Radar phase noise and frequency deviation fitting method, system and terminal

Abstract

The invention discloses a radar phase noise and frequency offset fitting method, system and terminal, and the method comprises the steps: controlling a target radar to transmit a single-frequency signal, and collecting the single-frequency signal through a frequency spectrograph; and respectively inputting a plurality of frequencies, phase noise and frequency deviation input by a user into the constructed prediction model and training, and outputting a fitting result of a frequency difference value corresponding to the frequency and the phase noise based on the frequency input by the user and the trained model. According to the method, the radar data are received through the frequency spectrograph, so that the influence of radar frequency modulation convergence on the data is eliminated, meanwhile, frequency deviation and phase noise are fitted from the mixed data, and the accuracy of a model output result is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of radar parameter fitting, and in particular to a radar phase noise and frequency offset fitting method, system, terminal and computer-readable storage medium. Background Art

[0002] The existing radar modeling process generally uses empirical values ​​to set the phase noise and frequency offset parameters of the signal source, or obtains them from the manufacturer. However, the former cannot establish a high-precision model, and the latter is not universal.

[0003] In other words, the problems faced in establishing the phase noise and frequency difference model include two aspects: first, the radar signal frequency is affected by phase noise, frequency offset and frequency modulation convergence at the same time, and it is difficult to separate the data affecting frequency modulation convergence from the end-to-end data; second, the way phase noise and frequency offset affect the signal is very different, and general methods cannot effectively fit the parameters.

[0004] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0005] The main purpose of the present invention is to provide a radar phase noise and frequency offset fitting method, system, terminal and computer-readable storage medium, aiming to solve the problem of convergence in radar data of multiple mixed parameters in the prior art, which leads to inaccurate data calculated by the model.

[0006] To achieve the above object, the present invention provides a radar phase noise and frequency offset fitting method, the radar phase noise and frequency offset fitting method comprising the following steps:

[0007] Acquire a single-frequency signal collected by a spectrum analyzer, where the single-frequency signal is emitted by a target radar, wherein the single-frequency signal includes phase noise and frequency offset;

[0008] Inputting the multiple frequencies input by the user, the phase noise and the frequency offset into the constructed prediction model respectively, and outputting multiple mixing parameters and multiple frequency difference values;

[0009] All the frequency difference values ​​and all the mixing parameters are input into the constructed mixed Gaussian model, and a fitting result between the phase noise and the frequency offset is output.

[0010] Optionally, the radar phase noise and frequency offset fitting method, wherein the step of acquiring a single frequency signal collected by a spectrum analyzer further comprises:

[0011] Phase noise and frequency offset are screened in the single frequency signal.

[0012] Optionally, the radar phase noise and frequency offset fitting method, wherein the multiple frequencies input by the user, the phase noise and the frequency offset are respectively input into the constructed prediction model, and a plurality of mixing parameters and a plurality of frequency difference values ​​are output, specifically comprises:

[0013] Acquire multiple frequencies input by a user, and acquire a phase characteristic of the phase noise and a frequency characteristic of the frequency offset;

[0014] The phase characteristic represents a nonlinear process of the phase noise changing with frequency, and the frequency characteristic represents a nonlinear process of the frequency offset changing with frequency;

[0015] All the frequencies, the phase noises, the frequency offsets, the phase characteristics and the frequency characteristics are respectively input into the constructed prediction model, and a plurality of mixing parameters and a plurality of frequency difference values ​​are output.

[0016] Optionally, in the radar phase noise and frequency offset fitting method, the mixing parameters include: a mixing mean square, a mixing variance and a mixing weight;

[0017] The step of inputting all the frequencies, the phase noises, the frequency offsets, the phase characteristics and the frequency characteristics into the constructed prediction model respectively and outputting a plurality of mixing parameters and a plurality of frequency difference values ​​specifically includes:

[0018] Constructing a prediction model, and inputting all the frequencies, the phase noises, the frequency offsets, the phase characteristics, and the frequency characteristics into the prediction model respectively;

[0019] The prediction model calculates and outputs the mixed mean square, the mixed variance and the mixed weight corresponding to each of the frequencies according to the phase noise, the phase characteristics and different frequencies;

[0020] The prediction model calculates and outputs the frequency difference value corresponding to each frequency according to the frequency offset, the frequency characteristics and different frequencies.

[0021] Optionally, the radar phase noise and frequency offset fitting method, wherein the prediction model calculates and outputs the frequency difference value corresponding to each frequency according to the frequency offset, the frequency characteristics and different frequencies, further comprises:

[0022] A probability density function of frequency offset is constructed according to the mixed mean square, the mixed variance and the mixed weight corresponding to each of the frequencies.

[0023] Optionally, the radar phase noise and frequency offset fitting method, wherein all the frequency difference values ​​and all the mixing parameters are input into a constructed mixed Gaussian model, and a fitting result between the phase noise and the frequency offset is output, specifically includes:

[0024] Constructing an initial mixed Gaussian model, and using the probability density function to train the initial mixed Gaussian model to obtain a mixed Gaussian model;

[0025] Inputting the frequency difference value, the mixed mean square, the mixed variance and the mixed weight corresponding to each frequency into the mixed Gaussian model respectively;

[0026] The mixed Gaussian model generates corresponding predicted phase noise according to the mixed mean square, the mixed variance and the mixed weight corresponding to different frequencies;

[0027] The mixed Gaussian model generates a corresponding predicted frequency offset according to the frequency difference values ​​corresponding to different frequencies;

[0028] The mixed Gaussian model calculates and outputs corresponding fitting results according to the predicted phase noise and the predicted frequency offset corresponding to different frequencies:

[0029] ″′

[0030] Δf s =Δf d +Δf pn ;

[0031] Where Δf s ′ represents the fitting result, Δf d ′ Denotes the predicted frequency offset, Δf pn ′ represents the predicted phase noise.

[0032] Optionally, the radar phase noise and frequency offset fitting method, wherein the initial mixed Gaussian model is constructed, and the initial mixed Gaussian model is trained using the probability density function to obtain the mixed Gaussian model, specifically includes:

[0033] Obtain model parameters in the prediction model, and construct a loss function based on the model parameters and the probability density function:

[0034]

[0035] in, represents the loss function, f s represents the frequency, Bs represents the model parameter, j represents the number of frequencies, N represents the order of the mixed Gaussian, k irepresents the mixture weight of the i-th Gaussian distribution, N(f si |μ i +Δf d ′ ,σ i ) represents Δf d ′ The probability density function of the Gaussian distribution after translation, f si represents the i-th frequency, μ i represents the mixed mean of the i-th Gaussian distribution, σ i represents the mixed variance of the i-th Gaussian distribution, and γ represents the current mixing weight;

[0036] An initial mixed Gaussian model is constructed, and the loss function is used to train the initial mixed Gaussian model to obtain a mixed Gaussian model.

[0037] In addition, to achieve the above object, the present invention also provides a radar phase noise and frequency offset fitting system, wherein the radar phase noise and frequency offset fitting system comprises:

[0038] A data acquisition module, used to acquire a single-frequency signal collected by a spectrum analyzer, wherein the single-frequency signal is emitted by a target radar, wherein the single-frequency signal includes phase noise and frequency offset;

[0039] A parameter calculation module, used to input multiple frequencies input by the user, the phase noise and the frequency offset into the constructed prediction model respectively, and output multiple mixing parameters and multiple frequency difference values;

[0040] The parameter fitting module is used to input all the frequency difference values ​​and all the mixed parameters into the constructed mixed Gaussian model, and output the fitting result between the phase noise and the frequency offset.

[0041] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a fitting program of radar phase noise and frequency offset stored in the memory and executable on the processor, wherein the fitting program of radar phase noise and frequency offset implements the steps of the fitting method of radar phase noise and frequency offset as described above when the fitting program of radar phase noise and frequency offset is executed by the processor.

[0042] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a fitting program for radar phase noise and frequency offset, and when the fitting program for radar phase noise and frequency offset is executed by a processor, the steps of the fitting method for radar phase noise and frequency offset as described above are implemented.

[0043] In the present invention, a single-frequency signal collected by a spectrum analyzer is obtained, and the single-frequency signal is emitted by a target radar, wherein the single-frequency signal includes phase noise and frequency offset; multiple frequencies input by a user, the phase noise, and the frequency offset are respectively input into a constructed prediction model, and multiple mixed parameters and multiple frequency difference values ​​are output; all the frequency difference values ​​and all the mixed parameters are input into a constructed mixed Gaussian model, and a fitting result between the phase noise and the frequency offset is output. The present invention receives radar data through a spectrum analyzer, thereby eliminating the influence of radar frequency modulation convergence on the data, and at the same time fitting the frequency offset and phase noise from the mixed data, thereby improving the accuracy of the model output result. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of a preferred embodiment of the radar phase noise and frequency offset fitting method of the present invention;

[0045] Figure 2 It is a schematic diagram of single frequency signal transmission of a preferred embodiment of the radar phase noise and frequency offset fitting method of the present invention;

[0046] Figure 3 It is a schematic diagram of receiving a single frequency signal according to a preferred embodiment of the radar phase noise and frequency offset fitting method of the present invention;

[0047] Figure 4 It is a schematic diagram of the model structure of a preferred embodiment of the radar phase noise and frequency offset fitting method of the present invention;

[0048] Figure 5 It is a schematic diagram of fitting frequency offset of a preferred embodiment of the method for fitting radar phase noise and frequency offset of the present invention;

[0049] Figure 6 It is a schematic diagram of phase noise fitting of a preferred embodiment of the radar phase noise and frequency offset fitting method of the present invention;

[0050] Figure 7 It is a schematic diagram of the loss of different Gaussian orders of a preferred embodiment of the radar phase noise and frequency offset fitting method of the present invention;

[0051] Figure 8 It is a structural diagram of a preferred embodiment of the radar phase noise and frequency offset fitting system of the present invention;

[0052] Fig. 9 It is a structural diagram of a preferred embodiment of the terminal of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0054] The radar phase noise and frequency offset fitting method described in the preferred embodiment of the present invention is as follows: Figure 1 As shown, the radar phase noise and frequency offset fitting method comprises the following steps:

[0055] Step S10: acquiring a single-frequency signal collected by a spectrum analyzer, wherein the single-frequency signal is emitted by a target radar, wherein the single-frequency signal includes phase noise and frequency offset.

[0056] Among them, Figure 2 As shown in the figure, frequency offset and phase noise are common in the signal generated by the signal source, but convergence is only present when frequency modulation is performed. When collecting signal source data, since the highly integrated radar RF circuit does not have an RF interface that can be directly connected to the measuring instrument through a cable, an antenna is required to collect the signal using a spectrum analyzer or other equipment.

[0057] Further, phase noise and frequency offset are screened in the single frequency signal, wherein, Figure 3 As shown in the figure, the target radar is first used to transmit a single-frequency continuous signal, and the radar signal (i.e., single-frequency signal) is received using a standard horn antenna and a spectrum analyzer. At this time, the collected radar signal will only be affected by phase noise and frequency offset, thereby eliminating convergence and improving the accuracy of the subsequent model analysis process.

[0058] Step S20: input the multiple frequencies input by the user, the phase noise and the frequency offset into the constructed prediction model respectively, and output multiple mixing parameters and multiple frequency difference values.

[0059] Among them, Figure 4 As shown, since the data contains two parameters, frequency offset and phase noise, and according to the nonlinear changes of these two parameters with frequency and their differentiated random distribution characteristics at different frequencies, the distribution of phase noise can be fitted by a Gaussian Mixture Model (GMM), and the frequency offset values ​​and the mixed Gaussian parameter values ​​of phase noise at different frequencies can be solved by a multilayer perceptron (i.e., the prediction model mentioned above) (Multilayer Perceptron, MLP).

[0060] Specifically, multiple frequencies input by the user are obtained, and the phase characteristics of the phase noise and the frequency characteristics of the frequency offset are obtained; wherein the phase characteristics represent the nonlinear process of the phase noise changing with the frequency, and the frequency characteristics represent the nonlinear process of the frequency offset changing with the frequency; all the frequencies, the phase noise, the frequency offset, the phase characteristics and the frequency characteristics are respectively input into the constructed prediction model, and multiple mixing parameters and multiple frequency difference values ​​are output.

[0061] Furthermore, a prediction model is constructed, and all the frequencies, the phase noise, the frequency offset, the phase characteristics and the frequency characteristics are respectively input into the prediction model; the prediction model calculates and outputs the mixed mean square, the mixed variance and the mixed weight corresponding to each of the frequencies according to the phase noise, the phase characteristics and different frequencies; the prediction model calculates and outputs the frequency difference value corresponding to each of the frequencies according to the frequency offset, the frequency characteristics and different frequencies.

[0062] The mixing parameters include: mixing mean square, mixing variance and mixing weight. First, the data input into the prediction model is the theoretical frequency value (input by the user), and the frequency difference value and the mean (mixing mean square), variance (mixing variance) and weight (mixing weight) of the mixed Gaussian can be determined according to the nonlinear process of different frequencies, frequency offset and frequency variation, and the nonlinear process of phase noise and frequency variation.

[0063] Among them, by establishing a multi-layer perceptron (i.e., the prediction model), the phase noise and frequency offset are fitted from the data mixed with frequency offset and phase noise, which can effectively detect the radar phase noise and frequency offset, while improving the output accuracy of the next step of the mixed Gaussian model and the accuracy of the radar model.

[0064] Furthermore, a probability density function of frequency offset is constructed according to the mixed mean square, the mixed variance and the mixed weight corresponding to each of the frequencies.

[0065] Among them, after calculating the mixed mean square, mixed variance and mixed weight corresponding to each frequency, the probability density function of the Gaussian distribution after frequency offset translation can be established for subsequent training of the mixed Gaussian model to improve the accuracy of the radar model.

[0066] Step S30: input all the frequency difference values ​​and all the mixing parameters into the constructed mixed Gaussian model, and output the fitting result between the phase noise and the frequency offset.

[0067] Among them, the mixed Gaussian model is a probabilistic model that assumes that all data points are generated by a mixture of multiple Gaussian distributions, each Gaussian distribution represents a potential "cluster" or "category" in the data, and these clusters are represented by Gaussian distributions with different parameters (mean, covariance matrix and mixing coefficient). According to the existing data, the mean, covariance matrix and mixing coefficient of these data are solved by GMM, and the model of the data can be obtained. The solution process of this model relies on the EM (Expectation-Maximization) algorithm to estimate the model parameters through iterative optimization. First, the mean, covariance matrix and mixing coefficient of GMM are initialized; then, in the first step of the EM algorithm, the posterior probability (responsibility) of each data point belonging to each Gaussian component is calculated, and in the second step of the EM algorithm, the model parameters are updated according to these responsibilities, including the mean, covariance and mixing coefficient of each component. This process is repeated until the model parameters converge or the change of the log-likelihood function is less than the preset threshold.

[0068] Specifically, an initial mixed Gaussian model is constructed, and the initial mixed Gaussian model is trained using the probability density function to obtain a mixed Gaussian model; the frequency difference value, the mixed mean square, the mixed variance and the mixed weight corresponding to each frequency are respectively input into the mixed Gaussian model; the mixed Gaussian model generates the corresponding predicted phase noise according to the mixed mean square, the mixed variance and the mixed weight corresponding to different frequencies; the mixed Gaussian model generates the corresponding predicted frequency offset according to the frequency difference value corresponding to different frequencies; the mixed Gaussian model calculates and outputs the corresponding fitting result according to the predicted phase noise and the predicted frequency offset corresponding to different frequencies:

[0069] ″′

[0070] Δf s =Δf d +Δf pn ;

[0071] Where Δf s ′ represents the fitting result, Δf d ′ Denotes the predicted frequency offset, Δf pn ′ represents the predicted phase noise.

[0072] Furthermore, the model parameters in the prediction model are obtained, and a loss function is constructed according to the model parameters and the probability density function:

[0073]

[0074] in, represents the loss function, f s represents the frequency, Bs represents the model parameter, j represents the number of frequencies, N represents the order of the mixed Gaussian, k i represents the mixture weight of the i-th Gaussian distribution, N(f si |μ i +Δf d ′ ,σ i ) represents Δf d ′ The probability density function of the Gaussian distribution after translation, f si represents the i-th frequency, μ i represents the mixed mean of the i-th Gaussian distribution, σ i represents the mixed variance of the ith Gaussian distribution, and γ represents the current mixed weight; an initial mixed Gaussian model is constructed, and the loss function is used to train the initial mixed Gaussian model to obtain a mixed Gaussian model.

[0075] Among them, Figure 5 As shown, the fitting results of frequency offset at different frequencies can be obtained, such as Figure 6 As shown, the fitting result of the phase noise can be determined when the frequency is 77 GHz, that is, Figure 6 The fit between the probability distribution of the test data and the probability distribution predicted by the GMM.

[0076] Furthermore, if Figure 7 As shown in the figure, in the process of training the mixed Gaussian model, the MLP network uses softmax as the activation function, and uses adma (Adaptive Moment Estimation) as the optimizer, and the batch size is 300. Finally, the probability density function constructed above is used to train the mixed Gaussian model, improve the fitting degree of phase noise and frequency offset in subsequent results, and thus improve the accuracy of the radar model.

[0077] For example, 30 frequency points within the radar working frequency band of 76-78GHz are tested repeatedly, 100 frames of data are collected at each frequency point, and a total of 3000 sets of single-frequency continuous wave data are collected; the theoretical frequency of each set of data and the actual frequency of the received signal are counted, and the deviation between the received frequency of each set of data and the preset frequency is calculated. The theoretical frequency is used as input, the frequency difference is used as output, and the maximum and minimum values ​​are normalized. Then, a single-frequency signal data set of the transmitting module is established, of which 2500 sets of data are used as training sets, and 500 sets of data at five frequency points are used as test sets.

[0078] Furthermore, the weight of the loss function is 2.3, and the MLP uses a three-layer structure with 40 nodes each. We try different orders of Gaussian distribution models, and finally select the second-order mixed Gaussian model to fit the phase noise according to the optimization effect (e.g. Figure 7 As shown in FIG. 1 , the fitting result of the frequency offset is as described above, that is, the model can simulate the influence of the frequency offset and phase noise on the signal frequency.

[0079] The present invention receives radar data through a spectrum analyzer, thereby eliminating the influence of radar frequency modulation convergence on the data, and at the same time fits the frequency offset and phase noise from the mixed data, thereby improving the accuracy of the model output results. Furthermore, based on the present application, the differences in the mechanism characteristics of different parameters can be utilized, and each parameter can be fitted using a model that meets its characteristics, and then combined into other models according to the working characteristics, and a penalty term is used to ensure that the functions of the parameter models do not interfere with each other, thereby realizing the establishment of models of each parameter from the mixed data.

[0080] Furthermore, if Figure 8 As shown, based on the above-mentioned radar phase noise and frequency offset fitting method, the present invention also provides a radar phase noise and frequency offset fitting system, wherein the radar phase noise and frequency offset fitting system includes:

[0081] A data acquisition module 51 is used to acquire a single-frequency signal collected by a spectrum analyzer, wherein the single-frequency signal is emitted by a target radar, wherein the single-frequency signal includes phase noise and frequency offset;

[0082] A parameter calculation module 52, used to input the multiple frequencies input by the user, the phase noise and the frequency offset into the constructed prediction model respectively, and output multiple mixing parameters and multiple frequency difference values;

[0083] The parameter fitting module 53 is used to input all the frequency difference values ​​and all the mixed parameters into the constructed mixed Gaussian model, and output the fitting result between the phase noise and the frequency offset.

[0084] Furthermore, if Fig. 9 As shown, based on the above radar phase noise and frequency offset fitting method and system, the present invention also provides a terminal accordingly, and the terminal includes a processor 10, a memory 20 and a display 30. Fig. 9 Only some components of the terminal are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0085] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal. In other embodiments, the memory 20 may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Further, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various types of data installed in the terminal, such as the program code of the installation terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, a fitting program 40 for radar phase noise and frequency offset is stored on the memory 20, and the fitting program 40 for radar phase noise and frequency offset can be executed by the processor 10, thereby realizing the fitting method of radar phase noise and frequency offset in the present application.

[0086] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory 20, such as executing the fitting method of the radar phase noise and frequency offset.

[0087] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 30 is used to display information on the terminal and to display a visual user interface. The components of the terminal communicate with each other via a system bus.

[0088] In one embodiment, when the processor 10 executes the radar phase noise and frequency offset fitting program 40 in the memory 20, the following steps are implemented:

[0089] Acquire a single-frequency signal collected by a spectrum analyzer, where the single-frequency signal is emitted by a target radar, wherein the single-frequency signal includes phase noise and frequency offset;

[0090] Inputting the multiple frequencies input by the user, the phase noise and the frequency offset into the constructed prediction model respectively, and outputting multiple mixing parameters and multiple frequency difference values;

[0091] All the frequency difference values ​​and all the mixing parameters are input into the constructed mixed Gaussian model, and a fitting result between the phase noise and the frequency offset is output.

[0092] The step of acquiring a single frequency signal collected by a spectrum analyzer further includes:

[0093] Phase noise and frequency offset are screened in the single frequency signal.

[0094] The step of inputting the multiple frequencies, the phase noise and the frequency offset input by the user into the constructed prediction model respectively and outputting multiple mixing parameters and multiple frequency difference values ​​specifically includes:

[0095] Acquire multiple frequencies input by a user, and acquire a phase characteristic of the phase noise and a frequency characteristic of the frequency offset;

[0096] The phase characteristic represents a nonlinear process of the phase noise changing with frequency, and the frequency characteristic represents a nonlinear process of the frequency offset changing with frequency;

[0097] All the frequencies, the phase noises, the frequency offsets, the phase characteristics and the frequency characteristics are respectively input into the constructed prediction model, and a plurality of mixing parameters and a plurality of frequency difference values ​​are output.

[0098] Wherein, the mixing parameters include: mixing mean square, mixing variance and mixing weight;

[0099] The step of inputting all the frequencies, the phase noises, the frequency offsets, the phase characteristics and the frequency characteristics into the constructed prediction model respectively and outputting a plurality of mixing parameters and a plurality of frequency difference values ​​specifically includes:

[0100] Constructing a prediction model, and inputting all the frequencies, the phase noises, the frequency offsets, the phase characteristics, and the frequency characteristics into the prediction model respectively;

[0101] The prediction model calculates and outputs the mixed mean square, the mixed variance and the mixed weight corresponding to each of the frequencies according to the phase noise, the phase characteristics and different frequencies;

[0102] The prediction model calculates and outputs the frequency difference value corresponding to each frequency according to the frequency offset, the frequency characteristics and different frequencies.

[0103] The prediction model calculates and outputs the frequency difference value corresponding to each frequency according to the frequency offset, the frequency characteristics and different frequencies, and then further includes:

[0104] A probability density function of frequency offset is constructed according to the mixed mean square, the mixed variance and the mixed weight corresponding to each of the frequencies.

[0105] The step of inputting all the frequency difference values ​​and all the mixing parameters into the constructed mixed Gaussian model and outputting the fitting result between the phase noise and the frequency offset specifically includes:

[0106] Constructing an initial mixed Gaussian model, and using the probability density function to train the initial mixed Gaussian model to obtain a mixed Gaussian model;

[0107] Inputting the frequency difference value, the mixed mean square, the mixed variance and the mixed weight corresponding to each frequency into the mixed Gaussian model respectively;

[0108] The mixed Gaussian model generates corresponding predicted phase noise according to the mixed mean square, the mixed variance and the mixed weight corresponding to different frequencies;

[0109] The mixed Gaussian model generates a corresponding predicted frequency offset according to the frequency difference values ​​corresponding to different frequencies;

[0110] The mixed Gaussian model calculates and outputs corresponding fitting results according to the predicted phase noise and the predicted frequency offset corresponding to different frequencies:

[0111] ″′

[0112] Δf s =Δf d +Δf pn ;

[0113] Where Δf s ′ represents the fitting result, Δf d ′ Denotes the predicted frequency offset, Δf pn ′ represents the predicted phase noise.

[0114] The step of constructing an initial mixed Gaussian model and training the initial mixed Gaussian model using the probability density function to obtain a mixed Gaussian model specifically includes:

[0115] Obtain model parameters in the prediction model, and construct a loss function based on the model parameters and the probability density function:

[0116]

[0117] in, represents the loss function, f s represents the frequency, Bs represents the model parameter, j represents the number of frequencies, N represents the order of the mixed Gaussian, k i represents the mixture weight of the i-th Gaussian distribution, N(f si |μ i+Δf d ′ ,σ i ) represents Δf d ′ The probability density function of the Gaussian distribution after translation, f si represents the i-th frequency, μ i represents the mixed mean of the i-th Gaussian distribution, σ i represents the mixed variance of the i-th Gaussian distribution, and γ represents the current mixing weight;

[0118] An initial mixed Gaussian model is constructed, and the loss function is used to train the initial mixed Gaussian model to obtain a mixed Gaussian model.

[0119] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a fitting program for radar phase noise and frequency offset, and when the fitting program for radar phase noise and frequency offset is executed by a processor, the steps of the fitting method for radar phase noise and frequency offset as described above are implemented.

[0120] In summary, the present invention provides a radar phase noise and frequency offset fitting method and related equipment, the method comprising: obtaining a single-frequency signal collected by a spectrum analyzer, the single-frequency signal is emitted by a target radar, wherein the single-frequency signal includes phase noise and frequency offset; inputting multiple frequencies, the phase noise and the frequency offset input by a user into a constructed prediction model respectively, outputting multiple mixed parameters and multiple frequency difference values; inputting all the frequency difference values ​​and all the mixed parameters into a constructed mixed Gaussian model, and outputting the fitting result between the phase noise and the frequency offset. The present invention receives radar data through a spectrum analyzer, thereby eliminating the influence of radar frequency modulation convergence on the data, and at the same time fitting the frequency offset and phase noise from the mixed data, thereby improving the accuracy of the model output results.

[0121] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or terminal including the element.

[0122] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the program can be stored in a computer-readable storage medium that can be read by a computer, and the program can include the processes of the above-mentioned method embodiments when executed. The computer-readable storage medium can be a memory, a disk, an optical disk, etc.

[0123] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A radar phase noise and frequency offset fitting method, characterized in that: The radar phase noise and frequency offset fitting method comprises: Acquire a single-frequency signal collected by a spectrum analyzer, where the single-frequency signal is emitted by a target radar, wherein the single-frequency signal includes phase noise and frequency offset; Inputting the multiple frequencies input by the user, the phase noise and the frequency offset into the constructed prediction model respectively, and outputting multiple mixing parameters and multiple frequency difference values; All the frequency difference values ​​and all the mixing parameters are input into the constructed mixed Gaussian model, and a fitting result between the phase noise and the frequency offset is output.

2. The radar phase noise and frequency offset fitting method according to claim 1, characterized in that: The method of obtaining the single frequency signal collected by the spectrum analyzer further includes: Phase noise and frequency offset are screened in the single frequency signal.

3. The radar phase noise and frequency offset fitting method according to claim 1, characterized in that: The step of inputting the multiple frequencies, the phase noise and the frequency offset inputted by the user into the constructed prediction model respectively and outputting multiple mixing parameters and multiple frequency difference values ​​specifically includes: Acquire multiple frequencies input by a user, and acquire a phase characteristic of the phase noise and a frequency characteristic of the frequency offset; The phase characteristic represents a nonlinear process of the phase noise changing with frequency, and the frequency characteristic represents a nonlinear process of the frequency offset changing with frequency; All the frequencies, the phase noises, the frequency offsets, the phase characteristics and the frequency characteristics are respectively input into the constructed prediction model, and a plurality of mixing parameters and a plurality of frequency difference values ​​are output.

4. The radar phase noise and frequency offset fitting method according to claim 3, characterized in that: The mixing parameters include: mixing mean square, mixing variance and mixing weight; The step of inputting all the frequencies, the phase noises, the frequency offsets, the phase characteristics and the frequency characteristics into the constructed prediction model respectively and outputting a plurality of mixing parameters and a plurality of frequency difference values ​​specifically includes: Constructing a prediction model, and inputting all the frequencies, the phase noises, the frequency offsets, the phase characteristics, and the frequency characteristics into the prediction model respectively; The prediction model calculates and outputs the mixed mean square, the mixed variance and the mixed weight corresponding to each of the frequencies according to the phase noise, the phase characteristics and different frequencies; The prediction model calculates and outputs the frequency difference value corresponding to each frequency according to the frequency offset, the frequency characteristics and different frequencies.

5. The radar phase noise and frequency offset fitting method according to claim 4, characterized in that: The prediction model calculates and outputs the frequency difference value corresponding to each of the frequencies according to the frequency offset, the frequency characteristics and different frequencies, and then further includes: A probability density function of frequency offset is constructed according to the mixed mean square, the mixed variance and the mixed weight corresponding to each of the frequencies.

6. The radar phase noise and frequency offset fitting method according to claim 5, characterized in that: The step of inputting all the frequency difference values ​​and all the mixing parameters into the constructed mixed Gaussian model and outputting the fitting result between the phase noise and the frequency offset specifically includes: Constructing an initial mixed Gaussian model, and using the probability density function to train the initial mixed Gaussian model to obtain a mixed Gaussian model; Inputting the frequency difference value, the mixed mean square, the mixed variance and the mixed weight corresponding to each frequency into the mixed Gaussian model respectively; The mixed Gaussian model generates corresponding predicted phase noise according to the mixed mean square, the mixed variance and the mixed weight corresponding to different frequencies; The mixed Gaussian model generates a corresponding predicted frequency offset according to the frequency difference values ​​corresponding to different frequencies; The mixed Gaussian model calculates and outputs corresponding fitting results according to the predicted phase noise and the predicted frequency offset corresponding to different frequencies: ″′ Δf s =Δf d +Δf pn ; Where Δf s ′ represents the fitting result, Δf d ′ Denotes the predicted frequency offset, Δf pn ′ represents the predicted phase noise.

7. The radar phase noise and frequency offset fitting method according to claim 6, characterized in that: The constructing of the initial mixed Gaussian model and training the initial mixed Gaussian model using the probability density function to obtain the mixed Gaussian model specifically includes: Obtain model parameters in the prediction model, and construct a loss function based on the model parameters and the probability density function: in, represents the loss function, f s represents the frequency, Bs represents the model parameter, j represents the number of frequencies, N represents the order of the mixed Gaussian, k i represents the mixture weight of the i-th Gaussian distribution, N(f si |μ i +Δf d ′ ,σ i ) represents Δf d ′ The probability density function of the Gaussian distribution after translation, f si represents the i-th frequency, μ i represents the mixed mean of the i-th Gaussian distribution, σ i represents the mixed variance of the i-th Gaussian distribution, and γ represents the current mixing weight; An initial mixed Gaussian model is constructed, and the loss function is used to train the initial mixed Gaussian model to obtain a mixed Gaussian model.

8. A radar phase noise and frequency offset fitting system, characterized in that: The radar phase noise and frequency offset fitting system includes: A data acquisition module, used to acquire a single-frequency signal collected by a spectrum analyzer, wherein the single-frequency signal is emitted by a target radar, wherein the single-frequency signal includes phase noise and frequency offset; A parameter calculation module, used to input multiple frequencies input by the user, the phase noise and the frequency offset into the constructed prediction model respectively, and output multiple mixing parameters and multiple frequency difference values; The parameter fitting module is used to input all the frequency difference values ​​and all the mixed parameters into the constructed mixed Gaussian model, and output the fitting result between the phase noise and the frequency offset.

9. A terminal, characterized in that: The terminal includes: a memory, a processor, and a radar phase noise and frequency offset fitting program stored in the memory and executable on the processor, wherein the radar phase noise and frequency offset fitting program, when executed by the processor, implements the steps of the radar phase noise and frequency offset fitting method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a fitting program for radar phase noise and frequency offset, and when the fitting program for radar phase noise and frequency offset is executed by a processor, the steps of the fitting method for radar phase noise and frequency offset as described in any one of claims 1 to 7 are implemented.

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