted navigation anti-interference method

Abstract

The present application relates to an IMU-assisted navigation anti-interference method. The method includes: processing the collected data to obtain a synchronous data stream; adding key sampling points to the compensated synchronous data stream to form a supplementary data stream; calculating the supplementary data stream according to the calculation parameters to obtain a compensation signal; performing IMU data acquisition, validity analysis, calibration processing and motion information extraction on the synchronous data stream to obtain carrier motion information; extracting time-frequency features for analysis to obtain signal spectrum features, performing abnormal feature identification, and comparing and analyzing the identification results with the carrier motion information to obtain interference features; performing parameter processing on the compensation signal with anti-interference parameters to obtain an anti-interference signal; performing quality index analysis on the anti-interference signal and obtaining signal enhancement features through feature enhancement, performing time-space domain reconstruction to establish a corresponding relationship with the carrier motion information, and outputting an anti-interference navigation signal through regional fusion. Effectively improves the anti-interference performance in complex environments.

Description

Technical Field

[0001] The present application relates to the field of navigation technology, and in particular to an IMU-assisted navigation anti-interference method. Background Art

[0002] As the application of navigation systems in complex environments continues to expand, traditional anti-interference technologies that rely on antenna arrays or single signal processing algorithms can no longer meet actual needs. Although IMU (Inertial Measurement Unit) has begun to be introduced in existing technologies as an auxiliary means of navigation systems, it is mainly used as a backup solution when the signal is lost. It fails to give full play to the auxiliary role of IMU data in the anti-interference process and lacks deep integration with anti-interference algorithms.

[0003] In practical applications, the characteristics of interference sources and the motion state of the carrier are often coupled with each other, and traditional technologies are difficult to accurately distinguish between interference effects and motion effects. Especially when the carrier moves rapidly or vibrates violently, the existing anti-interference algorithms are prone to misjudge normal motion effects as interference, resulting in reduced anti-interference performance. At the same time, the existing IMU-assisted anti-interference solutions have the problems of high computational complexity and poor real-time performance, and lack an effective compensation mechanism for the IMU's own errors, making it difficult to ensure stable performance in complex environments.

[0004] In view of this, how to achieve deep fusion of navigation signals and IMU data, establish accurate correspondence between motion characteristics and interference characteristics, and provide reliable anti-interference performance while ensuring real-time processing capabilities has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] Based on this, it is necessary to provide an IMU-assisted navigation anti-interference method that can improve navigation anti-interference performance in response to the above technical problems.

[0006] A navigation anti-interference method based on IMU assistance includes the following steps:

[0007] Collect navigation signals and IMU data, and perform time domain sampling and time synchronization processing on the navigation signals and IMU data to obtain synchronized data streams;

[0008] Performing periodic compensation processing on the synchronous data stream, adding key sampling points in the compensated synchronous data stream, and forming a supplementary data stream through compensation calculation;

[0009] The supplementary data stream is divided into weight levels according to data characteristics, calculation parameters are allocated according to the weight levels, and the supplementary data stream is operated according to the calculation parameters to obtain a compensation signal;

[0010] Perform IMU data acquisition and validity analysis on the synchronous data stream, and obtain the carrier motion information through calibration processing and motion information extraction;

[0011] Performing time-frequency domain conversion on the compensation signal, extracting time-frequency features from the converted compensation signal, and performing feature analysis on the time-frequency features to obtain signal spectrum features;

[0012] Identify abnormal features of signal spectrum characteristics, compare the identification results with carrier motion information, and obtain interference features through correlation analysis;

[0013] Determine the anti-interference parameter according to the interference characteristics, and perform parameter processing on the compensation signal using the anti-interference parameter to obtain the anti-interference signal;

[0014] Perform quality index analysis on the anti-interference signal, extract features based on the analysis results, and obtain signal enhancement features through feature enhancement;

[0015] The signal enhancement features are reconstructed in the spatiotemporal domain, a correspondence is established between the reconstructed signal enhancement features and the carrier motion information, and the anti-interference navigation signal is output through regional fusion.

[0016] In one embodiment, the steps of collecting navigation signals and IMU data, and performing time domain sampling and time synchronization processing on the navigation signals and IMU data to obtain a synchronized data stream include the following steps:

[0017] The navigation signal is coherently integrated once every first preset time interval by using segmented coherent integration to obtain in-phase data and orthogonal data, and the IMU data is sampled once every second preset time interval by using a fixed frequency equal interval sampling strategy to obtain IMU sampling data, so as to complete the time domain sampling of the navigation signal and IMU data;

[0018] Generate corresponding timestamps for the data sampling points of the navigation signal and IMU data. Based on the sampling frequency difference between the in-phase data and the orthogonal data, use the cubic spline interpolation algorithm to resample the IMU sampled data to obtain the IMU resampled data, so as to complete the time synchronization processing of the navigation signal and IMU data.

[0019] The timestamp, in-phase data, orthogonal data and IMU re-sampled data are encapsulated to obtain a synchronized data stream.

[0020] In one embodiment, the steps of performing periodic compensation processing on the synchronous data stream, adding key sampling points in the compensated synchronous data stream, and forming a supplementary data stream through compensation calculation include the steps of:

[0021] Taking the timestamp in the synchronous data stream as a benchmark, a sliding time window is used to analyze the time interval between adjacent data frames in the synchronous data, and the time correction value of each data frame in the synchronous data is obtained;

[0022] According to the time correction amount, interpolation processing is performed on the in-phase data, orthogonal data and IMU re-collected data in the synchronous data;

[0023] Key sampling points are added to the interpolation processing results, and data compensation calculations are performed on the key sampling points. The information of the key sampling points, the compensation calculation results and the synchronous data stream are organized into a supplementary data stream; the information of the key sampling points includes the key sampling point type flag and the key sampling point description field; the compensation calculation results include the compensation parameter field.

[0024] In one embodiment, the steps of dividing the supplementary data stream into weight levels according to data features, allocating calculation parameters according to the weight levels, and calculating the supplementary data stream according to the calculation parameters to obtain the compensation signal include the following steps:

[0025] Read the key sampling point type flag, key sampling point description field and compensation parameter field of each data frame in the supplementary data stream, and perform feature analysis in combination with the in-phase data, orthogonal data and IMU re-sampling data in the supplementary data stream; the feature analysis includes signal quality feature analysis, motion feature analysis, time feature analysis and compensation feature analysis;

[0026] Based on the feature analysis results, a multidimensional weight matrix is ​​established, and the comprehensive weight of each data frame in the supplementary data stream is calculated according to the multidimensional weight matrix and the feature weight adjustment factor; the feature weight adjustment factor includes a signal quality feature weight adjustment factor, a motion feature weight adjustment factor, a time feature weight adjustment factor and a compensation feature weight adjustment factor;

[0027] According to the comprehensive weight, the calculation parameters are allocated, and the calculation parameters are weighted to obtain the compensation signal; the compensation signal includes a timestamp, weighted in-phase data, weighted quadrature data, weighted IMU re-sampled data, a weight description field and a weighted processing parameter field.

[0028] In one embodiment, the steps of acquiring IMU data and analyzing validity of the synchronous data stream, and obtaining carrier motion information through calibration processing and motion information extraction include the steps of:

[0029] In the process of acquiring IMU data through synchronous data stream, the time series of IMU data is established according to the timestamps in the synchronous data stream, the difference between adjacent timestamps is calculated to evaluate the continuity of the time series, and the integrity of each data frame in the synchronous data stream is checked;

[0030] In the process of performing validity analysis on the synchronous data stream, the acceleration data and angular velocity data of the IMU data are analyzed for validity;

[0031] In the process of calibration processing of synchronous data stream, temperature compensation, zero bias calibration, scale factor calibration, inter-axis error calibration and dynamic error calibration are performed according to the effectiveness analysis results;

[0032] In the process of extracting motion information from the synchronous data stream, the gravity component and the motion acceleration component of the acceleration data in the calibrated synchronous data stream are separated, the angular velocity data in the calibrated synchronous data stream is integrated and calculated, and the carrier motion information is obtained based on the separation results and the integration calculation results; the carrier motion information includes timestamp, three-dimensional position coordinates, three-dimensional velocity vector, three-dimensional attitude angle, motion acceleration, angular velocity, acceleration change rate, angular velocity change rate and velocity change rate.

[0033] In one embodiment, the steps of converting the compensation signal into time-frequency domain, extracting time-frequency features from the converted compensation signal, and performing feature analysis on the time-frequency features to obtain signal spectrum features include the following steps:

[0034] Short-time Fourier transform and wavelet transform are used to convert the compensation signal into time-frequency domain;

[0035] Based on the energy distribution characteristics in the short-time Fourier transform result, the spectrum of the compensation signal is obtained;

[0036] Based on the spectrum of the compensation signal, the cross-spectrum of the in-phase data and the orthogonal data of the navigation signal is calculated to obtain the phase difference feature; and the instantaneous phase and frequency characteristics of the navigation signal are calculated through Hilbert transform. Based on the spectrum of the compensation signal, the amplitude ratio characteristics of the acceleration data and angular velocity data of the IMU data are calculated, and based on the wavelet transform result, the energy distribution of the IMU data is calculated to complete the extraction of time-frequency features;

[0037] The signal spectrum characteristics are obtained by performing feature analysis on the time-frequency characteristics. The signal spectrum characteristics include timestamp identification characteristics, energy distribution characteristics, shape characteristics, dynamic characteristics, phase characteristics and wavelet characteristics.

[0038] In one embodiment, the step of identifying abnormal features of signal spectrum features, comparing the identification results with carrier motion information, and obtaining interference features through correlation analysis includes the following steps:

[0039] Identify energy anomalies of energy distribution features in signal spectrum features, shape anomalies of shape features, and dynamic anomalies of dynamic features, and organize the energy anomalies, shape anomalies, and dynamic anomalies in chronological order to form an anomaly feature sequence;

[0040] Matching the timestamps of the anomaly feature sequence with the timestamps of the carrier motion information to establish time alignment;

[0041] On the basis of time alignment, the abnormal feature sequence is compared with the carrier motion information, and the energy abnormal points are analyzed based on the acceleration change rate and angular velocity change rate to determine whether the energy abnormal points are abnormalities related to drastic changes in motion. The shape abnormal points are analyzed based on the three-dimensional posture angle change to determine whether the shape abnormal points are abnormalities related to drastic changes in motion. The dynamic abnormal points are analyzed based on the velocity change rate to determine whether the dynamic abnormal points are abnormalities related to drastic changes in motion.

[0042] Based on the comparison results, the energy anomaly points, shape anomaly points and dynamic anomaly points are correlated and analyzed to obtain the corresponding correlation coefficients. When the correlation coefficients of the energy anomaly points, shape anomaly points and dynamic anomaly points are all lower than the corresponding threshold range, the corresponding abnormal features in the abnormal feature sequence are marked as potential interference sources.

[0043] Perform feature quantification analysis on potential interference sources to obtain interference features; interference features include timestamps, abnormal feature quantification values, motion parameters, and correlation coefficients.

[0044] In one embodiment, the step of determining the anti-interference parameter according to the interference feature and performing parameter processing on the compensation signal using the anti-interference parameter to obtain the anti-interference signal includes the steps of:

[0045] Based on the quantized value of abnormal features in the interference features, for the energy abnormal interference features in the interference features, the suppression depth is set according to the signal-to-noise ratio and the bandwidth; for the shape abnormal interference features in the interference features, the equalization parameters are configured according to the degree of spectrum distortion; for the dynamic abnormal interference features in the interference features, the tracking bandwidth is set according to the frequency hopping characteristics;

[0046] Based on the suppression depth, equalization parameters and tracking bandwidth, an anti-interference algorithm configuration is established for the interference characteristics to configure the notch filter parameters and the adaptive equalizer parameters in the frequency domain suppression algorithm; based on the suppression depth, equalization parameters, tracking bandwidth, notch filter parameters and the adaptive equalizer parameters, interference parameters are composed;

[0047] The anti-interference parameter is used to perform parameter processing on the compensation signal to obtain the anti-interference signal.

[0048] In one embodiment, the steps of analyzing the quality index of the anti-interference signal, extracting features according to the analysis results, and obtaining signal enhancement features by feature enhancement include the following steps:

[0049] Calculating the carrier-to-noise ratio, signal integrity and phase stability of the navigation signal, and extracting features including signal amplitude envelope, instantaneous amplitude, phase change rate and power spectrum density, or average amplitude, phase standard deviation and spectrum profile, or mean, variance and peak distribution within a preset window according to the quality levels of the carrier-to-noise ratio, signal integrity and phase stability;

[0050] Calculate the smoothness and consistency of the IMU data, and according to the quality level of the smoothness and consistency, extract features including motion parameters, or motion trends and key change points, or basic statistical features;

[0051] According to the quality level of carrier-to-noise ratio, signal integrity and phase stability, Wiener filter, FIR equalizer or adaptive filter is used to enhance the features and obtain signal enhancement features;

[0052] According to the quality level of smoothness and consistency, Butterworth filter, FIR filter or cubic spline interpolation and Kalman filter are used for feature enhancement to obtain signal enhancement features.

[0053] In one embodiment, the signal enhancement feature is reconstructed in the spatiotemporal domain, a corresponding relationship is established between the reconstructed signal enhancement feature and the carrier motion information, and the step of outputting the anti-interference navigation signal through regional fusion includes the following steps:

[0054] The signal enhancement features are reorganized in the order of timestamps to perform time domain reconstruction; the distribution of the features in space is calculated according to the enhanced motion features in the signal enhancement features to perform spatial domain reconstruction;

[0055] The reconstructed signal enhancement feature is time-aligned with the carrier motion information, and based on the time alignment, the correlation between the instantaneous acceleration and the feature change rate in the signal enhancement feature is compared, the corresponding relationship between the angular velocity data and the phase change in the signal enhancement feature is analyzed, and the correlation between the position change and the signal intensity distribution in the signal enhancement feature is evaluated, so as to establish the corresponding relationship between the reconstructed signal enhancement feature and the carrier motion information;

[0056] After the corresponding relationships are established, a credibility index is calculated for each corresponding relationship, and an area where the credibility index is within a first credibility range is marked as a high credibility area, an area where the credibility index is within a second credibility range is marked as a medium credibility area, and an area where the credibility index is within a third credibility range is marked as a low credibility area;

[0057] For high-confidence areas, the minimum mean square error criterion is used for optimal fusion. For medium-confidence areas, the weighted average method is used for optimal fusion. For low-confidence areas, motion information is used for feature correction for optimal fusion to output anti-interference navigation signals. The anti-interference navigation signals include the fused navigation signals, the fused IMU data and the fusion parameters.

[0058] One of the above technical solutions has the following advantages and beneficial effects:

[0059] The IMU-assisted navigation anti-interference method provided in each embodiment of the present application effectively improves the anti-interference performance of the system in complex environments through the deep fusion of data weight classification and weighted operations, achieves a comprehensive improvement in signal quality through the optimization configuration of anti-interference parameters and feature enhancement processing, and effectively solves the navigation signal processing problem in complex environments through the organic combination of spatiotemporal domain reconstruction and regional fusion. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a flow chart of the IMU-assisted navigation anti-interference method in an embodiment of the present application. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0062] In order to solve the aforementioned technical problems, in one embodiment, Figure 1 As shown, a navigation anti-interference method based on IMU assistance is provided, comprising the following steps:

[0063] Step S110, collecting navigation signals and IMU data, and performing time domain sampling and time synchronization processing on the navigation signals and IMU data to obtain a synchronous data stream.

[0064] The synchronous acquisition system for navigation signals and IMU data adopts a dual-channel parallel acquisition architecture based on FPGA (Field Programmable Gate Array), the core of which is the clock synchronization system. The clock synchronization system uses a temperature-compensated crystal oscillator as the main clock source, with a frequency stability of 0.1ppm and a reference frequency output of 10MHz. This high-precision clock signal generates multiple synchronous clocks through the phase-locked loop circuit inside the FPGA, providing a unified timing reference for navigation signal sampling and IMU data acquisition, thereby ensuring the time synchronization of the two-channel data acquisition.

[0065] Based on a unified clock reference, the system is equipped with a high-performance RF front end in the navigation signal sampling channel to receive and process navigation signals in the three frequency bands of L1, L2 and L5. The received RF signal is first amplified by a low-noise amplifier, the noise coefficient of which is controlled below 1.5dB, and its gain can be dynamically adjusted in the range of 30dB-60dB to adapt to reception scenarios with different signal strengths. The amplified signal is then passed through a bandpass filter for out-of-band interference suppression, and the bandwidth of the filter is precisely matched to the signal bandwidth. In order to convert the RF signal to a frequency band suitable for digital processing, the system adopts a three-stage down-conversion structure to convert the signal into a high intermediate frequency of 400MHz, a low intermediate frequency of 70MHz, and finally into a baseband signal. The converted baseband signal is quantized and sampled by a 16-bit high-precision analog-to-digital converter, and the sampling rate is set to 25MHz.

[0066] While the navigation signal sampling channel completes the baseband signal sampling, the IMU data acquisition channel uses a high-performance MEMS inertial measurement unit, which integrates a three-axis accelerometer and a three-axis gyroscope on the same chip. The accelerometer has four optional ranges of ±2g, ±4g, ±8g, and ±16g, and its full-scale noise density is controlled at 100μg / The gyroscope provides multiple ranges from ±250° / s to ±2000° / s, and its angle random walk is better than 0.1° / The IMU sensor is connected to the main controller using the SPI bus, and the clock frequency is set to 10MHz. In order to ensure the real-time data transmission, the system uses DMA in the IMU data transmission link to avoid the delay caused by CPU intervention.

[0067] After completing the acquisition of navigation signals and IMU data, the system needs to perform time domain sampling processing on the navigation signals and IMU data. In one example, the steps of acquiring navigation signals and IMU data, and performing time domain sampling and time synchronization processing on the navigation signals and IMU data to obtain a synchronous data stream include the following steps: using segmented coherent integration to perform coherent integration on the navigation signal once every first preset time length to obtain in-phase data and orthogonal data, using a fixed frequency equal interval sampling strategy to sample the IMU data once every second preset time length to obtain IMU sampling data to complete the time domain sampling of the navigation signal and IMU data; generating corresponding timestamps for the data sampling points of the navigation signal and IMU data, and resampling the IMU sampling data based on the sampling frequency difference between the in-phase data and the orthogonal data using a cubic spline interpolation algorithm to obtain IMU resampled data to complete the time synchronization processing of the navigation signal and IMU data; and encapsulating the timestamp, in-phase data, orthogonal data, and IMU resampled data to obtain a synchronous data stream.

[0068] Specifically, in the navigation signal processing link, the system adopts the segmented coherent integration method to perform coherent integration on the 25MHz navigation signal every 1ms (first preset duration) to obtain the in-phase data (I) and quadrature data (Q) after integration. In conjunction with the navigation signal integration processing, the system adopts a fixed-frequency equal-interval sampling strategy for the IMU data, completing the complete collection of sensor data every 5ms (second preset duration).

[0069] After the time domain sampling is completed, the system enters the data time synchronization processing stage. The FPGA first generates a 64-bit timestamp for each data sampling point of the navigation signal and IMU data. The resolution of the timestamp reaches 1ns, providing a time reference for subsequent precise synchronization. In view of the difference in sampling frequency between the navigation signal and the IMU data, the system uses a cubic spline interpolation algorithm to resample the IMU data so that its sampling points establish an accurate correspondence with the integration period of the navigation signal. For the time jitter that may occur during the sampling process, the system smoothes it through adaptive time window filtering.

[0070] After the above synchronization processing, the system organizes the two channels of data into a standardized synchronous data stream. Each data frame is encapsulated in a unified data structure, including the system timestamp, the navigation signal I / Q dual-channel integration value (i.e., in-phase data and orthogonal data), three-axis acceleration data, and three-axis angular velocity data (i.e., IMU data). In order to ensure the reliability of the data, the data frame includes a frame header and a CRC checksum. These data frames are organized into a continuous data stream in the order of timestamps, with a fixed frame interval of 1ms, which is synchronized with the integration period of the navigation signal. The integrity of the data frame is doubly guaranteed by the CRC checksum and the frame counter.

[0071] Step S120, performing period compensation processing on the synchronous data stream, adding key sampling points in the compensated synchronous data stream, and forming a supplementary data stream through compensation calculation.

[0072] For synchronous data streams, the system needs to deal with sampling instability caused by the high dynamic motion of the carrier. When the carrier adjusts its posture or changes its trajectory, it will cause irregular fluctuations in the sensor sampling sequence, which is manifested as random changes in the sampling interval and uneven information density. The system corrects the time consistency of the sampling sequence through a periodic compensation mechanism, and increases the data collection density in the interval where the carrier state changes significantly to ensure information integrity.

[0073] In one example, the steps of performing periodic compensation processing on a synchronous data stream, adding key sampling points in the compensated synchronous data stream, and forming a supplementary data stream through compensation calculation include the following steps: using the timestamp in the synchronous data stream as a reference, using a sliding time window to analyze the time interval between adjacent data frames in the synchronous data, and obtaining the time correction amount of each data frame in the synchronous data; interpolating the in-phase data, orthogonal data and IMU re-sampled data in the synchronous data according to the time correction amount; adding key sampling points to the interpolation processing results, performing data compensation calculation on the key sampling points, and organizing the information of the key sampling points, the compensation calculation results and the synchronous data stream into a supplementary data stream; the information of the key sampling points includes a key sampling point type flag and a key sampling point description field; the compensation calculation results include a compensation parameter field.

[0074] Specifically, the compensation process uses the timestamp of the data frame in the synchronous data stream as a benchmark and combines the frame counter information for time series analysis. The system uses a sliding time window to analyze the time interval between adjacent data frames. The window length is set to 20 data frames and the sliding step is 5 frames. For the standard 1ms frame interval, the system sets a tolerance range of ±50ns. When a time interval that exceeds the tolerance is detected, the compensation mechanism will be activated. The compensation mechanism first counts the distribution characteristics of the time interval in the window, including the mean, standard deviation, and trend change, and then calculates the compensation value based on these statistical characteristics. The compensation value Tc is calculated by weighted average: Tc=Σ(wi·Δti) / Σwi, where Tc is the final compensation time value, wi represents the weight coefficient of the i-th sample (which decreases with the increase of the time interval deviation), and Δti represents the time interval deviation of the i-th sample, and finally the time correction amount of each data frame is obtained.

[0075] Based on the calculated time correction, the system interpolates the I / Q data and IMU data of the navigation signal in the synchronous data stream. For the navigation signal, the system uses a complex domain interpolation algorithm to process the I / Q data and corrects the sampling time of each data frame according to the time correction. During the interpolation process, the navigation signal is expressed in the complex form S(t)=I(t)+jQ(t), where I(t) represents the in-phase component, Q(t) represents the orthogonal component, and j is the imaginary unit. The system normalizes the complex signal, and the normalized expression is A(t)=|S(t)| / Amax, where A(t) is the normalized amplitude value, |S(t)| is the instantaneous amplitude of the signal, and Amax is the maximum amplitude value in the observation window. The selection of interpolation points follows the principle of phase continuity, that is, the phase change of adjacent sampling points is required to be Δφ=|φ(t+Δt)-φ(t)|≤π / 4, where φ(t) represents the signal phase, ensuring that the phase change rate before and after interpolation does not exceed the preset range.

[0076] For IMU data, the system adopts a cubic spline interpolation strategy and adjusts the time position of the interpolation point according to the time correction. The interpolation function in the time interval [ti, ti+1] is expressed as S(t)=ai+bit+cit²+dit³, where ai represents the initial value term, bi represents the linear rate of change term, ci and di represent the coefficients of the quadratic and cubic terms respectively, and t is the time variable after time correction. At the same time, kinematic constraints are introduced, including that the synthetic acceleration |A| does not exceed 16g, the synthetic angular velocity |ω| does not exceed 2000° / s, the acceleration change rate |dA / dt| is limited to 2g / ms, and the angular velocity change rate |dω / dt| is limited to 100° / s², where A represents the three-axis synthetic acceleration and ω represents the three-axis synthetic angular velocity. These constraints ensure that the interpolation results meet the actual motion characteristics.

[0077] After obtaining the interpolation processing results, the system adds key sampling points to the corrected data. For navigation signals, the system detects four key features: amplitude jump, phase mutation, carrier-to-noise ratio mutation and Doppler frequency shift mutation. Amplitude jump detection is achieved by calculating the local variance σ²(t)=E[(A(t-μ)²], where σ²(t) is the local variance, A(t) is the signal amplitude, μ is the local mean, and E[·] represents the mathematical expectation operation. When the variance exceeds 3dB, 5 key sampling points are added before and after the current time after time correction. Phase mutation detection is performed by calculating the phase change rate Δφ / Δt, and key sampling points are added when the phase difference exceeds π / 4 radians. Carrier-to-noise ratio mutation detection is based on the short-term carrier-to-noise ratio change rate d(SNR) / dt, and key sampling points are added when the change rate exceeds 2dB / ms. Doppler frequency shift mutation detection is achieved by calculating the frequency change rate Δf / Δt, and key sampling points are added when the frequency change rate exceeds 10Hz / ms.

[0078] For IMU data, the system monitors the synthetic acceleration A= and angular velocity ω = The change characteristics of the three-axis acceleration components, ax, ay, az, and ωx, ωy, ωz are the three-axis angular velocity components. When the acceleration change rate dA / dt exceeds 2g / ms or the angular velocity change rate dω / dt exceeds 100° / s², 5 evenly distributed sampling points are added before and after the time correction, and the sampling interval is set to 0.1ms.

[0079] The system performs special data compensation calculations for the newly added key sampling points. In the navigation signal processing, an integral calculation is performed for each subdivided interval [t0, t1]: I'(t)=∫I(τ)dτ and Q'(t)=∫Q(τ)dτ, where τ is the integral variable, and the time-corrected t0 and t1 determine the integral interval. I'(t) and Q'(t) are the in-phase and orthogonal components after integration, respectively. The integral results are processed by amplitude normalization and phase calibration to ensure consistency with the original data. In the IMU data processing, the system uses a high-precision interpolation algorithm for the newly added sampling points. In addition to considering the smoothness constraints of the cubic spline, the algorithm also introduces acceleration continuity and angular velocity continuity constraints to ensure that the interpolation results meet the laws of physical motion.

[0080] Finally, the system organizes the processing results into supplementary data streams. The new data frame structure inherits the basic format of step 1 and expands the data field to include timestamp, navigation signal I / Q data, IMU data, compensation parameter field, key sampling point type flag, and key sampling point description field. Among them, the sampling point description field records whether the frame is a newly added key sampling point and the specific type of the sampling point. This supplementary data stream structure provides a complete data foundation for subsequent weighted grading processing, ensuring the stable operation of the system in complex environments.

[0081] Step S130, dividing the supplementary data stream into weight levels according to data features, allocating calculation parameters according to the weight levels, and performing calculations on the supplementary data stream according to the calculation parameters to obtain a compensation signal.

[0082] The reliability of the supplementary data stream is significantly affected by the characteristics of the transmission medium and the electromagnetic environment. Under different propagation paths and interference intensity conditions, the signal quality will show obvious regional differences. Data reliability is closely related to the geometric characteristics of the propagation medium, the electromagnetic reflection characteristics, and the intensity of the multipath effect. The system needs to build a data reliability assessment model and establish a quality level classification mechanism through multi-dimensional analysis to provide a basis for subsequent weight classification processing.

[0083] For the supplementary data stream, the system first divides the data into weight levels according to the data features. For each data frame, the system reads the key sampling point type flag, key sampling point description field and compensation parameter field, and performs feature analysis in combination with the navigation signal I / Q data and IMU data. Feature analysis is based on four dimensions: signal quality features, motion features, time features and compensation features.

[0084] In one example, the steps of dividing the supplementary data stream into weight levels according to data features, allocating calculation parameters according to the weight levels, and calculating the supplementary data stream according to the calculation parameters to obtain a compensation signal include the following steps: reading the key sampling point type flag, key sampling point description field and compensation parameter field of each data frame in the supplementary data stream, and performing feature analysis in combination with the in-phase data, orthogonal data and IMU re-sampled data in the supplementary data stream; the feature analysis includes signal quality feature analysis, motion feature analysis, time feature analysis and compensation feature analysis; based on the feature analysis results, establishing a multidimensional weight matrix, and calculating the comprehensive weight of each data frame in the supplementary data stream according to the multidimensional weight matrix and feature weight adjustment factors; the feature weight adjustment factors include signal quality feature weight adjustment factors, motion feature weight adjustment factors, time feature weight adjustment factors and compensation feature weight adjustment factors; according to the comprehensive weights, allocating calculation parameters, and performing weighted operations on the calculation parameters to obtain the compensation signal; the compensation signal includes a timestamp, weighted in-phase data, weighted quadrature data, weighted IMU re-sampled data, a weight description field and a weighted processing parameter field.

[0085] In the signal quality feature analysis, the system prioritizes the I / Q data of the navigation signal. The carrier-to-noise ratio (SNR) is used as the main evaluation index, and its calculation adopts SNR=10log 10 The SNR is in the form of (Ps / Pn), where Ps represents the signal power and Pn represents the noise power. According to the calculation results of the carrier-to-noise ratio, the system can use the piecewise weight function W(SNR) to assign weights: it is recommended to set the weight to a value in the range of 0.9-1.0 when the SNR is higher than 25dB, to 0.6-0.8 when the SNR is between 15dB-25dB, and to 0.3-0.5 when the SNR is lower than 15dB. For IMU data, the system defines the normalized range coefficient η=M / Mmax, where M is the current measurement value and Mmax is the full-scale value. It is recommended to set the weight to 0.8-1.0 when η is in the range of 0.2-0.8, to 0.5-0.7 when it is in the range of 0.8-0.9, and to 0.2-0.4 in other cases.

[0086] In the motion feature analysis, the system evaluates the motion state of the carrier based on IMU data. The motion stability evaluation uses the synthetic acceleration change rate ΔA / Δt and angular velocity change rate Δω / Δt, where ΔA represents the acceleration change, Δω represents the angular velocity change, and Δt is the sampling time interval. The motion stability weight Ws can be determined by a piecewise linear function: it is recommended that when ΔA / Δt is less than 0.8-1.2g / ms and Δω / Δt is less than 45-55° / s², Ws can be set to 0.9-1.0; when the threshold is exceeded, the weight can be linearly reduced to 0.3-0.5. The motion continuity evaluation is achieved by calculating the change rate of the motion parameters of adjacent data frames δm=|m(t)-m(t-1)| / Δt, where m(t) represents the motion parameters at the current moment. It is recommended that the weight coefficient can be set to 0.8-0.95 when the parameter changes smoothly, and can be reduced to 0.4-0.6 when a jump occurs. The system can take the smaller value of the motion stability and continuity weights as the final motion feature weight.

[0087] Time feature analysis focuses on the sampling uniformity of data frames. The system defines the time interval deviation coefficient ε=|Δt-T0| / T0, where Δt is the actual sampling interval and T0 is the standard sampling interval of 1ms. For data frames with standard intervals, it is recommended that the weight coefficient be set to 0.9-1.0. For the key sampling points added in step 2, the weight can be set according to the uniformity of the time interval: it is recommended that the weight be set to 0.8-0.95 when ε is less than 0.1, the weight be set to 0.6-0.8 when ε is between 0.1 and 0.2, and the weight be set to 0.4-0.6 when ε is greater than 0.2.

[0088] The compensation feature analysis evaluates the compensation effect based on the compensation parameters in step 2. The system defines the data consistency index λ=|D'-D| / |D|, where D' is the compensated data value and D is the original data value. It is recommended that when λ is less than 0.05 (consistency is higher than 95%), the weight coefficient can be set to 0.85-0.95; when λ is between 0.05 and 0.15, the weight coefficient can be set to 0.6-0.8; when λ is greater than 0.15, the weight coefficient can be set to 0.3-0.5.

[0089] Based on the above classification results, the system establishes a multi-dimensional weight matrix W=[wij], where i represents the data frame number and j represents the feature dimension. In order to balance the importance of different features, the system can introduce a feature weight adjustment factor α, and the recommended value ranges are: signal quality feature 0.35-0.45, time feature 0.25-0.35, motion feature 0.15-0.25, compensation feature 0.05-0.15. The comprehensive weight of the data frame is calculated as Wc=Σ(αj·wij), where αj is the adjustment factor of the j-th dimension feature and wij is the corresponding feature weight.

[0090] After determining the weight of the data frame, the system starts to allocate calculation parameters. The bandwidth Bf, smoothing window length Ls and fusion coefficient Kf of the filter parameters can be dynamically adjusted according to the comprehensive weight Wc. It is recommended that for high-weight data frames (Wc>0.8), a smaller filter bandwidth and smoothing window can be used; for medium-weight data frames (0.5≤Wc≤0.8), the filter bandwidth and smoothing window can be appropriately increased; for low-weight data frames (Wc<0.5), a larger filter bandwidth and smoothing window can be used.

[0091] Subsequently, the system uses the assigned parameters for weighted operations. For the I / Q data of the navigation signal, the weighted sliding average method is used: y(n)=Σ[Wc(k)·x(nk)] / Σ[Wc(k)], where y(n) is the output data, x(n) is the input data, and Wc(k) is the corresponding weight coefficient. For IMU data, the system uses a weighted Kalman filter, and its state estimation equation is X(k|k)=X(k|k-1)+Wc·K(k)[Z(k)-H·X(k|k-1)], where K(k) is the Kalman gain matrix, H is the observation matrix, and Z(k) is the observation value.

[0092] During the weighted calculation process, the system pays special attention to the key sampling points added in step 2. Although the sampling intervals of these points are small, the system recommends maintaining a relatively high weight range (0.8-0.95) during calculation because they capture the rapid change characteristics of the signal. The gradual change of weights is achieved by constructing a smooth weight transition function W(t)=exp(-|t-t0| / τ), where t0 is the key sampling point moment, τ is the feature-related time constant, and the value of τ can be adjusted within the range of 1-10ms according to the actual application scenario.

[0093] Finally, the system outputs the compensation signal. The data frame structure of the compensation signal includes: timestamp, weighted navigation signal I / Q data, weighted IMU data, weight description field and processing parameter field. The weight description field records the comprehensive weight level and feature weight distribution of the data frame, and the processing parameter field stores the key parameters used in the weighted operation.

[0094] Step S140, performing IMU data acquisition and validity analysis on the synchronous data stream, and obtaining carrier motion information through calibration processing and motion information extraction.

[0095] In one example, the steps of acquiring IMU data and analyzing validity of a synchronous data stream, and obtaining carrier motion information through calibration processing and motion information extraction include the following steps:

[0096] In the process of acquiring IMU data in synchronous data stream, the time series of IMU data is established according to the timestamp in the synchronous data stream, the difference between adjacent timestamps is calculated to evaluate the continuity of the time series, and the integrity check is performed on each data frame in the synchronous data stream. For the synchronous data stream, the system first acquires IMU data. The acquisition process is based on the IMU data in the data frame of the synchronous data stream, including three-axis acceleration data and three-axis angular velocity data. The system establishes the time series of IMU data according to the timestamp, and the sampling interval can be set to the range of 4-6ms. Since the IMU data may contain temperature effects, the system also processes the temperature information in the data frame for subsequent temperature compensation. In the data acquisition stage, the system performs an integrity check on each data frame to ensure that the synchronization field of the data frame is correct and the CRC check passes. The continuity of the time series is evaluated by calculating the difference between adjacent timestamps δt=ti+1-ti. It is recommended to use cubic spline interpolation to repair the continuity of the data series when it is detected that δt exceeds the preset range (generally set to ±5% of the nominal interval).

[0097] In the process of validating the synchronous data stream, the IMU data acceleration data and angular velocity data are analyzed for validity. The validity analysis of IMU data is first evaluated on the acceleration data. The validity analysis of acceleration data is based on the calculation of the three-axis composite acceleration: A= , where ax, ay, and az represent the acceleration components in the three axes. It is recommended that the root mean square value of the resultant acceleration should be in the range of 0.98g to 1.02g in a stationary state. The system can calculate the short-time standard deviation σa= Evaluate the stability of acceleration data, where Ai is the acceleration value of the i-th sampling point, Ā is the average value, N is the number of sampling points, and the recommended σa threshold can be set in the range of 0.05g to 0.15g. For the three-axis angular velocity data, the system checks whether it is within the recommended range (which can be set to ±250° / s to ±2000° / s according to the application scenario). The angular velocity change rate is evaluated using the calculation method of dω / dt=(ωi+1-ωi) / Δt. It is recommended to set the change rate threshold in the range of 80° / s²-120° / s². When data mutations are detected, the system will mark these points as suspicious data points that require special processing.

[0098] During the calibration process of the synchronous data stream, temperature compensation, zero bias calibration, scale factor calibration, inter-axis error calibration and dynamic error calibration are performed according to the effectiveness analysis results.

[0099] Based on the results of the effectiveness analysis, the system begins to implement a comprehensive calibration process. First, temperature compensation and zero bias calibration are performed. Temperature compensation is based on the temperature-zero bias relationship curve. The system recommends establishing a compensation point every 10°C in the range of -40°C to +85°C, and using a piecewise linear fitting method to correct the output. Zero bias calibration uses static segment data. When the standard deviation of the synthetic acceleration is less than the recommended value (such as 0.08m / s²-0.12m / s²) and the standard deviation of the angular velocity is less than the recommended threshold (such as 0.08° / s-0.12° / s) for more than 0.8-1.2 seconds, it can be determined as a static segment.

[0100] Then the scale factor calibration, inter-axis error calibration and dynamic error calibration are carried out. The scale factor calibration is achieved by the integral verification method, and it is recommended to control the calibration accuracy within the range of 0.08% to 0.12%. The inter-axis error calibration adopts the six-plane calibration method. Under the condition of known gravity acceleration, the IMU is rotated to six orthogonal planes to establish an installation error compensation model, so that the coupling error calibration accuracy is better than 0.4°-0.6°. The dynamic error calibration dynamically adjusts the calibration parameters according to the change of the motion state.

[0101] The system divides the motion state into four basic states: static, uniform, accelerating and turning. State recognition can be based on the combination of acceleration and angular velocity, for example:

[0102] Stationary state: The recommended threshold for the synthetic acceleration change rate is 0.05g / s-0.15g / s, and the recommended threshold for the angular velocity is 0.5° / s-1.5° / s.

[0103] Uniform speed state: The recommended threshold value for the synthetic acceleration change rate is 0.15g / s-0.25g / s, and the recommended threshold value for the angular velocity is 1.5° / s-2.5° / s.

[0104] Acceleration state: The recommended threshold value of the synthetic acceleration change rate is greater than 0.25g / s.

[0105] Turning state: The recommended angular velocity threshold is greater than 2.5° / s.

[0106] In the process of extracting motion information from the synchronous data stream, the gravity component and the motion acceleration component of the acceleration data in the calibrated synchronous data stream are separated, the angular velocity data in the calibrated synchronous data stream is integrated and calculated, and the carrier motion information is obtained based on the separation results and the integration calculation results; the carrier motion information includes timestamp, three-dimensional position coordinates, three-dimensional velocity vector, three-dimensional attitude angle, motion acceleration, angular velocity, acceleration change rate, angular velocity change rate and velocity change rate.

[0107] The data that has completed the calibration process enters the motion information extraction stage. The system first separates the gravity component and the motion acceleration component of the acceleration data. The separation process uses low-pass filtering technology, and the filter cutoff frequency can be set in the range of 15-25Hz according to the actual application. It is recommended to use a fourth-order Butterworth filter to obtain good phase-frequency characteristics. The separated gravity component is used for attitude estimation, and its direction is the vertical direction reference in the carrier coordinate system; the motion acceleration component obtains velocity and position information through time domain integration.

[0108] For the angular velocity data, the system calculates the change in the carrier's attitude angle by integration. The integration process uses the quaternion representation method, and the quaternion update equation is: Where q represents the quaternion, ω represents the angular velocity vector, Represents quaternion multiplication. The attitude solution update interval is recommended to be set within the range of 80-120ms to balance the calculation accuracy and real-time requirements. The system uses error feedback to correct the integral error and updates the attitude estimate through the gravity vector reference.

[0109] Finally, the system outputs the carrier motion information in a standard format. The data frame structure of the motion information includes: timestamp, 3D position coordinates (recommended accuracy better than 0.8-1.2m), 3D velocity vector (recommended accuracy better than 0.08-0.12m / s), 3D attitude angle (recommended accuracy better than 0.4-0.6°), motion acceleration (three axes) and angular velocity (three axes). Each motion parameter is accompanied by an accuracy evaluation index as a quality indicator of the motion information. These motion information will provide important auxiliary references for subsequent navigation signal processing.

[0110] Step S150, performing time-frequency domain conversion on the compensation signal, extracting time-frequency features from the converted compensation signal, and performing feature analysis on the time-frequency features to obtain signal spectrum features.

[0111] In one example, the steps of performing time-frequency domain conversion on the compensation signal, extracting time-frequency features from the converted compensation signal, and performing feature analysis on the time-frequency features to obtain signal spectrum features include the following steps:

[0112] The compensation signal is converted into time-frequency domain by using short-time Fourier transform and wavelet transform; the spectrum of the compensation signal is obtained based on the energy distribution characteristics in the short-time Fourier transform result; based on the spectrum of the compensation signal, the cross-spectrum of the in-phase data and the orthogonal data of the navigation signal is calculated to obtain the phase difference feature; and the instantaneous phase and frequency characteristics of the navigation signal are calculated by Hilbert transform, and the amplitude ratio characteristics of the acceleration data and angular velocity data of the IMU data are calculated based on the spectrum of the compensation signal, and the energy distribution of the IMU data is calculated based on the wavelet transform result to complete the extraction of time-frequency features; the time-frequency features are analyzed to obtain the signal spectrum features, and the signal spectrum features include timestamp identification features, energy distribution features, shape features, dynamic features, phase features and wavelet features.

[0113] Specifically, for the compensation signal, the system mainly uses short-time Fourier transform and wavelet transform to convert the time-frequency domain. In the short-time Fourier transform processing, the conversion process is based on the navigation signal I / Q data and IMU data in the compensation signal, and the weight description field is used as a reference for data credibility. The time window length can be dynamically adjusted according to the weight level: it is recommended to use an 8ms-12ms window for data segments with a weight greater than 0.8, and the window overlap rate can be set to 70% to 80%; a 15ms-25ms window is used when the weight is between 0.5-0.8, and the overlap rate can be set to 45% to 55%; a 35ms-45ms window is used when the weight is less than 0.5, and the overlap rate can be set to 20% to 30%.

[0114] Each processing window is first normalized: S'(t)=[S(t)-μ] / σ, where S'(t) is the normalized signal, S(t) is the original signal, μ is the mean in the window, and σ is the standard deviation. Then the Hanning window function w(n) is used for weighting: w(n)=0.5[1-cos(2πn / N)], where n is the sample number and N is the window length.

[0115] The system improves the frequency resolution of high-weight windows by zero padding, and the recommended resolution range is 0.8Hz-1.2Hz; the medium-weight window maintains a resolution of 1.8Hz-2.2Hz; the low-weight window uses a resolution of 3.8Hz-4.2Hz. For the I / Q data processing of the navigation signal, the system performs orthogonal processing before performing FFT to eliminate the impact of I / Q data imbalance. For IMU data, the system performs bandpass filtering on acceleration and angular velocity data before FFT. The filter bandwidth can be adaptively adjusted according to the motion characteristics. The recommended bandwidth range is 0.8-1.2 times the main frequency of the signal.

[0116] In wavelet transform processing, the system selects wavelet basis functions of different scales to perform multi-resolution analysis on the signal. Wavelet transform can be expressed as: W(a,b)=(1 / √a)∫x(t)ψ*(tb / a)dt, where a is the scale parameter, b is the translation parameter, and ψ(t) is the wavelet basis function. It is recommended to use complex wavelet basis functions (such as Morlet wavelet) for navigation signals to maintain phase information; it is recommended to use real wavelet basis functions (such as db4 wavelet) for IMU data, focusing on energy distribution. The scale range of wavelet transform is recommended to be from 1 to 8, and the corresponding frequency coverage is recommended to be 0.8kHz-1.2kHz.

[0117] After obtaining the time-frequency conversion results, the system begins to extract time-frequency features. Feature extraction is first based on the short-time Fourier transform results, focusing on the energy distribution characteristics of the signal. The system divides the spectrum into three intervals: low frequency band (recommended range 0Hz-8Hz), medium frequency band (recommended range 8Hz-120Hz) and high frequency band (recommended greater than 120Hz). The characteristic parameters are calculated in each interval: E(i)=Σ|X(f)|², f∈Fi, where E(i) is the energy value of the i-th frequency band, X(f) is the spectrum amplitude, and Fi is the frequency band range.

[0118] For the navigation signal I / Q data, the system obtains the phase difference characteristics by calculating the cross-spectrum S12(f)=X1(f)X2*(f), where X1(f) and X2(f) are the spectra of the I and Q channels. The system calculates the instantaneous phase and frequency characteristics of the signal through Hilbert transform. After performing Hilbert transform H[s(t)] on the original signal s(t), the analytical signal sa(t)=s(t)+jH[s(t)] can be obtained. Based on the analytical signal, the instantaneous phase φ(t)=arctan(H[s(t)] / s(t) is calculated, and then the instantaneous frequency f(t)=(1 / 2π)·dφ(t) / dt can be obtained. In this way, the system can accurately track the phase and frequency change characteristics of the signal.

[0119] For IMU data, the system focuses on analyzing the amplitude ratio characteristics of acceleration and angular velocity signals in each frequency band Ra / ω(f)=|Xa(f)| / |Xω(f)|, where Xa(f) and Xω(f) are the frequency spectra of acceleration and angular velocity, respectively. At the same time, based on the wavelet transform results, the system calculates the energy distribution at each scale: E(j)=Σ|W(aj,b)|², where aj is the jth scale parameter. The system also pays special attention to the migration law of energy in the time-frequency plane by calculating the trajectory and speed of the energy center of gravity: fc(t)=∫f|X(f,t)|²df / ∫|X(f,t)|²dfvc(t)=dfc(t) / dt, where fc(t) is the frequency center of gravity and vc(t) is the center of gravity speed.

[0120] Based on the extracted time-frequency features, the system conducts in-depth analysis to obtain complete spectrum features. During the analysis, the navigation signal is first processed and the power spectrum density P(f) of the signal is obtained by short-time Fourier transform. The system characterizes the frequency domain characteristics of the signal by calculating the spectrum statistical moments: the spectrum center of gravity M1=∫fP(f)df reflects the main frequency position where the energy is concentrated; the spectrum broadening M2=∫(f-M1) 2 P(f)df describes the degree of energy dispersion; spectral skewness M3=∫(f-M1) 3 P(f)df characterizes the asymmetry of energy distribution; spectral kurtosis M4=∫(f-M1) 4 P(f)df reflects the steepness of the energy distribution. At the same time, the system analyzes the local spectrum characteristics of the navigation signal, including the main lobe bandwidth, side lobe level ratio and zero point distribution. These parameters together reflect the fine structure of the signal in the frequency domain.

[0121] For IMU data, the system calculates the cross-correlation function of the acceleration and angular velocity spectra: Ra,ω(τ)=∫Xa(f)Xω*(f)exp(j2πfτ)df, where Xa(f) and Xω(f) represent the acceleration and angular velocity spectra, respectively, and τ is the time delay variable. By analyzing the coupling relationship between the two signals in the frequency domain, the system extracts the characteristic parameters of the cross-correlation function, including peak value (normalized value with a recommended range of 0.8-1.2), delay (recommended range of ±5ms), and bandwidth (recommended range of 0.8-1.2 times the signal bandwidth). This analysis can reveal the intrinsic correlation between acceleration and angular velocity signals in IMU data.

[0122] Finally, the system organizes and stores all features in a standard format. The basic structure of spectrum feature data includes: time attributes of timestamp identification features, energy distribution features (including energy distribution parameters of each frequency band), shape features (including statistical moment features of the spectrum), dynamic features (including instantaneous phase and frequency change features), as well as phase features (phase stability and modulation characteristics), and wavelet features (multi-scale analysis results).

[0123] Step S160, performing abnormal feature recognition on the signal spectrum features, comparing the recognition results with the carrier motion information, and obtaining interference features through correlation analysis.

[0124] In a complex navigation environment, the anomaly of the spectrum characteristics may be caused by external interference or normal signal changes caused by changes in the carrier's motion. The system needs to accurately determine the source of these anomalies through correlation analysis.

[0125] In one example, the steps of identifying abnormal features of signal spectrum features, comparing the identification results with carrier motion information, and obtaining interference features through correlation analysis include the following steps: identifying energy abnormal points of energy distribution features in signal spectrum features, shape abnormal points of shape features, and dynamic abnormal points of dynamic features, and organizing the energy abnormal points, shape abnormal points, and dynamic abnormal points in chronological order to form an abnormal feature sequence; matching the timestamp of the abnormal feature sequence with the timestamp of the carrier motion information to establish time alignment; on the basis of time alignment, comparing the abnormal feature sequence with the carrier motion information, analyzing the energy abnormal points based on the acceleration change rate and the angular velocity change rate, and judging whether the energy abnormal points are inconsistent with the motion information. Anomalies related to drastic changes in motion are analyzed based on the changes in three-dimensional posture angles to determine whether the shape anomalies are anomalies related to drastic changes in motion. Dynamic anomalies are analyzed based on the speed change rate to determine whether the dynamic anomalies are anomalies related to drastic changes in motion. Based on the comparison results, correlation analysis is performed on energy anomalies, shape anomalies and dynamic anomalies to obtain corresponding correlation coefficients. When the correlation coefficients of energy anomalies, shape anomalies and dynamic anomalies are all lower than the corresponding threshold range, the corresponding abnormal features in the abnormal feature sequence are marked as potential interference sources. Feature quantification analysis is performed on potential interference sources to obtain interference features. Interference features include timestamps, abnormal feature quantization values, motion parameters and correlation coefficients.

[0126] Specifically, for the spectrum features, the system first identifies abnormal features. The identification process is based on the analysis of the energy distribution features, shape features, and dynamic features in the spectrum features. In the energy distribution anomaly identification, the system recommends setting the energy ratio threshold to: 25% to 35% for the low frequency band (0Hz-8Hz), 45% to 55% for the mid-frequency band (8Hz-120Hz), and 15% to 25% for the high frequency band (>120Hz). Energy anomaly judgment can be made using: E_ratio(i)=Ei / Etotal, where Ei is the energy of the i-th frequency band and Etotal is the total energy. When the energy ratio of any frequency band exceeds the recommended threshold range, the system records it as an energy anomaly point.

[0127] The recognition of shape feature anomalies is based on the analysis of the statistical moment changes of the spectrum. When the center of gravity offset Δfc of the spectrum exceeds the recommended range of the nominal position (15% to 25%), or the spectrum broadening ratio Ws exceeds the recommended range of the normal value (1.8 times to 2.2 times), the system marks it as a shape anomaly. Where: Δfc=|fc-fc_nominal| / fc_nominalWs=σf² / σf_nominal².

[0128] In the dynamic characteristic anomaly identification, the system monitors the changing characteristics of the instantaneous frequency f(t) and phase φ(t). When the frequency change rate exceeds the recommended range of 80Hz / ms-120Hz / ms, or the phase jump exceeds the recommended range (0.45π-0.55π), the system records it as a dynamic anomaly. The system organizes all abnormal points in chronological order to form an abnormal characteristic sequence.

[0129] For the identified abnormal feature sequence, the system calls the carrier motion information in step 4 for comparison. The comparison process first establishes time alignment, matches the timestamp of the abnormal feature point with the timestamp of the carrier motion information, and recommends that the alignment accuracy be set within the range of 0.8ms-1.2ms. For each abnormal point, the system extracts the three-dimensional position coordinates of the carrier at the corresponding moment (the recommended accuracy is better than 0.8m-1.2m), three-dimensional velocity vector (the recommended accuracy is better than 0.08m / s-0.12m / s), three-dimensional attitude angle (the recommended accuracy is better than 0.4°-0.6°), motion acceleration and angular velocity data. The system extracts the motion data of 45m / s-55ms before and after the abnormal feature point to construct a motion feature window.

[0130] Based on time alignment, the system performs feature comparison. For energy anomalies, the system analyzes based on the acceleration change rate Ra and the angular velocity change rate Rω: Ra=|dA / dt|Rω=|dω / dt|It is recommended that when Ra exceeds 1.8g / ms-2.2g / ms or Rω exceeds 90° / s²-110° / s², the energy anomaly is determined to be related to a drastic change in motion; otherwise, it is marked as a non-motion-related anomaly.

[0131] For abnormal shape points, the system checks the three-dimensional attitude angle change rate Rθ: Rθ= Among them, θ, φ, and ψ are three attitude angles. It is recommended that when Rθ exceeds 8-12° / s, the spectrum deformation is considered to be related to attitude adjustment; deformation below this threshold range is recorded as non-motion-related anomalies.

[0132] For dynamic anomalies, the system compares the velocity vector characteristics. It is recommended that when the velocity change rate exceeds 0.45m / s²-0.55m / s², the frequency jump can be attributed to the motion state transition; otherwise, it is recorded as a non-motion related anomaly.

[0133] Based on the above comparison results, the system conducts correlation analysis. The analysis uses a sliding time window of 90ms-110ms in length, and the window overlap rate is recommended to be set to 45%-55%. In each window, the system calculates the correlation coefficient matrix between abnormal features and motion parameters: ρij=Cov(Xi,Yj) / [σ(Xi)σ(Yj)], where Xi represents the abnormal feature parameters and Yj represents the motion parameters.

[0134] For energy anomalies, the system calculates the correlation between the energy value of the abnormal point and the synthetic acceleration and angular velocity, and recommends setting the correlation threshold to 0.25-0.35. For shape anomalies, the system analyzes the correlation between the spectrum shape parameters and the attitude angle change, and recommends setting the correlation threshold to 0.35-0.45. For dynamic anomalies, the system examines the correlation strength between the frequency change characteristics and the speed change, and recommends setting the correlation threshold to 0.3-0.4. When the correlation coefficients of the three types of abnormal features are all lower than the corresponding threshold range, the system marks the abnormal feature as a potential interference source.

[0135] The system performs quantitative feature analysis on the marked potential interference sources. First, the amplitude characteristics of the abnormal features are calculated, including the ratio of the signal amplitude to the background noise (the recommended signal-to-noise ratio calculation range is 15dB-25dB), the amplitude difference between the abnormal peak and the adjacent frequency point (the recommended prominence setting range is 8dB-12dB), and the amplitude change trend during the duration of the abnormality. The system accurately locates the frequency position of the abnormal feature, records the center frequency point, frequency offset (the recommended offset range is ±4% to 6% of the center frequency), and frequency stability. The system measures the impact range of the abnormal feature, including the main lobe bandwidth (recommended -3dB bandwidth measurement), sidelobe level, and spectrum leakage.

[0136] The interference features of the final output are organized in a standard format. Each feature contains: timestamp, abnormal feature quantization value (including interference type, frequency position and feature strength), motion parameters and correlation coefficients at the corresponding moment. The system organizes the interference features into a continuous feature stream in chronological order. These features accurately describe the potential interference separated from the spectrum anomaly, providing an important basis for subsequent anti-interference processing.

[0137] Step S170, determining an anti-interference parameter according to the interference feature, and performing parameter processing on the compensation signal using the anti-interference parameter to obtain an anti-interference signal.

[0138] In one example, anti-interference parameters are determined according to interference features, and the steps of parameter processing the compensation signal with the anti-interference parameters to obtain the anti-interference signal include the following steps: based on the quantized value of the abnormal feature in the interference feature, for the energy abnormal interference feature in the interference feature, setting the suppression depth according to the signal-to-noise ratio and the bandwidth; for the shape abnormal interference feature in the interference feature, configuring the equalization parameter according to the degree of spectrum distortion; for the dynamic abnormal interference feature in the interference feature, setting the tracking bandwidth according to the frequency hopping characteristics; based on the suppression depth, equalization parameters and tracking bandwidth, establishing the anti-interference algorithm configuration for the interference feature to configure the notch filter parameters and the adaptive equalizer parameters in the frequency domain suppression algorithm; based on the suppression depth, equalization parameters, tracking bandwidth, notch filter parameters and adaptive equalizer parameters to form interference parameters; and parameter processing the compensation signal with the anti-interference parameters to obtain the anti-interference signal.

[0139] Specifically, based on the interference characteristics, the system first determines the anti-interference parameters. The determination process is based on the quantized values ​​of the abnormal characteristics, including the interference type, frequency position, and characteristic strength. For energy anomaly interference, the system sets the suppression depth according to the signal-to-noise ratio and bandwidth. It is recommended that when the signal-to-noise ratio exceeds 20dB, the suppression depth can be set to 28dB-32dB, and when the signal-to-noise ratio is between 10-20dB, the suppression depth can be set to 18dB-22dB. For shape anomaly interference, the system configures the equalization parameters according to the degree of spectrum distortion. It is recommended that when the spectrum center of gravity offset exceeds 25% to 35%, the equalization strength can be set to 0.75-0.85, and when the offset is between 10% and 30%, the equalization strength can be set to 0.45-0.55. For dynamic abnormal interference, the system sets the tracking bandwidth based on the frequency hopping characteristics. If the hopping rate is greater than 80Hz / ms-120Hz / ms, it is recommended to set the tracking bandwidth to 180Hz-220Hz. When the hopping rate is between 40Hz / ms-60Hz / ms, the bandwidth can be set to 90Hz-110Hz.

[0140] Based on the basic parameters determined above, the system further establishes detailed anti-interference algorithm configuration for each type of interference feature. In the frequency domain suppression algorithm, the system sets the notch filter parameters according to the frequency position of the interference feature. The center frequency fc, bandwidth B and attenuation depth D of the notch filter are determined by the following relationship: H(f)=1-D·exp[-(f-fc)² / (2B²)], where the quality factor Q=fc / B of the notch filter can be dynamically adjusted according to the interference bandwidth. It is recommended that the quality factor of narrowband interference (bandwidth less than 8Hz-12Hz) can be set to 45-55, and the quality factor of broadband interference (bandwidth greater than 8Hz-12Hz) can be set to 8-12. At the same time, the system configures the adaptive equalizer parameters, and the gain curve G(f) of the equalizer is adjusted in real time according to the degree of spectrum distortion: G(f)=α·[1+β·sin(2πf / fs)], where α is the reference gain (recommended range 0.8-1.2), β is the modulation coefficient (recommended range 0.1-0.3), and fs is the sampling frequency.

[0141] Based on the basic parameters determined above, the system further establishes detailed anti-interference algorithm configuration for each type of interference feature. In the frequency domain suppression algorithm, the system sets the notch filter parameters according to the frequency position of the interference feature. The center frequency fc, bandwidth B and attenuation depth D of the notch filter are determined by the following relationship: H(f)=1-D·exp[-(f-fc)² / (2B²)], where the quality factor Q=fc / B of the notch filter can be dynamically adjusted according to the interference bandwidth. It is recommended that the quality factor of narrowband interference (bandwidth less than 8Hz-12Hz) can be set to 45-55, and the quality factor of broadband interference (bandwidth greater than 8Hz-12Hz) can be set to 8-12. At the same time, the system configures the adaptive equalizer parameters, and the gain curve G(f) of the equalizer is adjusted in real time according to the degree of spectrum distortion: G(f)=α·[1+β·sin(2πf / fs)], where α is the reference gain (recommended range 0.8-1.2), β is the modulation coefficient (recommended range 0.1-0.3), and fs is the sampling frequency.

[0142] The system combines basic parameters and detailed algorithm configuration to form a complete processing strategy. The basic parameters determine the overall suppression strength, while the algorithm configuration determines the specific implementation method. After completing the configuration combination, the system begins to apply anti-interference parameters to the compensation signal in step three. The processing process first reads the navigation signal I / Q data and IMU data in the compensation signal, and pre-processes them in combination with the corresponding weight information. For high-weight data segments (weights greater than 0.8), it is recommended to use narrowband notches for precise suppression; for medium-weight data segments (weights between 0.5-0.8), it is recommended to use a wider suppression bandwidth and introduce adaptive equalization; for low-weight data segments (weights less than 0.5), it is recommended to adopt a conservative suppression strategy, mainly for spectrum smoothing.

[0143] When implementing the above processing strategy, the system adopts a sliding window mechanism. It is recommended that the window length be set to 90ms-110ms, and the overlap rate can be set to 70%-80%. In each processing window, the system performs FFT transformation, notch suppression, spectrum equalization and dynamic tracking in sequence. Notch suppression is implemented using an IIR digital filter, and its transfer function can be expressed as: H(z)=[b0+b1z -1 +b2z -2 ] / [1+a1z -1 +a2z -2]The zero-pole position of the filter is updated in real time according to the interference frequency and bandwidth. Spectral equalization is completed by FIR type equalizer, and its output can be expressed as: y(n)=Σh(k)x(nk) where h(k) is the equalizer coefficient, and the recommended coefficient length range is 15-25. The coefficient can be adaptively adjusted according to the degree of spectrum distortion. Dynamic tracking uses phase-locked loop technology, and its phase error control equation is: e(n)=φ(n)-φ'(n) where the bandwidth of the tracking loop can be dynamically configured according to the change rate of the interference characteristics.

[0144] To ensure the continuity and smoothness of processing, the system uses the overlap-add method between adjacent windows and uses the Hanning window for weighting: w(n)=0.5[1-cos(2πn / N)] where N is the window length. For each overlapping area, the system calculates the weighted average value based on the amplitude characteristics of the window function. It is recommended that the length of the overlapping area be 20% to 30% of the window length. After processing, the system converts the signal back to the time domain through IFFT and uses an all-pass filter for phase correction. Its transfer function is: Ha(z)=(a*+z -1 ) / (1+az -1 ) The correction parameter a can be dynamically adjusted according to the phase contrast before and after processing, and the recommended adjustment range is 0.85-0.95.

[0145] Finally, the system outputs an anti-interference signal. The signal data frame contains: timestamp, processed navigation signal I / Q value, processed IMU data and processing parameters (including key parameters of notch filter, equalizer and filter).

[0146] Step S180, performing quality index analysis on the anti-interference signal, extracting features according to the analysis results, and obtaining signal enhancement features through feature enhancement.

[0147] In one example, the steps of performing quality index analysis on the anti-interference signal, extracting features based on the analysis results, and obtaining signal enhancement features through feature enhancement include the following steps: calculating the carrier-to-noise ratio, signal integrity and phase stability of the navigation signal, and extracting features including signal amplitude envelope, instantaneous amplitude, phase change rate and power spectrum density, or average amplitude, phase standard deviation and spectrum profile, or mean, variance and peak distribution within a preset window, based on the quality level of the carrier-to-noise ratio, signal integrity and phase stability; calculating the smoothness and consistency of the IMU data, and extracting features including motion parameters, or motion trends and key change points, or basic statistical features, based on the quality level of the smoothness and consistency; using a Wiener filter, an FIR equalizer or an adaptive filter to enhance the features and obtain signal enhancement features, based on the quality level of the carrier-to-noise ratio, signal integrity and phase stability; and using a Butterworth filter, an FIR filter or a cubic spline interpolation and a Kalman filter to enhance the features and obtain signal enhancement features, based on the quality level of the smoothness and consistency.

[0148] Specifically, to counter the interference signal, the system first performs a quality index analysis. The analysis process is based on the navigation signal I / Q data and IMU data. For the navigation signal, the system calculates three basic indicators: carrier-to-noise ratio, signal integrity, and phase stability. The calculation of the carrier-to-noise ratio SNR uses SNR=10log 10 (Ps / Pn), where Ps represents signal power and Pn represents noise power. It is recommended to use a 100ms sliding window to calculate the average power and background noise in the window. The carrier-to-noise ratio threshold is recommended to be set to 13dB-17dB. Signal integrity is evaluated through the continuity of data frames. The system detects the frame interval and frame sequence number. It is recommended that the allowable interval should not exceed 0.8ms-1.2ms. Phase stability is calculated by the phase difference of the I / Q signals: , where φi is the instantaneous phase, For the average phase, it is recommended that the phase jitter be less than 0.08-0.12 radians to be considered stable.

[0149] For IMU data, the system calculates two indicators: data smoothness Sm and consistency Co. Data smoothness is evaluated by calculating the variance of 50 adjacent sampling points: Sm=1-σ² / σ²max, where σ² is the actual variance and σ²max is the maximum allowed variance. Consistency is determined by checking the continuity and change rate of the data: Co=1-|ΔV| / ΔVmax, where ΔV is the actual change rate and ΔVmax is the maximum allowed change rate. The system counts these quality indicators according to the time window of 90ms-110ms.

[0150] Based on the quality index analysis results, the system starts feature extraction. The extraction process adopts differentiated strategies for signals of different quality levels. For high-quality signal segments with a carrier-to-noise ratio higher than 20dB, the system extracts its fine features: the signal amplitude envelope is recommended to use a sampling accuracy of 0.08dB-0.12dB, and the instantaneous amplitude A(t) is extracted through Hilbert transform = , where I(t) and Q(t) are the in-phase and quadrature components. The phase change rate is recommended to be calculated with an accuracy of 0.008-0.012 radians: dφ / dt=[φ(t+Δt)-φ(t)] / Δt. The power spectral density is calculated using a frequency resolution of 0.8Hz-1.2Hz through a 2048-point FFT: P(f)=|X(f)|² / N, where X(f) is the FFT result and N is the number of FFT points.

[0151] For medium-quality signal segments with a carrier-to-noise ratio between 15dB and 20dB, the system extracts medium-precision features: the average amplitude is recommended to use a sampling accuracy of 0.4dB-0.6dB, and the mean is calculated every 8ms-12ms; the phase stability is obtained by calculating the phase standard deviation; the spectrum profile is recommended to use a frequency resolution of 4-6Hz and use a 512-point FFT calculation. For low-quality signal segments with a carrier-to-noise ratio below 15dB, the system calculates basic statistical features such as the mean, variance, and peak distribution within a 45ms-55ms window.

[0152] For IMU data, the system extracts characteristic parameters of acceleration and angular velocity based on its smoothness and consistency indicators. When the smoothness index is higher than the recommended threshold (variance is less than 8% to 12% of the standard value), the complete motion parameters are extracted; when the smoothness index is between 8% to 12% and 28% to 32% of the recommended threshold, the motion trend and key change points are extracted; when the smoothness index exceeds 28% to 32% of the recommended threshold, only basic statistical features are extracted.

[0153] After feature extraction is completed, the system enters the feature enhancement stage. The enhancement process is directly based on the above-mentioned quality analysis results and the extracted features, and a hierarchical enhancement strategy is adopted. The navigation signal enhancement adopts a three-level processing strategy: for high-quality signal segments (carrier-to-noise ratio>20dB and phase stability), the system uses a 28-36 order Wiener filter to improve signal purity, and the bandwidth is adjusted in the range of 0.08Hz-12Hz according to the power spectrum density characteristics, and the second-order phase-locked loop technology is used to optimize the phase characteristics. The loop bandwidth is adaptively matched to the phase change rate, and the recommended bandwidth range is 0.8Hz-52Hz.

[0154] For medium-quality signal segments (15dB-20dB or slight phase jitter), the system uses a 14-18th order FIR equalizer for amplitude correction, and the equalization parameters are adjusted within the range of ±5dB-7dB according to the spectrum profile characteristics; the first-order phase compensation technology is used to improve phase stability, and the compensation depth is determined by the phase stability. The recommended maximum compensation range is ±0.7-0.8π / 4; envelope detection and reconstruction are performed through Hilbert transform, and the reconstruction parameters are configured based on the average amplitude characteristics.

[0155] For low-quality signal segments (carrier-to-noise ratio <15dB or severe phase jitter), the system uses a 60-68 order adaptive filter for deep denoising. The denoising intensity is adjusted in the range of 8dB-12dB based on the statistical characteristics of the signal. Global phase reconstruction technology is used to restore phase continuity. The recommended reconstruction window length is 180ms-220ms, with a step size of 45ms-55ms. The amplitude recovery algorithm based on the minimum mean square error criterion is used to enhance signal strength.

[0156] IMU data enhancement adopts a corresponding hierarchical strategy: for data segments with high smoothness (variance less than 8% to 12% of the threshold), it is recommended to use 6-10 order Butterworth filters for noise suppression; for data segments with average smoothness but good continuity (variance between 8% to 12% and 28% to 32% of the threshold), it is recommended to use 14-18 order FIR filters for moderate smoothing; for data segments with poor quality (variance exceeding 28% to 32% of the threshold), it is recommended to restore data continuity through a combination of cubic spline interpolation and Kalman filtering. The enhancement process always maintains the physical constraint characteristics of the IMU data to ensure that the acceleration and angular velocity data satisfy the kinematic equations.

[0157] After feature enhancement processing, the system finally outputs signal enhancement features. Each feature frame contains: timestamp, enhanced signal features (including finely enhanced amplitude features, phase features and spectrum features), enhanced motion features (including acceleration and angular velocity features), and quality assessment value (recording the enhancement effect).

[0158] Step S190, reconstructing the signal enhancement features in the spatiotemporal domain, establishing a corresponding relationship between the reconstructed signal enhancement features and the carrier motion information, and outputting an anti-interference navigation signal through regional fusion.

[0159] In one example, the signal enhancement feature is reconstructed in the spatiotemporal domain, and a corresponding relationship is established between the reconstructed signal enhancement feature and the carrier motion information, and the step of outputting the anti-interference navigation signal through regional fusion includes the following steps: reorganizing the signal enhancement feature in timestamp order to reconstruct the time domain; calculating the distribution of the feature in space according to the enhanced motion feature in the signal enhancement feature to reconstruct the space domain; time-aligning the reconstructed signal enhancement feature with the carrier motion information, and on the basis of the time alignment, comparing the correlation between the instantaneous acceleration and the feature change rate in the signal enhancement feature, analyzing the corresponding relationship between the angular velocity data and the phase change in the signal enhancement feature, and evaluating the correlation between the position change and the signal strength distribution in the signal enhancement feature to establish the reconstruction. the correspondence between the signal enhancement features and the carrier motion information after the corresponding relationship is established; after completing the establishment of the corresponding relationship, the credibility index is calculated for each corresponding relationship, the area where the credibility index is within the first credibility range is marked as a high credibility area, the area where the credibility index is within the second credibility range is marked as a medium credibility area, and the area where the credibility index is within the third credibility range is marked as a low credibility area; for the high credibility area, the minimum mean square error criterion is used for optimal fusion, for the medium credibility area, the weighted average method is used for optimal fusion, and for the low credibility area, motion information is used for feature correction for optimal fusion to output an anti-interference navigation signal; the anti-interference navigation signal includes the fused navigation signal, the fused IMU data and the fusion parameters.

[0160] Specifically, for the signal enhancement features, the system first reconstructs the time and space domain. The reconstruction process uses the enhanced amplitude features, phase features and spectrum features as basic data. The reconstruction adopts a hierarchical processing strategy. The first layer is time domain reconstruction. The system reorganizes the enhanced features in timestamp order. It is recommended to use a basic processing window of 18ms-22ms, and the window overlap rate is recommended to be 70% to 80%. In each processing window, the system first reconstructs the amplitude features. Amplitude reconstruction is achieved through inverse Hilbert transform: s(t)=A(t)·cos[φ(t)], where A(t) is the reconstructed amplitude envelope and φ(t) is the phase function. Then reconstruct the phase features and use phase-locked loop technology (it is recommended that the locking bandwidth can be adjusted within the range of 8Hz-52Hz) to maintain phase continuity. Finally, the complete I / Q signal is synthesized through complex modulation: I(t)=A(t)·cos[φ(t)]Q(t)=A(t)·sin[φ(t)].

[0161] The second layer is spatial reconstruction. The system uses enhanced motion features (including acceleration and angular velocity features) to calculate the distribution of feature points in space. Spatial reconstruction uses adaptive grid division, and the grid size can be dynamically adjusted according to the change rate of motion features. It is recommended to use finer grid division (recommended 5cm-15cm) when the motion change rate is large (such as acceleration changes exceeding 1.8g / ms-2.2g / ms) to ensure that important feature areas have higher spatial resolution.

[0162] Based on the reconstruction results, the system begins to establish a correspondence with the carrier motion information in step 4. The correspondence establishment process first performs time alignment to accurately match the timestamps of the reconstructed feature points with the carrier motion information. It is recommended that the alignment accuracy be set within the range of 0.08ms-0.12ms. Then, spatial mapping is performed to map the position of the reconstructed feature points to the carrier coordinate system through the coordinate transformation matrix T: P'=T·P, where P is the original coordinate of the feature point, and P' is the mapped coordinate. The recommended update rate of the transformation matrix is ​​8Hz-12Hz.

[0163] On this basis, the system analyzes the corresponding relationship of motion parameters: compares the correlation between instantaneous acceleration and characteristic change rate, and recommends setting the correlation coefficient threshold to 0.75-0.85; analyzes the corresponding relationship between angular velocity data and phase change, and recommends setting the correlation coefficient threshold to 0.8-0.9; evaluates the correlation between position change and signal intensity distribution, and recommends setting the correlation coefficient threshold to 0.7-0.8. The system calculates the credibility index W for each corresponding relationship: W=wa·Ca+wω·Cω+wp·Cp, where Ca, Cω, and Cp are the correlation coefficients of acceleration, angular velocity, and position, respectively, and wa, wω, and wp are the corresponding weights (the recommended ranges are 0.3-0.4, 0.3-0.4, and 0.2-0.3, respectively).

[0164] After the corresponding relationship is established, the system enters the regional fusion stage. The fusion process adopts a multi-level strategy: the first is feature-level fusion, the system combines the reconstructed features with the motion features in the same time window, and the recommended fusion window length is 45ms-55ms, and the step length is 8ms-12ms. Based on the credibility index established above, the system calculates the feature correlation matrix R: R(i,j)=Σ[X(i,t)·Y(j,t)] / Where X and Y represent different types of feature sequences respectively.

[0165] It is recommended that areas with feature correlation higher than 0.75-0.85 be marked as high-confidence areas, areas with correlation between 0.45-0.75 as medium-confidence areas, and areas with correlation lower than 0.45 as low-confidence areas. Then comes parameter-level fusion. The system uses the transformation matrix and correspondence established above to adopt differentiated fusion strategies for different credible areas: high-confidence areas use the minimum mean square error criterion for optimal fusion, and the fusion weight is determined by the quality assessment value: S'=Σ(Wi·Si) / Σ(Wi) where S' is the fusion result, Si is each feature component, and Wi is the corresponding weight.

[0166] The medium-confidence area uses the weighted average method, and the weight is dynamically adjusted according to the correlation coefficient: W(r)=exp(-|1-r| / τ) where r is the correlation coefficient, τ is the feature correlation time constant, and it is recommended to set τ in the range of 0.8-1.2. The low-confidence area gives priority to using motion information for feature correction, and the correction function is: Sc=S+α(S'-S) where S is the original feature, S' is the predicted value based on motion information, and α is the correction coefficient (recommended range 0.3-0.7). The Kalman prediction method is used to obtain the predicted value, and the prediction is made by constructing a state vector X(k) containing signal features and motion parameters. The system uses the state transfer equation X(k|k-1)=F(k)X(k-1)+B(k)u(k) to implement the prediction process, where F(k) represents the state transfer matrix, which describes the evolution law of the system state, and B(k) and u(k) are the control matrix and control input, respectively, which are used to introduce constraints on the carrier motion model. Based on the statistical analysis of historical data, the system establishes the mapping relationship H between signal features and motion states, thereby obtaining the predicted signal feature value S'=H·X(k|k-1). This prediction mechanism makes full use of the continuity characteristics of the carrier movement. The prediction window length is recommended to be set to 80ms-120ms, which can effectively improve the accuracy of feature correction.

[0167] Finally, the system outputs anti-interference navigation signals through regional fusion. The output data frame structure fully records the processed navigation signal I / Q data, fused IMU data and fusion parameters. These high-quality navigation signals that have undergone spatiotemporal reconstruction, feature fusion and parameter optimization have effectively improved the performance of the navigation system in complex environments.

[0168] In a specific application scenario, when a car is driving in a complex urban environment, the navigation signal is often affected by various factors such as reflection from high-rise buildings and electromagnetic interference. In order to ensure navigation accuracy, the system first needs to simultaneously obtain the navigation signal and IMU data from the on-board navigation receiver. Through the FPGA-based dual-channel parallel acquisition architecture, the system accurately synchronizes the two data channels to ensure that the information recorded is at the same time, and obtains a synchronized data stream under a unified time reference.

[0169] When a vehicle encounters speed bumps, sharp turns, or uneven roads during driving, data sampling may be uneven. The system will handle these sampling intervals through dynamic period compensation, and increase the density of sampling points at critical moments when the vehicle accelerates, turns, and other movements change dramatically, forming a more complete and continuous supplementary data stream.

[0170] The system needs to evaluate the reliability of these supplementary data. For example, when a vehicle is driving on an open road, the navigation signal quality is high, but when passing under an overpass or in a tunnel, the signal quality may be significantly reduced. The system establishes a multi-dimensional weight matrix to weight data of different quality levels and obtain a compensation signal containing reliability information.

[0171] At the same time, the system processes the IMU data, analyzes the vehicle's acceleration, angular velocity and other motion parameters, and combines temperature compensation and zero bias calibration to accurately obtain the vehicle's motion status information.

[0172] The system conducts in-depth analysis of the obtained compensation signal. For example, when the vehicle is traveling at high speed, the Doppler frequency shift will cause the signal spectrum to change; when entering an urban area, the reflection of surrounding buildings will cause abnormal energy distribution. The system extracts these characteristic information through time-frequency analysis to form a complete spectrum characteristic record.

[0173] By associating these spectral features with vehicle motion information, the system can accurately distinguish signal changes from different sources. For example, when a spectrum mutation is detected, the system will compare the current vehicle motion state to determine whether the change is caused by external interference or the vehicle's own motion, thereby identifying the true interference feature.

[0174] Based on the identified interference features, the system configures the corresponding anti-interference parameters. For example, for multipath interference caused by building reflections, the system adjusts the notch filter parameters; for broadband interference, an adaptive equalization strategy is used to obtain a preliminary anti-interference signal through these parameter processing.

[0175] To further improve the signal quality, the system adopts a hierarchical enhancement strategy. During the driving process of the vehicle, the system flexibly adjusts the processing strategy according to the signal quality in different areas. For example, a fine enhancement method is used for high-quality signal areas, while a stronger enhancement method is used in areas with poor signal quality, ultimately obtaining a stable signal enhancement feature.

[0176] Finally, the system fuses the enhanced signal features with the vehicle motion information. Through spatiotemporal reconstruction and regional fusion, the system establishes an accurate correspondence between signal features and motion states, outputs high-quality anti-interference navigation signals, and ensures that vehicles can maintain stable navigation accuracy in complex environments such as under viaducts, in tunnels, or in urban canyons.

[0177] The IMU-assisted navigation anti-interference method provided in each embodiment of the present application effectively improves the anti-interference performance of the system in complex environments through the deep fusion of data weight classification and weighted operations, achieves a comprehensive improvement in signal quality through the optimization configuration of anti-interference parameters and feature enhancement processing, and effectively solves the navigation signal processing problem in complex environments through the organic combination of spatiotemporal domain reconstruction and regional fusion.

[0178] This application designs an adaptive fusion mechanism based on a multidimensional weight matrix. Through the combined weight allocation of carrier-to-noise ratio, time characteristics, motion characteristics and compensation characteristics, a dynamic optimization strategy for signal processing is established. This mechanism can not only perform adaptive processing according to signal quality, but also accurately distinguish interference effects from motion effects, effectively avoiding the problem of misjudging normal motion as interference in traditional methods. This mechanism effectively improves the anti-interference performance of the system in complex environments through the deep fusion of data weight classification and weighted operations.

[0179] This application proposes a hierarchical enhancement method driven by quality indicators. The system uses different orders of Wiener filtering, FIR equalizer and phase-locked loop parameter configuration according to indicators such as carrier-to-noise ratio, signal integrity and phase stability to achieve differentiated processing of signals of different qualities. This method significantly improves the accuracy of signal processing while ensuring computational efficiency. This application achieves a comprehensive improvement in signal quality through anti-interference parameter optimization configuration and feature enhancement processing.

[0180] This application establishes an innovative spatiotemporal processing architecture. By designing a feature correlation matrix and a credibility index system, the system achieves accurate identification and differentiated processing of different credible areas. At the same time, the introduction of a prediction and correction mechanism based on motion information significantly improves the system's anti-interference ability. This architecture effectively solves the problem of navigation signal processing in complex environments through the organic combination of spatiotemporal reconstruction and regional fusion.

[0181] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0182] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0183] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0184] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A navigation anti-interference method based on IMU assistance, characterized in that: The following steps are involved: Collecting navigation signals and IMU data, and performing time domain sampling and time synchronization processing on the navigation signals and the IMU data to obtain a synchronous data stream; Performing periodic compensation processing on the synchronous data stream, adding key sampling points in the compensated synchronous data stream, and forming a supplementary data stream through compensation calculation; Dividing the supplementary data stream into weight levels according to data features, allocating calculation parameters according to the weight levels, and performing calculations on the supplementary data stream according to the calculation parameters to obtain a compensation signal; Performing IMU data acquisition and validity analysis on the synchronous data stream, and obtaining carrier motion information through calibration processing and motion information extraction; Performing time-frequency domain conversion on the compensation signal, extracting time-frequency features from the converted compensation signal, and performing feature analysis on the time-frequency features to obtain signal spectrum features; Performing abnormal feature recognition on the signal spectrum feature, comparing the recognition result with the carrier motion information, and obtaining interference features through correlation analysis; Determine an anti-interference parameter according to the interference feature, and perform parameter processing on the compensation signal using the anti-interference parameter to obtain an anti-interference signal; Performing quality index analysis on the anti-interference signal, performing feature extraction according to the analysis result, and obtaining a signal enhancement feature by feature enhancement; The signal enhancement features are reconstructed in the spatiotemporal domain, a corresponding relationship is established between the reconstructed signal enhancement features and the carrier motion information, and an anti-interference navigation signal is output through regional fusion.

2. The IMU-assisted navigation anti-interference method according to claim 1, characterized in that: The steps of collecting navigation signals and IMU data, and performing time domain sampling and time synchronization processing on the navigation signals and the IMU data to obtain a synchronous data stream include the following steps: The navigation signal is coherently integrated once at intervals of a first preset time length by using segmented coherent integration to obtain in-phase data and orthogonal data, and the IMU data is sampled once at intervals of a second preset time length by using a fixed-frequency equal-interval sampling strategy to obtain IMU sampling data, so as to complete time domain sampling of the navigation signal and the IMU data; Generate corresponding timestamps for the data sampling points of the navigation signal and the IMU data, and resample the IMU sampled data using a cubic spline interpolation algorithm based on the sampling frequency difference between the in-phase data and the orthogonal data to obtain IMU resampled data, so as to complete the time synchronization processing of the navigation signal and the IMU data; The timestamp, the in-phase data, the orthogonal data and the IMU re-sampled data are encapsulated to obtain the synchronous data stream.

3. The IMU-assisted navigation anti-interference method according to claim 2, characterized in that: The step of performing periodic compensation processing on the synchronous data stream, adding key sampling points in the compensated synchronous data stream, and forming a supplementary data stream through compensation calculation includes the steps of: Taking the timestamp in the synchronous data stream as a reference, a sliding time window is used to analyze the time interval between adjacent data frames in the synchronous data to obtain a time correction amount for each data frame in the synchronous data; According to the time correction amount, interpolation processing is performed on the in-phase data, the orthogonal data and the IMU re-collected data in the synchronous data; Adding key sampling points to the interpolation processing result, performing data compensation calculation on the key sampling points, and organizing the information of the key sampling points, the compensation calculation results and the synchronous data stream into the supplementary data stream; The information of the key sampling point includes a key sampling point type flag and a key sampling point description field; The compensation calculation result includes a compensation parameter field.

4. The IMU-assisted navigation anti-interference method according to claim 3, characterized in that: The step of dividing the supplementary data stream into weight levels according to data features, allocating calculation parameters according to the weight levels, and calculating the supplementary data stream according to the calculation parameters to obtain a compensation signal comprises the following steps: Read the key sampling point type flag, key sampling point description field and compensation parameter field of each data frame in the supplementary data stream, and perform feature analysis in combination with the in-phase data, orthogonal data and IMU re-sampling data in the supplementary data stream; the feature analysis includes signal quality feature analysis, motion feature analysis, time feature analysis and compensation feature analysis; Based on the characteristic analysis results, a multidimensional weight matrix is ​​established, and the comprehensive weight of each data frame in the supplementary data stream is calculated according to the multidimensional weight matrix and the characteristic weight adjustment factor; The feature weight adjustment factors include a signal quality feature weight adjustment factor, a motion feature weight adjustment factor, a time feature weight adjustment factor and a compensation feature weight adjustment factor; According to the comprehensive weight, the calculation parameters are allocated, and the calculation parameters are weighted to obtain the compensation signal; the compensation signal includes a timestamp, weighted in-phase data, weighted quadrature data, weighted IMU re-sampled data, a weight description field and a weighted processing parameter field.

5. The IMU-assisted navigation anti-interference method according to claim 1, characterized in that: The steps of acquiring IMU data and analyzing validity of the synchronous data stream, and obtaining carrier motion information through calibration processing and motion information extraction include the following steps: In the process of acquiring IMU data by the synchronous data stream, a time series of the IMU data is established according to the timestamps in the synchronous data stream, a difference between adjacent timestamps is calculated to evaluate the continuity of the time series, and an integrity check is performed on each data frame in the synchronous data stream; During the validity analysis of the synchronous data stream, the acceleration data and the angular velocity data of the IMU data are analyzed for validity; During the calibration process of the synchronous data stream, temperature compensation, zero bias calibration, scale factor calibration, inter-axis error calibration and dynamic error calibration are performed according to the effectiveness analysis result; In the process of extracting motion information from the synchronous data stream, the gravity component and the motion acceleration component of the acceleration data in the calibrated synchronous data stream are separated, the angular velocity data in the calibrated synchronous data stream is integrated and calculated, and based on the separation result and the integration calculation result, the carrier motion information is obtained; the carrier motion information includes timestamp, three-dimensional position coordinates, three-dimensional velocity vector, three-dimensional attitude angle, acceleration, angular velocity, acceleration change rate, angular velocity change rate and velocity change rate.

6. The IMU-assisted navigation anti-interference method according to claim 1, characterized in that: The steps of performing time-frequency domain conversion on the compensation signal, extracting time-frequency features from the converted compensation signal, and performing feature analysis on the time-frequency features to obtain signal spectrum features include the following steps: Performing time-frequency domain conversion on the compensation signal by using short-time Fourier transform and wavelet transform; Based on the energy distribution characteristics in the short-time Fourier transform result, obtaining the frequency spectrum of the compensation signal; Based on the frequency spectrum of the compensation signal, calculating the cross-spectrum of the in-phase data and the orthogonal data of the navigation signal to obtain a phase difference feature; The instantaneous phase and frequency characteristics of the navigation signal are calculated by Hilbert transform, the amplitude ratio characteristics of the acceleration data and the angular velocity data of the IMU data are calculated based on the frequency spectrum of the compensation signal, and the energy distribution of the IMU data is calculated based on the wavelet transform result to complete the extraction of the time-frequency characteristics; The time-frequency features are subjected to feature analysis to obtain signal spectrum features, wherein the signal spectrum features include time stamp identification features, energy distribution features, shape features, dynamic features, phase features, and wavelet features.

7. The IMU-assisted navigation anti-interference method according to claim 6, characterized in that: The step of identifying abnormal features of the signal spectrum features, comparing the identification results with the carrier motion information, and obtaining interference features through correlation analysis includes the following steps: Identify energy anomaly points of energy distribution features, shape anomaly points of shape features, and dynamic anomaly points of dynamic features in the signal spectrum features, and organize the energy anomaly points, the shape anomaly points, and the dynamic anomaly points in chronological order to form an anomaly feature sequence; Matching the timestamp of the abnormal feature sequence with the timestamp of the carrier motion information to establish time alignment; On the basis of time alignment, the abnormal feature sequence is compared with the carrier motion information, the energy abnormal point is analyzed based on the acceleration change rate and the angular velocity change rate to determine whether the energy abnormal point is an abnormality related to a drastic change in motion, the shape abnormal point is analyzed based on the three-dimensional posture angle change to determine whether the shape abnormal point is an abnormality related to a drastic change in motion, and the dynamic abnormal point is analyzed based on the velocity change rate to determine whether the dynamic abnormal point is an abnormality related to a drastic change in motion; Based on the comparison result, correlation analysis is performed on the energy abnormal point, the shape abnormal point and the dynamic abnormal point to obtain corresponding correlation coefficients, and when the correlation coefficients of the energy abnormal point, the shape abnormal point and the dynamic abnormal point are all lower than the corresponding threshold range, the corresponding abnormal feature in the abnormal feature sequence is marked as a potential interference source; Performing feature quantification analysis on the potential interference source to obtain the interference feature; the interference feature includes a timestamp, an abnormal feature quantization value, a motion parameter, and a correlation coefficient.

8. The IMU-assisted navigation anti-interference method according to claim 7, characterized in that: The step of determining an anti-interference parameter according to the interference feature, and performing parameter processing on the compensation signal using the anti-interference parameter to obtain an anti-interference signal comprises the following steps: Based on the quantized value of the abnormal feature in the interference feature, for the energy abnormal interference feature in the interference feature, the suppression depth is set according to the signal-to-noise ratio and the bandwidth; for the shape abnormal interference feature in the interference feature, the equalization parameter is configured according to the degree of spectrum distortion; for the dynamic abnormal interference feature in the interference feature, the tracking bandwidth is set according to the frequency hopping characteristics; Based on the suppression depth, the equalization parameter and the tracking bandwidth, an anti-interference algorithm configuration is established for the interference feature to configure a notch filter parameter and an adaptive equalizer parameter in a frequency domain suppression algorithm; and the interference parameters are composed based on the suppression depth, the equalization parameter, the tracking bandwidth, the notch filter parameter and the adaptive equalizer parameter; The anti-interference parameter is used to perform parameter processing on the compensation signal to obtain an anti-interference signal.

9. The IMU-assisted navigation anti-interference method according to claim 1, characterized in that: The steps of performing quality index analysis on the anti-interference signal, extracting features according to the analysis results, and obtaining signal enhancement features by feature enhancement include the following steps: Calculating the carrier-to-noise ratio, signal integrity and phase stability of the navigation signal, and extracting features including signal amplitude envelope, instantaneous amplitude, phase change rate and power spectrum density, or average amplitude, phase standard deviation and spectrum profile, or mean, variance and peak distribution within a preset window according to the quality levels of the carrier-to-noise ratio, the signal integrity and the phase stability; Calculating the smoothness and consistency of the IMU data, and extracting features including motion parameters, or motion trends and key change points, or basic statistical features according to the quality levels of the smoothness and the consistency; According to the quality level of the carrier-to-noise ratio, the signal integrity and the phase stability, a Wiener filter, a FIR equalizer or an adaptive filter is used to perform feature enhancement to obtain the signal enhancement feature; According to the quality level of the smoothness and the consistency, a Butterworth filter, a FIR filter or a cubic spline interpolation and a Kalman filter are used to perform feature enhancement to obtain the signal enhancement feature.

10. The IMU-assisted navigation anti-interference method according to claim 1, characterized in that: The step of reconstructing the signal enhancement feature in the spatiotemporal domain, establishing a corresponding relationship between the reconstructed signal enhancement feature and the carrier motion information, and outputting the anti-interference navigation signal through regional fusion includes the following steps: The signal enhancement features are reorganized in a time stamp order to perform time domain reconstruction; the distribution of features in space is calculated according to the enhanced motion features in the signal enhancement features to perform spatial domain reconstruction; The reconstructed signal enhancement feature is time-aligned with the carrier motion information, and based on the time alignment, the correlation between the instantaneous acceleration and the feature change rate in the signal enhancement feature is compared, the corresponding relationship between the angular velocity data and the phase change in the signal enhancement feature is analyzed, and the correlation between the position change and the signal intensity distribution in the signal enhancement feature is evaluated, so as to establish the corresponding relationship between the reconstructed signal enhancement feature and the carrier motion information; After the corresponding relationships are established, a credibility index is calculated for each of the corresponding relationships, and an area of ​​the credibility index within a first credibility range is marked as a high credibility area, an area of ​​the credibility index within a second credibility range is marked as a medium credibility area, and an area of ​​the credibility index within a third credibility range is marked as a low credibility area; For the high-credible area, the minimum mean square error criterion is used for optimal fusion, for the medium-credible area, the weighted average method is used for optimal fusion, and for the low-credible area, motion information is used for feature correction and optimal fusion to output the anti-interference navigation signal; The anti-interference navigation signal includes the fused navigation signal, the fused IMU data and fusion parameters.

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