FMCW radar ranging method and device

Abstract

The invention discloses an FMCW radar range finding method and device, the device comprises a data acquisition unit, a preprocessing unit, a core processing unit and a call management unit, a range finding experiment is carried out through a dual-band radar joint model, so that the range finding error of radar sensors of two bands is evaluated, and the range finding error of the radar sensors of the two bands is evaluated; interference factor information is introduced to be combined with distance measurement errors to carry out interference influence function analysis, so that the influence degree of the interference factor information on the distance measurement errors of the radar sensors of the two frequency bands is evaluated, targeted distance measurement correction is carried out, and the dual-frequency-band technology is used in a combined mode; therefore, the frequency range radar sensor adapting to the current ranging application scene is preferably selected and automatically called, or double-radar fusion ranging is synchronously called, finally, the precision and anti-interference capability of FMCW radar ranging are improved, and the robustness and accuracy of the whole device are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of radar ranging, and in particular to a method and device for FMCW radar ranging. Background Art

[0002] FMCW stands for frequency modulated continuous wave. FMCW technology is used in high-precision radar ranging. Its basic principle is that the transmitted wave is a high-frequency continuous wave, and the frequency changes over time according to the law of triangular waves. FMCW (frequency modulated continuous wave) radar is a radar system widely used in ranging, speed measurement and imaging, especially in the fields of automobiles, drones and meteorological monitoring. Radar ranging will be interfered by application scenario factors, including electromagnetic frequency band, noise frequency and meteorological parameters. In actual application scenarios, radars of different frequency bands are subject to different interferences. For example, millimeter-wave radars are susceptible to electromagnetic interference of similar frequencies, as well as weather interferences of rain, snow, and haze; while ultrasonic radars are susceptible to low-frequency noise interference and environmental interferences of airflow and wind speed. Therefore, the existing FMCW radar ranging will have the defect of insufficient anti-interference ability when using radar ranging of any frequency band, and the resulting radar ranging precision is insufficient, the robustness is poor, and the accuracy is low. In view of the above technical defects, a solution is now proposed. Summary of the invention

[0003] The purpose of the present invention is to solve the defects of insufficient anti-interference ability in the prior art, and the problems of insufficient radar ranging precision, poor robustness and low accuracy caused by the above problems. The present invention constructs a dual-band radar joint model and conducts ranging experiments to evaluate the ranging errors of radar sensors of two frequency bands, and then introduces interference factor information to combine with the ranging error to perform interference influence function analysis, so as to evaluate the influence of the interference factor information on the ranging errors of radar sensors of the two frequency bands, and performs targeted ranging correction. By jointly using the dual-band technology, the frequency band radar sensor adapted to the current ranging application scenario is preferably selected and automatically called, or the dual radar fusion ranging is performed synchronously, so as to finally improve the ranging precision and anti-interference ability of the FMCW radar, and improve the robustness and accuracy of the overall device.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: A method for FMCW radar ranging, comprising the following steps: Step 1: Build a dual-band radar joint model: Set up a radar sensor group to perform a ranging experiment. The radar sensor group includes two radar sensors with different frequency bands. The two different frequency bands are marked as frequency band Ψ1 and frequency band Ψ2, respectively. A radar ranging simulation scene is established and simulated scene data is collected. Then, the radar sensor group is used to perform ranging in the simulated scene to obtain ranging experiment results. The simulated scene data includes the ranging preset value A0 and interference factor information. Step 2: Evaluate the ranging error of the dual-band radar joint model: compare the ranging preset value A0 with the ranging experiment result to evaluate the ranging error of the radar sensor group; Step 3: Evaluate the influence of interference factors on ranging error and make ranging correction: construct an interference influence function between interference factor information and ranging error, so as to evaluate the degree of radar ranging error through interference factor information and generate corresponding ranging correction coefficient; Step 4: Automatically call the radar sensor with the optimal frequency band: Apply the dual-band radar joint model to the actual scene, collect the interference factor information of the actual scene, and then substitute it into the interference influence function to evaluate the radar ranging error of the actual scene, so as to determine the priority of the radar sensors in two different frequency bands, and select the radar sensor with a higher priority for ranging operation, so as to generate and output the ranging correction result; Step 5: Compare and judge to perform radar fusion ranging: set the threshold of radar ranging error. When the radar ranging errors of two radar sensors in different frequency bands exceed the corresponding threshold, radar fusion ranging is performed to correct and integrate the actual ranging results obtained by the radar sensor group, thereby generating and outputting the ranging verification result. Step 6: Refresh the radar ranging mode regularly: collect interference factor information regularly through regular feedback management instructions to refresh the radar call signal.

[0005] Furthermore, the specific process of evaluating the ranging error of the dual-band radar joint model is as follows: Sa1, the ranging experiment result includes the ranging experiment values ​​of the radar sensor of frequency band Ψ1 and the radar sensor of frequency band Ψ2, wherein the ranging experiment value of the radar sensor of frequency band Ψ1 is marked as B1; and the ranging experiment value of the radar sensor of frequency band Ψ2 is marked as B2; Sa2, establish the error analysis sub-model, the specific process is: Sa2-1, input N0 control data groups into the error analysis sub-model, and any data control group includes a preset value a and an experimental value b; Sa2-2, obtain the deviation comparison value through the preset value a and the experimental value b , thereby calculating the deviation comparison value of N0 control data groups The variance value of ; Sa2-3, by deviation comparison value and the difference factor , obtain the deviation coefficient η; Sa2-4, error analysis sub-model output deviation coefficient η; Sa3, setting the number of ranging experiments of the radar sensor group to N1, and obtaining N1 ranging data sets; Sa3-1, the ranging data set includes the ranging preset value A0, the ranging experimental value B1 and the ranging experimental value B2; The preset distance measurement value A0 and the experimental distance measurement value B1 are integrated and marked as the frequency band Ψ1 distance measurement data group; The preset distance measurement value A0 and the distance measurement experimental value B2 are integrated and marked as the frequency band Ψ2 distance measurement data group; Sa3-2, substitute the N1 frequency band Ψ1 ranging data groups into the error analysis sub-model, output the deviation index η1 of the frequency band Ψ1, and thus evaluate the ranging error degree of the frequency band Ψ1 radar sensor; Sa3-3, substitute the N1 frequency band Ψ2 ranging data groups into the error analysis sub-model, output the deviation index η2 of the frequency band Ψ2, and thus evaluate the ranging error degree of the frequency band Ψ2 radar sensor; Sa4, set the ranging error evaluation interval, and compare the deviation indexes η1 and η2 with the ranging error evaluation interval to evaluate the degree of ranging error.

[0006] Furthermore, the specific process of evaluating the influence of interference factors on ranging error is as follows: Sb1, interference factor information includes electromagnetic frequency band, noise frequency and meteorological parameters; wherein meteorological parameters include rainfall, snowfall, wind speed and haze visibility; preset interference factor information includes M0 interference factor indicators; Mark any interference factor index as Gi, set the information collection period Tc to collect the simulation scene data regularly, obtain the values ​​of M0 interference factor indicators, and integrate the values ​​of M0 interference factor indicators in any information collection period Tc to generate an interference factor vector Rg; Sb2, mark the number of information collection cycles Tc as m1, and construct the interference factor index matrix P through the interference factor vectors Rg of m1 information collection cycles Tc; According to the information collection period Tc, the deviation index η1 of the frequency band Ψ1 and the deviation index η2 of the frequency band Ψ2 are synchronously calculated to obtain the deviation index η1 of the frequency band Ψ1 and the deviation index η2 of the frequency band Ψ2 corresponding to m1 information collection periods Tc; Sb3, integrate the deviation index η1 of the frequency band Ψ1 corresponding to m1 information collection cycles Tc into a deviation vector No. 1 C1; The deviation index η2 of the frequency band Ψ2 corresponding to the m1 information collection period Tc is integrated into the second deviation vector C2; Sb4, establish the interference analysis sub-model, the specific process is as follows: Sb4-1, input m0 association vector groups into the interference analysis sub-model, any association vector group includes an interference factor vector Inte and a deviation index vector Devi; Sb4-2, using the interference factor vector Inte as the horizontal coordinate and the deviation index vector Devi as the vertical coordinate, constructs the correlation curve Sde through m0 correlation vector groups, and generates the fitting function Fs of the correlation curve Sde; Sb4-3, the interference analysis sub-model outputs the fitting function Fs of the association vector group; Sb5, through the interference factor indicator matrix P, the first deviation vector C1 and the second deviation vector C2, are substituted into the interference analysis sub-model to evaluate the interference factor information on the radar ranging error degree of the frequency band Ψ1 and the frequency band Ψ2 respectively.

[0007] Furthermore, the specific process of generating the ranging correction coefficient is: Sc1, extract any column vector of the interference factor indicator matrix P and mark it as the periodic numerical vector GP of the interference factor indicator Gi; Sc2, integrate the periodic numerical vector GP of the interference factor index Gi and the first deviation vector C1 and mark them as the first associated vector group of the interference factor index Gi; then substitute them into the interference analysis sub-model, output the first error influence function Fgi1 of the interference factor index Gi, so as to evaluate the radar ranging error degree of the frequency band Ψ1 radar sensor through the interference factor index Gi; Through the first error influence function Fgi1 of the M0 interference factor indicators, the comprehensive interference influence function FG1 of the interference factor information on the frequency band Ψ1 radar sensor is obtained, and then the ranging correction coefficient H1 between the interference factor information and the frequency band Ψ1 radar sensor is obtained; Sc3, integrate the periodic numerical vector GP of the interference factor index Gi and the second deviation vector C2 into a second associated vector group of the interference factor index Gi; then substitute them into the interference analysis sub-model, output the second error influence function Fgi2 of the interference factor index Gi, so as to evaluate the radar ranging error degree of the frequency band Ψ2 radar sensor through the interference factor index Gi; Through the second error influence function Fgi2 of the M0 interference factor indicators, the comprehensive interference influence function FG2 of the interference factor information on the frequency band Ψ2 radar sensor is obtained, and then the ranging correction coefficient H2 between the interference factor information and the frequency band Ψ2 radar sensor is obtained.

[0008] Furthermore, the specific process of evaluating the radar ranging error in the actual scene is as follows: Sd1, the dual-band radar joint model is applied to the actual scene, the interference factor information of the actual scene is collected and marked as the interference factor information set W, and any interference factor indicator data of the interference factor information set W is marked as ; Sd2, obtains the actual deviation coefficient of the interference factor index Gi in the frequency band Ψ1 through the first error influence function Fgi1 of the interference factor index Gi , and similarly obtain the actual deviation coefficient of all interference factor indicators in the frequency band Ψ1; Then, the actual comprehensive interference influence function value of the interference factor information on the frequency band Ψ1 radar sensor is obtained through the comprehensive interference influence function FG1 of the interference factor information on the frequency band Ψ1 radar sensor , evaluate the radar ranging error degree of the frequency band Ψ1 radar sensor in actual scenes; Sd3, obtains the actual deviation coefficient of the interference factor index Gi in the frequency band Ψ2 through the second error influence function Fgi2 of the interference factor index Gi , and similarly obtain the actual deviation coefficient of all interference factor indicators in the frequency band Ψ2; Then, the actual comprehensive interference influence function value of the interference factor information on the frequency band Ψ2 radar sensor is obtained through the comprehensive interference influence function FG2 of the interference factor information on the frequency band Ψ2 radar sensor , evaluate the radar ranging error degree of frequency band Ψ2 radar sensor in actual scenarios.

[0009] Furthermore, the specific process of determining and generating the radar call signal is as follows: By comparing the actual comprehensive interference impact function value and , thereby determining the priority of two radar sensors in different frequency bands: when < When , it is determined that the frequency band Ψ1 radar sensor has a high priority, and a radar call signal No. 1 is generated and output, and the frequency band Ψ1 radar sensor is selected for ranging operation; when > When , it is determined that the frequency band Ψ2 radar sensor has a high priority, and the second radar call signal is output, and the frequency band Ψ2 radar sensor is selected for ranging operation; Set the threshold of radar ranging error to Q. When the actual comprehensive interference affects the function value >Q and the actual comprehensive interference affects the function value >Q, the No. 3 radar call signal is generated and output, and radar fusion ranging is performed.

[0010] A FMCW radar ranging device, comprising a data acquisition unit, a preprocessing unit, a core processing unit and a call management unit, wherein the data acquisition unit, the preprocessing unit, the core processing unit and the call management unit are communicatively connected, and the device applies the above-mentioned FMCW radar ranging method; The data acquisition unit is used to collect interference factor information of radar application scenarios; The pre-processing unit is used to analyze the interference factor information and evaluate the radar ranging error; The core processing unit is used to build a dual-band radar joint model, evaluate the impact of interference factors on ranging errors and generate ranging correction functions, and then generate corresponding radar call signals by analyzing the degree of radar ranging errors. The actual ranging results of the radar sensor are corrected by the ranging correction function, thereby obtaining and outputting the ranging results of the dual-band radar joint model. The call management unit is used to receive radar call signals, automatically call the radar sensor with the optimal frequency band, and perform radar fusion ranging operations in special application scenarios, and feedback management instructions to the data acquisition unit to perform regular collection of interference factor information, thereby realizing the refresh of the radar call signal.

[0011] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: The present invention firstly performs ranging experiments through a dual-band radar joint model, thereby evaluating the ranging errors of radar sensors of two frequency bands, and then introduces interference factor information and combines it with the ranging error to perform interference influence function analysis, thereby evaluating the influence degree of the interference factor information on the ranging errors of radar sensors of the two frequency bands, and performs targeted ranging correction. By dynamically selecting and jointly using dual-band technology, the frequency band radar sensor adapted to the current ranging application scenario is preferably selected and automatically called, or the dual radar fusion ranging is performed synchronously, thereby finally improving the ranging accuracy and anti-interference capability of the FMCW radar, and improving the robustness and accuracy of the overall device. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic diagram showing the steps of the overall method of the present invention is shown; Figure 2 A flow chart showing the dual-band radar joint model of the present invention is shown; Figure 3 A connection diagram of the system modules of the present invention is shown. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] Embodiment 1: like Figure 1-Figure 3 As shown, a method for FMCW radar ranging includes the following steps: S1, building a dual-band radar joint model: setting up a radar sensor group to perform a ranging experiment, the radar sensor group includes two radar sensors in different frequency bands, marking the two different frequency bands as frequency band Ψ1 and frequency band Ψ2, establishing a radar ranging simulation scene and collecting simulation scene data, and then performing a simulated scene ranging through the radar sensor group to obtain ranging experiment results; the simulation scene data includes a ranging preset value A0 and interference factor information; Simulated scene ranging refers to the experimental process of applying the radar sensor group to the radar ranging simulation scene for synchronous ranging; The ranging experiment results include the ranging experiment values ​​of the frequency band Ψ1 radar sensor and the frequency band Ψ2 radar sensor; Among them, the ranging experimental value of the radar sensor in the frequency band Ψ1 is marked as B1; the ranging experimental value of the radar sensor in the frequency band Ψ2 is marked as B2; For example, a millimeter wave radar with a frequency band of 24 GHz or 77 GHz and an ultrasonic sensor with a frequency band of 40 kHz-200 kHz are selected as the radar sensor group; the interference factor information includes electromagnetic frequency band, noise frequency and meteorological parameters; wherein the meteorological parameters include rainfall, snowfall, wind speed and haze visibility; the preset interference factor information includes M0 interference factor indicators, and the interference factor indicators include electromagnetic frequency band, noise frequency, rainfall, snowfall, wind speed and haze visibility; S2, evaluate the ranging error of the dual-band radar joint model: compare the ranging preset value A0 with the ranging experiment results to evaluate the ranging error of the radar sensor group; S2-1, establish the error analysis sub-model, the specific process is: Inputting N0 control data groups into the error analysis sub-model, any data control group includes a preset value a and an experimental value b; Obtain the deviation comparison value by using the preset value a and the experimental value b : , thereby calculating the deviation comparison value of N0 control data groups The variance value of : ,in, ; By comparing the deviation and the difference factor , get the deviation coefficient η: , when the deviation comparison value and the difference factor The higher it is, the higher the coefficient of deviation η is, indicating that the degree of difference among the N0 control data groups is higher, and the degree of error in evaluating the control data groups is higher; The error analysis sub-model outputs the deviation coefficient η; S2-2, setting the number of ranging experiments of the radar sensor group to N1, and obtaining N1 ranging data sets; The ranging data set includes a ranging preset value A0, a ranging experimental value B1 and a ranging experimental value B2; The preset distance measurement value A0 and the experimental distance measurement value B1 are integrated and marked as the frequency band Ψ1 distance measurement data group; the preset distance measurement value A0 and the experimental distance measurement value B2 are integrated and marked as the frequency band Ψ2 distance measurement data group; Substitute the N1 frequency band Ψ1 ranging data groups into the error analysis sub-model, compare the ranging preset value A0 with the ranging experimental value B1, output the corresponding deviation coefficient and mark it as the deviation index η1 of the frequency band Ψ1, so as to evaluate the ranging error degree of the frequency band Ψ1 radar sensor; Substitute the N1 frequency band Ψ2 ranging data groups into the error analysis sub-model, compare the ranging preset value A0 with the ranging experimental value B2, output the corresponding deviation coefficient and mark it as the deviation index η2 of the frequency band Ψ2, so as to evaluate the ranging error degree of the frequency band Ψ2 radar sensor; Set the ranging error evaluation interval and evaluate the ranging error degree by comparing the intervals; S3, evaluate the influence of interference factors on ranging error and make ranging correction: construct the interference influence function between interference factor information and ranging error, so as to evaluate the degree of radar ranging error through interference factor information and generate corresponding ranging correction coefficient. The specific process is as follows: S3-1, mark any interference factor index as Gi, set the information collection period Tc to collect the simulation scene data regularly, obtain the values ​​of M0 interference factor indicators, and integrate the values ​​of M0 interference factor indicators in any information collection period Tc to generate an interference factor vector Rg: ; Where i refers to the serial number of the interference factor index; S3-2, mark the number of information collection cycles Tc as m1, and construct the interference factor index matrix P through the interference factor vectors Rg of m1 information collection cycles Tc: ; in, It refers to the value of the interference factor index Gi in the jth information collection period Tc; According to the information collection period Tc, the deviation index η1 of the frequency band Ψ1 and the deviation index η2 of the frequency band Ψ2 are synchronously calculated to obtain the deviation index η1 of the frequency band Ψ1 and the deviation index η2 of the frequency band Ψ2 corresponding to m1 information collection periods Tc; S3-3, integrate the deviation index η1 of the frequency band Ψ1 corresponding to the m1 information collection cycles Tc into the first deviation vector C1: ; The deviation index η2 of the frequency band Ψ2 corresponding to the m1 information collection period Tc is integrated into the second deviation vector C2: ; S3-4, establish the interference analysis sub-model, the specific process is as follows: Input m0 association vector groups into the interference analysis sub-model, any association vector group includes an interference factor vector Inte and a deviation index vector Devi; With the interference factor vector Inte as the horizontal coordinate and the deviation index vector Devi as the vertical coordinate, the correlation curve Sde is constructed through m0 correlation vector groups and the fitting function Fs is generated: , substitute the interference factor vector Inte into the fitting function Fs to obtain the corresponding deviation index vector Devi; The interference analysis sub-model outputs the fitting function Fs of the associated vector group; S3-5, extract any column vector of the interference factor indicator matrix P and mark it as a periodic numerical vector GP of the interference factor indicator Gi; S3-501, integrating the periodic numerical vector GP and the first deviation vector C1 of the interference factor index Gi and marking them as the first associated vector group of the interference factor index Gi; then substituting the first associated vector group of the interference factor index Gi into the interference analysis sub-model, outputting the corresponding fitting function and marking it as the first error influence function Fgi1 of the interference factor index Gi, so as to evaluate the radar ranging error degree of the frequency band Ψ1 radar sensor through the interference factor index Gi; Through the first error influence function Fgi1 of M0 interference factor indicators, the comprehensive interference influence function FG1 of the interference factor information on the frequency band Ψ1 radar sensor is obtained: ; in, It refers to the deviation weight proportion of the interference factor index Gi in the frequency band Ψ1. The comprehensive interference influence function FG1 is obtained by weighted averaging, so as to evaluate the comprehensive influence of M0 interference factor indicators on the frequency band Ψ1 radar sensor, thereby evaluating the comprehensive ranging error of the frequency band Ψ1 radar sensor; The interference factor information is used to determine the comprehensive interference influence function FG1 of the frequency band Ψ1 radar sensor, and the ranging correction coefficient H1 between the interference factor information and the frequency band Ψ1 radar sensor is obtained: ; Wherein, ω1 is the adjustment constant of the frequency band Ψ1, and ω1 is preset to be greater than 0; when the comprehensive interference influence function FG1 is a positive number, it means that the deviation index of the radar sensor ranging of the frequency band Ψ1 is greater than 0, indicating that the experimental value is greater than the preset value and the experimental value is too large. At this time, the experimental value B1 needs to be lowered and corrected, and the corrected ranging correction coefficient H1 is lower; S3-502, integrating the periodic numerical vector GP and the second deviation vector C2 of the interference factor index Gi and marking them as the second associated vector group of the interference factor index Gi; then substituting the second associated vector group of the interference factor index Gi into the interference analysis sub-model, outputting the corresponding fitting function and marking it as the second error influence function Fgi2 of the interference factor index Gi, so as to evaluate the radar ranging error degree of the frequency band Ψ2 radar sensor through the interference factor index Gi; Through the second error influence function Fgi2 of M0 interference factor indicators, the comprehensive interference influence function FG2 of the interference factor information on the frequency band Ψ2 radar sensor is obtained: ; in, It refers to the deviation weight proportion of the interference factor index Gi in the frequency band Ψ2. The comprehensive interference influence function FG2 is obtained by weighted averaging, so as to evaluate the comprehensive influence of M0 interference factor indicators on the frequency band Ψ2 radar sensor, thereby evaluating the comprehensive ranging error of the frequency band Ψ2 radar sensor; The comprehensive interference influence function FG2 of the interference factor information on the frequency band Ψ2 radar sensor is used to obtain the ranging correction coefficient H2 between the interference factor information and the frequency band Ψ2 radar sensor: ; Wherein, ω2 is the adjustment constant of the frequency band Ψ2, and ω2 is preset to be greater than 0; when the comprehensive interference influence function FG2 is a negative number, it means that the deviation index of the frequency band Ψ2 radar sensor ranging is less than 0, indicating that the experimental value is less than the preset value and the experimental value is too small. At this time, the experimental value B2 needs to be increased and corrected. The higher the corrected ranging correction coefficient H2 is; S4, automatically calling the radar sensor with the optimal frequency band: applying the dual-band radar joint model to the actual scene, collecting the interference factor information of the actual scene, and then substituting it into the interference influence function to evaluate the radar ranging error of the actual scene, so as to determine the priority of the radar sensors of two different frequency bands, and select the radar sensor with a higher priority to perform ranging operation, so as to generate and output the ranging correction result; S4-1, applying the dual-band radar joint model to the actual scene, collecting the interference factor information of the actual scene and marking it as the interference factor information set W; Mark any interference factor indicator data of the interference factor information set W as ; S4-101, through the first error influence function Fgi1 of the interference factor index Gi, the actual interference factor index data Substitute the first error influence function Fgi1 to obtain the actual deviation coefficient of the interference factor index Gi in the frequency band Ψ1 , and similarly obtain the actual deviation coefficient of all interference factor indicators in the frequency band Ψ1; Then, through the comprehensive interference influence function FG1 of the interference factor information on the frequency band Ψ1 radar sensor, the interference factor information set W and its actual deviation coefficient corresponding to the frequency band Ψ1 are substituted into the comprehensive interference influence function FG1 to obtain the actual comprehensive interference influence function value of the interference factor information on the frequency band Ψ1 radar sensor , evaluate the radar ranging error degree of the frequency band Ψ1 radar sensor in actual scenes; S4-102, through the second error influence function Fgi2 of the interference factor index Gi, the actual interference factor index data Substitute the second error influence function Fgi2 to obtain the actual deviation coefficient of the interference factor index Gi in the frequency band Ψ2 , and similarly obtain the actual deviation coefficient of all interference factor indicators in the frequency band Ψ2; Then, through the comprehensive interference influence function FG2 of the interference factor information on the frequency band Ψ2 radar sensor, the interference factor information set W and its actual deviation coefficient corresponding to the frequency band Ψ2 are substituted into the comprehensive interference influence function FG2 to obtain the actual comprehensive interference influence function value of the interference factor information on the frequency band Ψ2 radar sensor , evaluate the radar ranging error degree of the frequency band Ψ2 radar sensor in actual scenes; S4-2, by comparing the actual comprehensive interference impact function value and , thereby determining the priority of two radar sensors in different frequency bands: S4-201, when < When , it means that the radar ranging error of the frequency band Ψ1 radar sensor in the actual scene is small, and the frequency band Ψ1 radar sensor is judged to have a high priority, and the radar call signal No. 1 is generated and output; When receiving the call signal of radar No. 1, the radar sensor of frequency band Ψ1 is selected to perform ranging operation; The actual distance measurement value of the radar sensor in the frequency band Ψ1 is marked as D1, and the actual distance measurement value D1 and the actual comprehensive interference influence function value are compared. , substitute it into the distance correction coefficient H1 to obtain the correction value φd1 of the actual distance value D1; The correction value φd1 of the actual distance measurement value D1 is marked as the distance measurement correction result of the frequency band Ψ1 radar sensor and output; S4-202, when > When , it means that the radar ranging error of the frequency band Ψ2 radar sensor in the actual scene is small, and it is determined that the frequency band Ψ2 radar sensor has a high priority, and the second radar call signal is output; When receiving the call signal of radar No. 2, the radar sensor of frequency band Ψ2 is selected to perform ranging operation; The actual distance measurement value of the radar sensor in frequency band Ψ2 is marked as D2, and the actual distance measurement value D2 and the actual comprehensive interference influence function value are compared. , substitute it into the distance correction coefficient H2 to obtain the correction value φd2 of the actual distance value D2; The correction value φd2 of the actual distance measurement value D2 is marked as the distance measurement correction result of the frequency band Ψ2 radar sensor and output; S5, performing radar fusion ranging by comparison and judgment: setting a threshold of radar ranging error. When the radar ranging errors of two radar sensors in different frequency bands exceed the corresponding threshold, radar fusion ranging is performed to correct and integrate the actual ranging results obtained by the radar sensor group, thereby generating and outputting a ranging verification result. S5-1, set the threshold of radar ranging error to Q, when the actual comprehensive interference affects the function value >Q and the actual comprehensive interference affects the function value >Q, the call signal of radar No. 3 is generated and output; S5-2, when receiving the call signal of radar No. 3, radar fusion ranging is performed: S5-201, correcting the actual ranging result obtained by the radar sensor group, and synchronously obtaining a correction value φd1 of the radar sensor in the frequency band Ψ1 and a correction value φd2 of the radar sensor in the frequency band Ψ2; S5-202, integrate the correction value φd1 and the correction value φd2 to obtain the distance measurement verification value φd3: , where λ1 and λ2 are the proportional coefficients of the correction value φd1 and the correction value φd2, and both λ1 and λ2 are preset to be greater than 0. , the proportionality coefficient is preset and obtained after a large amount of experimental data is measured; S5-203, marking the ranging verification value φd3 as the ranging verification result of the radar fusion ranging and outputting it; S6, timed refresh radar ranging mode: through timed feedback management instructions, the interference factor information is collected regularly to achieve the refresh of the radar call signal.

[0015] A FMCW radar ranging device, comprising a data acquisition unit, a preprocessing unit, a core processing unit and a call management unit, wherein the data acquisition unit, the preprocessing unit, the core processing unit and the call management unit are communicatively connected, and the device applies the above-mentioned FMCW radar ranging method; The data acquisition unit is used to collect interference factor information of radar application scenarios, wherein the radar application scenarios include simulation scenarios and actual scenarios; The pre-processing unit is used to analyze the interference factor information and evaluate the radar ranging error; The core processing unit is used to build a dual-band radar joint model, evaluate the impact of interference factors on ranging errors and generate ranging correction functions, and then generate corresponding radar call signals by analyzing the degree of radar ranging errors. The actual ranging results of the radar sensor are corrected by the ranging correction function, thereby obtaining and outputting the ranging results of the dual-band radar joint model. The call management unit is used to receive radar call signals, automatically call the radar sensor with the optimal frequency band, and perform radar fusion ranging operations in special application scenarios, and feedback management instructions to the data acquisition unit to perform regular collection of interference factor information, thereby realizing the refresh of the radar call signal.

[0016] In summary, the present invention uses a dual-band radar joint model and conducts ranging experiments to evaluate the ranging errors of radar sensors in the two frequency bands, and then introduces interference factor information to perform influence function analysis, thereby evaluating the degree of ranging error of the interference factor information for the two frequency band radar sensors, and performs targeted ranging correction. By dynamically selecting and jointly using dual-band technologies, such as millimeter waves and ultrasonic waves, the frequency band radar sensor adapted to the current ranging application scenario is preferably selected and automatically called, or synchronously called for dual radar fusion ranging, and finally, the accuracy and anti-interference ability of radar ranging are improved, thereby improving the robustness and accuracy of the overall device.

[0017] It should be noted that the technical solution of the present invention includes but is not limited to a dual-band joint model. Those skilled in the art can easily associate a multi-band radar joint model with three or more bands. Therefore, the multi-band radar sensor ranging solution that can be extended from this solution should be included in the protection scope of the present invention. The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technical personnel in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0018] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for FMCW radar ranging, characterized by: The following steps are involved: Step 1: Build a dual-band radar joint model: Set up a radar sensor group to conduct ranging experiments. The radar sensor group includes radar sensors in two different frequency bands, labeled as band Ψ1 and band Ψ2. Create a radar ranging simulation scenario and collect simulated scenario data. Then, perform ranging in the simulated scenario using the radar sensor group to obtain ranging experiment results. The simulation scenario data includes the ranging preset value A0 and interference factor information; Step 2: Evaluate the ranging error of the dual-band radar joint model: Compare the preset ranging value A0 with the ranging experiment results to evaluate the ranging error of the radar sensor group; Step 3: Evaluate the impact of interference factors on ranging error and perform ranging correction: Construct an interference influence function between interference factor information and ranging error, thereby evaluating the degree of radar ranging error based on the interference factor information and generating the corresponding ranging correction coefficient; Step 4: Automatically call the radar sensor with the optimal frequency band: Apply the dual-band radar joint model to the actual scene, collect interference factor information in the actual scene, substitute it into the interference impact function, and evaluate the radar ranging error in the actual scene. This determines the priority of the two radar sensors in different frequency bands, and selects the radar sensor with the higher priority for ranging operation, thereby generating and outputting the ranging correction result. Step 5: Compare and determine the radar fusion ranging: Set the threshold for radar ranging error. When the radar ranging errors of two radar sensors in different frequency bands exceed the corresponding threshold, radar fusion ranging is performed. The actual ranging results obtained by the radar sensor group are corrected and integrated to generate and output the ranging verification result. Step 6: Refresh the radar ranging mode regularly: collect interference factor information regularly through regular feedback management instructions to refresh the radar call signal.

2. The FMCW radar ranging method according to claim 1, wherein: The specific process of evaluating the ranging error of the dual-band radar joint model is as follows: Sa1, the ranging experiment results include the ranging experiment values ​​of the radar sensor in the frequency band Ψ1 and the radar sensor in the frequency band Ψ2, wherein the ranging experiment value of the radar sensor in the frequency band Ψ1 is marked as B1; the ranging experiment value of the radar sensor in the frequency band Ψ2 is marked as B2; Sa2, establish the error analysis sub-model, the specific process is: Sa2-1, input N0 control data groups into the error analysis sub-model, where any data control group includes a preset value a and an experimental value b; Sa2-2, obtain the deviation comparison value through the preset value a and the experimental value b , thereby calculating the deviation comparison value of N0 control data groups The variance value of ; Sa2-3, by deviation comparison value and the difference factor , obtain the deviation coefficient η; Sa2-4, error analysis sub-model output deviation coefficient η; Sa3, set the number of ranging experiments of the radar sensor group to N1, and obtain N1 ranging data sets; Sa3-1, the ranging data set includes the ranging preset value A0, the ranging experimental value B1 and the ranging experimental value B2; The preset distance measurement value A0 and the experimental distance measurement value B1 are integrated and marked as the frequency band Ψ1 distance measurement data group; The preset distance measurement value A0 and the experimental distance measurement value B2 are integrated and marked as the frequency band Ψ2 distance measurement data group; Sa3-2, substitute the N1 frequency band Ψ1 ranging data sets into the error analysis sub-model, output the deviation index η1 of frequency band Ψ1, and thus evaluate the ranging error degree of the frequency band Ψ1 radar sensor; Sa3-3, substitute the N1 frequency band Ψ2 ranging data sets into the error analysis sub-model, output the deviation index η2 of the frequency band Ψ2, and thus evaluate the ranging error degree of the frequency band Ψ2 radar sensor; Sa4, set the ranging error evaluation interval, and compare the deviation exponents η1 and η2 with the ranging error evaluation interval to evaluate the degree of ranging error.

3. The FMCW radar ranging method according to claim 2, wherein: The specific process of evaluating the impact of interference factors on ranging error is: Sb1, interference factor information includes electromagnetic frequency band, noise frequency and meteorological parameters; wherein meteorological parameters include rainfall, snowfall, wind speed and haze visibility; preset interference factor information includes M0 interference factor indicators; Mark any interference factor index as Gi, set the information collection period Tc to collect the simulation scene data regularly, obtain the values ​​of M0 interference factor indicators, and integrate the values ​​of M0 interference factor indicators in any information collection period Tc to generate the interference factor vector Rg; Sb2, mark the number of information collection cycles Tc as m1, and construct the interference factor index matrix P through the interference factor vector Rg of m1 information collection cycles Tc; According to the information collection period Tc, the deviation index η1 of the frequency band Ψ1 and the deviation index η2 of the frequency band Ψ2 are synchronously calculated to obtain the deviation index η1 of the frequency band Ψ1 and the deviation index η2 of the frequency band Ψ2 corresponding to m1 information collection periods Tc; Sb3, integrate the deviation index η1 of the frequency band Ψ1 corresponding to the m1 information collection period Tc into the first deviation vector C1; The deviation index η2 of the frequency band Ψ2 corresponding to the m1 information collection period Tc is integrated into the second deviation vector C2; Sb4, establish the interference analysis sub-model, the specific process is as follows: Sb4-1, input m0 association vector groups into the interference analysis sub-model, where any association vector group includes an interference factor vector Inte and a deviation index vector Devi; Sb4-2, with the interference factor vector Inte as the horizontal coordinate and the deviation index vector Devi as the vertical coordinate, the correlation curve Sde is constructed through the m0 correlation vector groups, and the fitting function Fs of the correlation curve Sde is generated; Sb4-3, the interference analysis sub-model outputs the fitting function Fs of the associated vector group; Sb5, through the interference factor indicator matrix P, the first deviation vector C1 and the second deviation vector C2, substitute them into the interference analysis sub-model to evaluate the degree of radar ranging error of the interference factor information on the frequency band Ψ1 and the frequency band Ψ2 respectively.

4. The FMCW radar ranging method according to claim 3, wherein: The specific process of generating the ranging correction coefficient is: Sc1, extract any column vector of the interference factor indicator matrix P and mark it as the periodic numerical vector GP of the interference factor indicator Gi; Sc2: Integrate the periodic numerical vector GP of the interference factor index Gi and the first deviation vector C1 to mark them as the first correlation vector group of the interference factor index Gi; then substitute them into the interference analysis sub-model to output the first error influence function Fgi1 of the interference factor index Gi, thereby evaluating the radar ranging error degree of the frequency band Ψ1 radar sensor through the interference factor index Gi; Obtain the comprehensive interference influence function FG1 of the interference factor information on the radar sensor in the frequency band Ψ1 through the first error influence function Fgi1 of the M0 interference factor indicators, and then obtain the ranging correction coefficient H1 between the interference factor information and the radar sensor in the frequency band Ψ1; Sc3, the periodic numerical vector GP of the interference factor index Gi and the second deviation vector C2 are integrated and marked as the second correlation vector group of the interference factor index Gi; then, they are substituted into the interference analysis sub-model to output the second error influence function Fgi2 of the interference factor index Gi, thereby evaluating the radar ranging error degree of the frequency band Ψ2 radar sensor through the interference factor index Gi; Through the second error influence function Fgi2 of the M0 interference factor indicators, the comprehensive interference influence function FG2 of the interference factor information on the frequency band Ψ2 radar sensor is obtained, and then the ranging correction coefficient H2 between the interference factor information and the frequency band Ψ2 radar sensor is obtained.

5. The FMCW radar ranging method according to claim 4, wherein: The specific process of evaluating the radar ranging error in actual scenarios is as follows: Sd1, the dual-band radar joint model is applied to the actual scene, the interference factor information of the actual scene is collected and marked as the interference factor information set W, and any interference factor index data of the interference factor information set W is marked as ; Sd2, obtains the actual deviation coefficient of the interference factor index Gi in the frequency band Ψ1 through the first error influence function Fgi1 of the interference factor index Gi , and similarly obtain the actual deviation coefficient of all interference factor indicators in the frequency band Ψ1; Then, the actual comprehensive interference influence function value of the interference factor information on the frequency band Ψ1 radar sensor is obtained through the comprehensive interference influence function FG1 of the interference factor information on the frequency band Ψ1 radar sensor , evaluate the radar ranging error of the frequency band Ψ1 radar sensor in actual scenarios; Sd3, obtain the actual deviation coefficient of the interference factor index Gi in the frequency band Ψ2 through the second error influence function Fgi2 of the interference factor index Gi , and similarly obtain the actual deviation coefficient of all interference factor indicators in the frequency band Ψ2; Then, the actual comprehensive interference influence function value of the interference factor information on the frequency band Ψ2 radar sensor is obtained through the comprehensive interference influence function FG2 of the interference factor information on the frequency band Ψ2 radar sensor. , evaluate the radar ranging error degree of the frequency band Ψ2 radar sensor in actual scenarios.

6. The FMCW radar ranging method according to claim 5, wherein: The specific process of determining and generating the radar call signal is as follows: By comparing the actual comprehensive interference impact function value and , thereby determining the priority of two radar sensors in different frequency bands: when < When , it is determined that the frequency band Ψ1 radar sensor has a high priority, generates and outputs the No. 1 radar call signal, and selects the frequency band Ψ1 radar sensor for ranging operation; when > When , it is determined that the frequency band Ψ2 radar sensor has a high priority, and the second radar call signal is output, and the frequency band Ψ2 radar sensor is selected for ranging operation; Set the threshold of radar ranging error to Q, when the actual integrated interference affects the function value >Q and the actual comprehensive interference affects the function value When >Q, the No. 3 radar call signal is generated and output, and radar fusion ranging is performed.

7. An FMCW radar ranging device, characterized by: The system comprises a data acquisition unit, a preprocessing unit, a core processing unit and a call management unit, wherein the data acquisition unit, the preprocessing unit, the core processing unit and the call management unit are communicatively connected to each other, and the system applies an FMCW radar ranging method according to any one of claims 1 to 6 above; The data acquisition unit is used to collect interference factor information of radar application scenarios; The pre-processing unit is used to analyze the interference factor information and evaluate the radar ranging error; The core processing unit is used to build a dual-band radar joint model, evaluate the impact of interference factors on ranging errors, and generate a ranging correction function. It then analyzes the degree of radar ranging error and generates a corresponding radar call signal. The ranging correction function corrects the actual ranging results of the radar sensor, thereby obtaining and outputting the ranging results of the dual-band radar joint model. The call management unit is used to receive radar call signals, automatically call the radar sensor with the optimal frequency band, perform radar fusion ranging operations in special application scenarios, and feedback management instructions to the data acquisition unit for regular collection of interference factor information, thereby refreshing the radar call signal.

Images