Method and device for realizing temperature and frequency compensation fitting processing for IQ imbalance based on Gaussian kernel model, processor and medium

Abstract

The invention relates to a method for realizing temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model. The method comprises the following steps of performing preparation work before calibration; multiple groups of IQ imbalance data high and low temperature experiments calibrated at different constant temperatures are carried out; and performing Gaussian kernel matrix constraint fitting by taking multiple groups of calibration data of different constant temperatures as a training set to obtain a temperature compensation coefficient. The invention also relates to a device for performing temperature and frequency compensation fitting processing on IQ imbalance based on the Gaussian kernel model, a processor and a computer readable storage medium thereof. By adopting the method and device for realizing temperature and frequency compensation fitting processing for IQ imbalance based on the Gaussian kernel model, the processor and the computer readable storage medium thereof, a temperature compensation fitting algorithm based on IQ imbalance of the Gaussian kernel model is provided, the fitting algorithm makes up the influence of temperature change on IQ imbalance calibration as much as possible, and the accuracy of IQ imbalance calibration is improved. Therefore, the IQ imbalance calibration effect in a proper temperature range is ensured as much as possible.

Description

Technical Field

[0001] The present invention relates to the field of digital signal processing, and in particular to the field of temperature compensation for IQ imbalance calibration, and specifically refers to a method, device, processor and computer-readable storage medium thereof for implementing temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model. Background Art

[0002] In modern signal transmission systems, since the wavelength of low-frequency signals is too long and is easily affected by surrounding environmental factors, there is a transmitter that moves the low-frequency signal to a high-frequency signal for signal transmission. This method is called signal modulation. At the receiving end, there is a receiver that can demodulate the signal and restore the signal originally moved to a high frequency to its original low-frequency signal.

[0003] In the process of signal modulation, due to the imperfect actual factors, the deviation of amplitude and phase will cause leakage of local oscillator and image signal, so it is necessary to calibrate the local oscillator and image signal to eliminate the leakage caused by imbalance; but because the modulator is an active device, the calibration effect will be reduced due to temperature change. Since the temperature effect of general modulators is nonlinear, the imbalance calibration effect cannot achieve the expected effect when temperature compensation is performed in the usual linear manner; and for the communication field, the IQ imbalance calibration effect will directly affect the quality of its communication signal, that is, the EVM will increase. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method, device, processor and computer-readable storage medium thereof for temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model, which are simple to operate, have small errors and a wide range of applications.

[0005] In order to achieve the above object, the method, device, processor and computer-readable storage medium thereof for implementing temperature and frequency compensation fitting processing for IQ imbalance based on Gaussian kernel model of the present invention are as follows:

[0006] The method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on Gaussian kernel model is mainly characterized in that the method comprises the following steps:

[0007] (1) Perform preparation work before calibration;

[0008] (2) Conduct multiple sets of high and low temperature experiments on IQ imbalance data calibrated at different constant temperatures;

[0009] (3) Based on multiple sets of calibration data at different constant temperatures as training sets, Gaussian kernel matrix constraint fitting is performed to obtain the temperature compensation coefficient, that is, the function obtained after Gaussian kernel matrix constraint.

[0010] Preferably, the step (1) specifically comprises the following steps:

[0011] (1.1) Place the signal source in a constant temperature box and connect the RF output of the signal source to the signal input of the spectrum analyzer;

[0012] (1.2) Connect the reference output of the spectrum analyzer to the reference input of the signal source;

[0013] (1.3) Initialize the signal source and turn on the modulator switch for preheating.

[0014] Preferably, the step (2) specifically comprises the following steps:

[0015] (2.1) The signal source is operated at a constant temperature at t0, the modulator is turned on during the preheating process, the temperature of the temperature sensor is read in real time, and the temperature is waited for stabilization;

[0016] (2.2) Turn on the modulator and send a modulation signal to control the signal source to modulate the output, respectively from the frequency F∈[F0, F1, F2, ..., F m-1 , F m ]Carry out calibration in sequence;

[0017] (2.3) Read and record the actual temperature t0 of the modulator after stabilization to obtain a set of dat data;

[0018] (2.4) Modify the constant temperature box temperature to t0, t1, t2, ..., t n , continue step (2.1) to recalibrate and obtain a set of data tables consisting of frequency and temperature.

[0019] Preferably, the step (3) specifically comprises the following steps:

[0020] (3.1) At the same calibration frequency, a set of array The obtained calibration data set dat array ;

[0021] (3.2) Set the fitting function f(τ,β);

[0022] (3.3) Set the number of training iterations α and obtain the Gaussian kernel function Ki under the current iteration;

[0023] (3.4) Use the gradient method to iterate and obtain the fitting parameter β;

[0024] (3.5) A threshold is determined based on the value of β-β0. If it is lower than the threshold, the next iteration is performed. If it is higher than the threshold, β0=β and the next iteration is performed.

[0025] (3.6) After the iteration, the fitting parameters β are cached in the storage space.

[0026] Preferably, the fitting function f(τ, β) is set in step (3.2), specifically:

[0027] The fitting function f(τ,β) is set according to the following formula:

[0028] f(τ,β)=K(τ,t array ′ )×β;

[0029] Among them, τ is the temperature variable to be fitted, β is the parameter value of the fitting function, and t arrat =[t0,t1,t2,……,t n ].

[0030] Preferably, the Gaussian kernel function Ki under the current iteration is obtained in step (3.3), specifically:

[0031] The Gaussian kernel function Ki under the current iteration is obtained according to the following formula:

[0032]

[0033] Among them, i is a random number between 1 and n, and δ is the range of the Gaussian kernel function.

[0034] Preferably, the fitting parameter β obtained in step (3.4) is specifically:

[0035] The fitting parameter β is obtained according to the following formula:

[0036] β=β0-e×Ki×(Ki ′ ×β0-dat array (i));

[0037] Among them, β0 is the initial parameter value, β0∈[0,1], Ki is the original matrix, Ki ′ is the inverse matrix of the Ki matrix.

[0038] Preferably, the method further comprises the following steps:

[0039] (4) A fitting function is performed according to the fitting parameter β to obtain the frequency variation trend, and the linear difference is calculated to obtain the fitting calibration parameter.

[0040] Preferably, the step (4) specifically comprises the following steps:

[0041] (4.1) Set the frequency to f s , the current temperature is τ x , take out the calibration point f p and fp+1 Parameters and Calculate the fitting function to get and

[0042] (4.2) After calibration frequency f p and f p+1 Make a fitting line and get the frequency at f s ∈[f p ,f p+1 ];

[0043] (4.3) The linear difference is calculated by the change trend, and the frequency point is calculated as f s The fitting calibration parameters

[0044] Preferably, the frequency point calculated in step (4.3) is f s The fitting calibration parameters Specifically:

[0045] The frequency point is calculated as f according to the following formula s The fitting calibration parameters

[0046]

[0047] Among them, τ x is the current temperature, frequency f s ∈[f p ,f p+1 ].

[0048] The main feature of the device for implementing temperature and frequency compensation fitting processing for IQ imbalance based on Gaussian kernel model is that the device comprises:

[0049] a processor configured to execute computer-executable instructions;

[0050] A memory stores one or more computer executable instructions, and when the computer executable instructions are executed by the processor, the various steps of the method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on the Gaussian kernel model are implemented.

[0051] The processor for implementing temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model has the main feature that the processor is configured to execute computer executable instructions. When the computer executable instructions are executed by the processor, the various steps of the method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model are implemented.

[0052] The computer-readable storage medium has the main feature of storing a computer program thereon, and the computer program can be executed by a processor to implement the various steps of the above-mentioned method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model.

[0053] The method, device, processor and computer-readable storage medium thereof for implementing temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model of the present invention are adopted, and a temperature compensation fitting algorithm for IQ imbalance based on a Gaussian kernel model is provided. The fitting algorithm compensates for the influence of temperature change on IQ imbalance calibration as much as possible, so as to ensure the IQ imbalance calibration effect within an appropriate temperature range as much as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 The present invention is a flow chart of a method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model. DETAILED DESCRIPTION

[0055] In order to more clearly describe the technical content of the present invention, further description is given below in conjunction with specific embodiments.

[0056] The method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on the Gaussian kernel model of the present invention comprises the following steps:

[0057] (1) Perform preparation work before calibration;

[0058] (2) Conduct multiple sets of high and low temperature experiments on IQ imbalance data calibrated at different constant temperatures;

[0059] (3) Based on multiple sets of calibration data at different constant temperatures as training sets, Gaussian kernel matrix constraint fitting is performed to obtain the temperature compensation coefficient, that is, the function obtained after Gaussian kernel matrix constraint, which is:

[0060] As a preferred embodiment of the present invention, the step (1) specifically comprises the following steps:

[0061] (1.1) Place the signal source in a constant temperature box and connect the RF output of the signal source to the signal input of the spectrum analyzer;

[0062] (1.2) Connect the reference output of the spectrum analyzer to the reference input of the signal source;

[0063] (1.3) Initialize the signal source and turn on the modulator switch for preheating.

[0064] As a preferred embodiment of the present invention, the step (2) specifically comprises the following steps:

[0065] (2.1) The signal source is operated at a constant temperature at t0, the modulator is turned on during the preheating process, the temperature of the temperature sensor is read in real time, and the temperature is waited for stabilization;

[0066] (2.2) Turn on the modulator and send a modulation signal to control the signal source to modulate the output, respectively from the frequency F∈[F0, F1, F2, ..., F m-1 , F m ]Carry out calibration in sequence;

[0067] (2.3) Read and record the actual temperature t0 of the modulator after stabilization to obtain a set of dat data;

[0068] (2.4) Modify the constant temperature box temperature to t0, t1, t2, ..., t n , continue step (2.1) to recalibrate and obtain a set of data tables consisting of frequency and temperature.

[0069] As a preferred embodiment of the present invention, the step (3) specifically comprises the following steps:

[0070] (3.1) At the same calibration frequency, a set of array The obtained calibration data set dat array ;

[0071] (3.2) Set the fitting function f(τ,β);

[0072] (3.3) Set the number of training iterations α and obtain the Gaussian kernel function Ki under the current iteration;

[0073] (3.4) Use the gradient method to iterate and obtain the fitting parameter β;

[0074] (3.5) A threshold is determined based on the value of β-β0. If it is lower than the threshold, the next iteration is performed. If it is higher than the threshold, β0=β and the next iteration is performed.

[0075] (3.6) After the iteration, the fitting parameters β are cached in the storage space.

[0076] As a preferred embodiment of the present invention, the fitting function f(τ, β) is set in step (3.2), specifically:

[0077] The fitting function f(τ,β) is set according to the following formula:

[0078] f(τ,β)=K(τ,t array ′ )×β;

[0079] Among them, τ is the temperature variable to be fitted, β is the parameter value of the fitting function, and tarray =[t0,t1,t2,……,t n ].

[0080] As a preferred embodiment of the present invention, the Gaussian kernel function Ki under the current iteration is obtained in step (3.3), specifically:

[0081] The Gaussian kernel function Ki under the current iteration is obtained according to the following formula:

[0082]

[0083] Among them, i is a random number between 1 and n, and δ is the range of the Gaussian kernel function.

[0084] As a preferred embodiment of the present invention, the fitting parameter β obtained in step (3.4) is specifically:

[0085] The fitting parameter β is obtained according to the following formula:

[0086] β=β0-e×Ki×(Ki′×β0-dat array (i));

[0087] Among them, β0 is the initial parameter value, β0∈[0,1], Ki is the original matrix, Ki ′ is the inverse matrix of the Ki matrix.

[0088] As a preferred embodiment of the present invention, the method further comprises the following steps:

[0089] (4) A fitting function is performed according to the fitting parameter β to obtain the frequency variation trend, and the linear difference is calculated to obtain the fitting calibration parameter.

[0090] As a preferred embodiment of the present invention, the step (4) specifically comprises the following steps:

[0091] (4.1) Set the frequency to f s , the current temperature is τ x , take out the calibration point f p and f p+1 Parameters and Calculate the fitting function to get and

[0092] (4.2) After calibration frequency f p and f p+1 Make a fitting line and get the frequency at f s ∈[f p ,f p+1 ];

[0093] (4.3) The linear difference is calculated by the change trend, and the frequency point is calculated as f s The fitting calibration parameters

[0094] As a preferred embodiment of the present invention, the frequency point f calculated in step (4.3) is s The fitting calibration parameters Specifically:

[0095] The frequency point is calculated as f according to the following formula s The fitting calibration parameters

[0096]

[0097] Among them, τ x is the current temperature, frequency f s ∈[f p ,f p+1 ].

[0098] The device for implementing temperature and frequency compensation fitting processing for IQ imbalance based on Gaussian kernel model of the present invention, wherein the device comprises:

[0099] a processor configured to execute computer-executable instructions;

[0100] A memory stores one or more computer executable instructions, and when the computer executable instructions are executed by the processor, the various steps of the method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on the Gaussian kernel model are implemented.

[0101] The processor of the present invention is used to implement temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model, wherein the processor is configured to execute computer executable instructions. When the computer executable instructions are executed by the processor, the various steps of the method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model are implemented.

[0102] The computer-readable storage medium of the present invention stores a computer program thereon, and the computer program can be executed by a processor to implement the various steps of the above-mentioned method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model.

[0103] In the specific implementation of the present invention, the technical solution adopted is as follows:

[0104] Based on the signal source IQ modulation system, high and low temperature experiments of IQ imbalance data calibrated at multiple constant temperatures are carried out; based on multiple sets of calibration data at different constant temperatures as training sets, the temperature compensation coefficient is fitted in combination with Gaussian kernel matrix constraints, and the error value of its fitting is reduced as much as possible. The present invention adopts Gaussian matrix fitting, which is nonlinear fitting and is more in line with the nonlinearity caused by practical factors.

[0105] 1. The preparatory work before calibration is as follows:

[0106] First, place the signal source in a constant temperature box and connect the RF output of the signal source to the signal input of the spectrum analyzer;

[0107] At the same time, connect the 10MHz reference output of the spectrum analyzer to the reference input of the signal source (for synchronization reference to prevent signal frequency deviation); open the program control software to initialize the signal source, and turn on the modulator switch to preheat.

[0108] 2. The operations during the calibration process are as follows:

[0109] 1. Perform constant temperature operation on the signal source at t0 to keep the instrument at a constant temperature for about 30 minutes. Turn on the modulator during the preheating process to prevent errors caused by turning on the modulator later. The temperature of the temperature sensor can be read in real time through the program control software. After the temperature reaches stability, the next step can be performed.

[0110] 2. Open the program-controlled calibration software, turn on the modulator and send a modulation signal to control the signal source to modulate the output, respectively from the frequency F∈[F0,F1,F2,……,F m-1 ,F m ]Carry out calibration in sequence;

[0111] 3. Read the actual temperature t0 of the modulator after stabilization and record it in the following table to obtain a set of dat data (dat 00 ,dat 10 ,......dat m0 );

[0112] <![CDATA[t0]]> <![CDATA[t1]]> <![CDATA[t2]]> … <![CDATA[t n ]]> <![CDATA[F0]]> <![CDATA[dat 00 ]]> <![CDATA[dat 01 ]]> <![CDATA[dat 02 ]]> … <![CDATA[dat 0n ]]> <![CDATA[F1]]> <![CDATA[dat 10 ]]> <![CDATA[dat 11 ]]> <![CDATA[dat 12 ]]> … <![CDATA[dat 1n ]]> <![CDATA[F2]]> <![CDATA[dat 20 ]]> <![CDATA[dat 21 ]]> <![CDATA[dat 22 ]]> … <![CDATA[dat 2n ]]> … … … … … … <![CDATA[F m-1 ]]> <![CDATA[dat (m-1)0 ]]> <![CDATA[dat (m-1)1 ]]> <![CDATA[dat (m-1)2 ]]> … <![CDATA[dat (m-1)n ]]> <![CDATA[F m ]]> <![CDATA[dat m0 ]]> <![CDATA[dat m1 ]]> <![CDATA[dat m2 ]]> … <![CDATA[dat mn ]]>

[0113] 4. After calibration, change the constant temperature box temperature to t0, t1, t2, ..., t n , then recalibrate the LO and image imbalance leakage from the first step, which will result in a set of data tables consisting of frequency and temperature.

[0114] 3. Algorithm in the fitting process:

[0115] 1. At the same calibration frequency, a set of calibration results can be obtained from different temperatures (t array=[t0,t1,t2,……,t n ]) The calibration data set dat array ;

[0116] 2. Assume that the fitting function f(τ,β)=K(τ,t array ′)*β; where τ is the temperature variable to be fitted, and β is the parameter value of the fitting function;

[0117] 3. Assume that the number of training iterations is α (α = μn, μ∈N * ) times, the parameter i is a random number (between 1 and n), and the Gaussian kernel function at the current iteration is Among them, δ controls the range of the Gaussian kernel function. The larger the value, the larger the range of influence, but it cannot be too small, which will lead to overfitting;

[0118] 4. Use the gradient method to iterate, assuming that the gradient amplitude is e; at this time, the fitting parameter β = β0-e×Ki×(Ki ′ ×β0-dat array (i)); where β0 is the initial parameter value (β0∈[0,1]);

[0119] 5. β-β0 performs threshold judgment. If it is lower than the threshold, it proceeds to the next iteration; if it is higher than the threshold, β0=β, and it proceeds to the next iteration;

[0120] 6. After the iteration, cache the β parameters into the storage space.

[0121] 4. Signal source application algorithm:

[0122] 1. Assume that the frequency point is set to f s ∈[f p ,f p+1 ], the current temperature is τ x , the host computer takes out the calibration point f from the cache space p 、f p+1 of Parameters, calculated by fitting function

[0123] 2. After calibration, the frequency point f p and f p+1 Make a fitting line according to It can be known that when the frequency is f s ∈[f p ,f p+1 ];

[0124] 3. Calculate the linear difference based on the change trend. At this time, when the frequency point is f s The fitting calibration parameters are:

[0125] 4. When the frequency switches to another calibration interval, take the upper and lower calibration frequency points adjacent to the frequency.

[0126] The specific implementation scheme of this embodiment can refer to the relevant description in the above embodiment, which will not be repeated here.

[0127] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0128] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0129] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0130] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution device. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0131] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the corresponding program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0132] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0133] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0134] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0135] The method, device, processor and computer-readable storage medium thereof for implementing temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model of the present invention are adopted, and a temperature compensation fitting algorithm for IQ imbalance based on a Gaussian kernel model is provided. The fitting algorithm compensates for the influence of temperature change on IQ imbalance calibration as much as possible, so as to ensure the IQ imbalance calibration effect within an appropriate temperature range as much as possible.

[0136] In this specification, the present invention has been described with reference to specific embodiments thereof. However, it is apparent that various modifications and variations may be made without departing from the spirit and scope of the present invention. Therefore, the specification and drawings should be regarded as illustrative rather than restrictive.

Claims

1. A method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model, characterized in that: The method comprises the following steps: (1) Perform preparation work before calibration; (2) Conduct multiple sets of high and low temperature experiments on IQ imbalance data calibrated at different constant temperatures; (3) Based on multiple sets of calibration data at different constant temperatures as training sets, Gaussian kernel matrix constraint fitting is performed to obtain the temperature compensation coefficient, that is, the function obtained after Gaussian kernel matrix constraint.

2. The method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on Gaussian kernel model according to claim 1, characterized in that: The step (1) specifically comprises the following steps: (1.1) Place the signal source in a constant temperature box and connect the RF output of the signal source to the signal input of the spectrum analyzer; (1.2) Connect the reference output of the spectrum analyzer to the reference input of the signal source; (1.3) Initialize the signal source and turn on the modulator switch for preheating.

3. The method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on Gaussian kernel model according to claim 1, characterized in that: The step (2) specifically comprises the following steps: (2.1) The signal source is operated at a constant temperature at t0, the modulator is turned on during the preheating process, the temperature of the temperature sensor is read in real time, and the temperature is waited for stabilization; (2.2) Turn on the modulator and send a modulation signal to control the signal source to modulate the output, respectively from the frequency F∈[F0, F1, F2, ..., F m-1 , F m ]Carry out calibration in sequence; (2.3) Read and record the actual temperature t0 of the modulator after stabilization to obtain a set of dat data; (2.4) Modify the constant temperature box temperature to t0, t1, t2, ..., t n , continue step (2.1) to recalibrate and obtain a set of data tables consisting of frequency and temperature.

4. The method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on Gaussian kernel model according to claim 1, characterized in that: The step (3) specifically comprises the following steps: (3.1) At the same calibration frequency, a set of array The obtained calibration data set dat array ; (3.2) Set the fitting function f(τ,β); (3.3) Set the number of training iterations α and obtain the Gaussian kernel function Ki under the current iteration; (3.4) Use the gradient method to iterate and obtain the fitting parameter β; (3.5) A threshold is determined based on the value of β-β0. If it is lower than the threshold, the next iteration is performed. If it is higher than the threshold, β0=β and the next iteration is performed. (3.6) After the iteration, the fitting parameters β are cached in the storage space.

5. The method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on Gaussian kernel model according to claim 4, characterized in that: In the step (3.2), the fitting function f(τ, β) is set as follows: The fitting function f(τ,β) is set according to the following formula: f(τ,β)=K(τ,t array ′ )×β; Among them, τ is the temperature variable to be fitted, β is the parameter value of the fitting function, and t array =[t0,t1,t2,……,t n ].

6. The method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on Gaussian kernel model according to claim 4, characterized in that: The Gaussian kernel function Ki under the current iteration is obtained in step (3.3), specifically: The Gaussian kernel function Ki under the current iteration is obtained according to the following formula: Among them, i is a random number between 1 and n, and δ is the range of the Gaussian kernel function.

7. The method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on Gaussian kernel model according to claim 4, characterized in that: The fitting parameter β obtained in step (3.4) is specifically: The fitting parameter β is obtained according to the following formula: β=β0-e×Ki×(Ki′×β0-dat array (and)); Among them, β0 is the initial parameter value, β0∈[0,1], Ki is the original matrix, and Ki′ is the inverse matrix of the Ki matrix.

8. The method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on Gaussian kernel model according to claim 1, characterized in that: The method further comprises the following steps: (4) A fitting function is performed according to the fitting parameter β to obtain the frequency variation trend, and the linear difference is calculated to obtain the fitting calibration parameter.

9. The method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on Gaussian kernel model according to claim 8, characterized in that: The step (4) specifically comprises the following steps: (4.1) Set the frequency to f s , the current temperature is τ x , take out the calibration point f p and f p+1 Parameters and Calculate the fitting function to get and (4.2) After calibration frequency f p and f p+1 Make a fitting line and get the frequency at f s ∈[f p ,f p+1 ]; (4.3) Calculate the linear difference through the change trend, and the frequency point is calculated as f s The fitting calibration parameters 10. The method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on Gaussian kernel model according to claim 9, characterized in that: The frequency point calculated in step (4.3) is f s The fitting calibration parameters Specifically: The frequency point is calculated as f according to the following formula s The fitting calibration parameters Among them, τ x is the current temperature, frequency f s ∈[f p ,f p+1 ].

11. A device for implementing temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model, characterized in that: The device comprises: a processor configured to execute computer executable instructions; A memory storing one or more computer executable instructions, wherein when the computer executable instructions are executed by the processor, the steps of the method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model as described in any one of claims 1 to 10 are implemented.

12. A processor for implementing temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model, characterized in that: The processor is configured to execute computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model according to any one of claims 1 to 10 are implemented.

13. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program can be executed by a processor to implement the various steps of the method for implementing temperature and frequency compensation fitting processing for IQ imbalance based on a Gaussian kernel model as described in any one of claims 1 to 10.

Images