Vector sensor noise floor measurement method and system using adaptive transfer function equalization

Abstract

The application discloses a kind of self-adapting transfer function equalization vector sensor background noise measurement method and system, belong to underwater acoustic sensor technical field.The output signal of the vector sensor to be measured and the reference signal of reference sensor are obtained;Based on the reference signal input to adaptive filter, the weight coefficient of adaptive filter is dynamically updated by adaptive algorithm, to establish the time-varying transfer function model from reference sensor to the sensor to be measured;Based on time-varying transfer function model, the reference signal is processed, and the result that input signal is transferred to the sensor to be measured by reference sensor transfer function compensation is obtained;The output signal of the vector sensor to be measured and the compensated signal are input into DHCM algorithm, and the background noise estimation of the sensor to be measured is obtained.The application is used to solve the problem that indirect measurement algorithm for different sensors will be distorted, aims at effectively improving the accuracy and environmental adaptability of low-frequency band background noise measurement.

Description

Technical Field

[0001] This invention belongs to the field of underwater acoustic sensor technology, specifically relating to a method and system for measuring the background noise of a vector sensor using adaptive transfer function equalization. Background Technology

[0002] Background noise is a key electroacoustic parameter characterizing the performance of underwater acoustic sensors, and its level directly determines the sensor's receiving sensitivity and signal-to-noise ratio. The background noise of vector sensors mainly originates from their core sensing unit and preamplifier circuit, specifically including various components such as electronic thermal noise, mechanical thermal noise, 1 / f noise, and shot noise. Accurate characterization of the low-noise characteristics of vector sensors relies on high-precision background noise measurement technology.

[0003] Currently, the main technical challenge in noise measurement using acoustic sensors lies in effectively suppressing environmental interference, including background noise, mechanical vibration, and electromagnetic interference. Compared to traditional acoustic barometers, vector sensors are more sensitive to vibration. Therefore, even in measurement systems with anti-interference designs, low-frequency environmental interference can still couple to the sensor input via the load-bearing structure in the form of vibration, leading to significant deviations in the measurement results.

[0004] In indirect measurement methods, existing techniques typically employ cross-spectral calculations between a reference sensor and the sensor under test (DUT) to eliminate the influence of environmental interference. However, this method relies heavily on the prior information of the consistency of the transfer functions between the two sensors. If the transfer functions of the reference sensor and the DUT differ, the cross-spectral calculation results will deviate significantly from the true noise floor of the DUT, greatly limiting the practical applicability and accuracy of this method in measuring the noise floor of vector sensors. Summary of the Invention

[0005] This invention provides a method and system for measuring the background noise of a vector sensor using adaptive transfer function equalization, which solves the problem of distortion caused by indirect measurement algorithms for different sensors, and aims to effectively improve the accuracy and environmental adaptability of low-frequency background noise measurement.

[0006] This invention is achieved through the following technical solution:

[0007] A method for measuring the noise floor of a vector sensor with adaptive transfer function equalization, the method comprising the following steps:

[0008] Step 1: Acquire the output signal of the vector sensor under test and the reference signal of the reference sensor;

[0009] Step 2: Input the reference signal from Step 1 into the adaptive filter, and dynamically update the weight coefficients of the adaptive filter through the adaptive algorithm to establish a time-varying transfer function model from the reference sensor to the sensor under test;

[0010] Step 3: Process the reference signal according to the time-varying transfer function model in Step 2 to obtain the compensation signal of the input signal to the sensor under test through the reference sensor transfer function;

[0011] Step 4: Input the output signal of the vector sensor under test from Step 1 and the compensated signal from Step 3 into the DHCM algorithm to obtain the noise floor estimate of the sensor under test.

[0012] Furthermore, the background noise of the sensor under test... With environmental noise Transfer function of the sensor under test The output signal is then directly measured at the output terminal of the sensor under test.

[0013]

[0014] The background noise of the reference sensor With environmental noise Transfer function of the sensor under test The output signal was then directly measured at the output terminal of the reference sensor.

[0015]

[0016] The output signal is directly measured at the output terminal of the reference sensor. The estimated signal after the adaptive filter is: ;

[0017] The formula for the adaptive filter error signal is:

[0018]

[0019] This is the difference between the output signal of the sensor under test and the compensated reference signal;

[0020] Will and The input to the DHCM algorithm is the noise floor of the sensor under test. .

[0021] Furthermore, step 2, the adaptive algorithm, specifically involves using the reference sensor output signal... As input, the adaptive filter error signal As feedback, its output is controlled through recursive operations. During the algorithm's convergence process, Approaching The convergence process of this algorithm ensures that the compensated reference signal... It can fit the noise component contained in the output signal of the sensor under test, thus equivalently completing the transfer function from the reference sensor. Transfer function to the sensor under test Compensation.

[0022] Furthermore, the DHCM algorithm in step 4 specifically involves the input being the output signal of the sensor under test. With the reference signal after adaptive filtering compensation The output value is the background noise voltage spectrum of the sensor under test. ;

[0023] Reference signal after adaptive filtering compensation The transfer function experienced and the output signal of the sensor under test Transfer function of the sensor under test Achieve fitting;

[0024] At this point, the background noise voltage spectrum of the sensor under test can be directly calculated by the following formula:

[0025]

[0026] in Output signal for the sensor under test The voltage self-power spectral density, It is the output signal of the sensor under test. With the output signal of the adaptive algorithm The amplitude coherence function in the frequency domain is defined as:

[0027]

[0028] in Output signal for the sensor under test With the output signal of the adaptive algorithm The cross-power spectral density.

[0029] An adaptive transfer function equalization vector sensor noise floor measurement system, wherein the measurement system uses the aforementioned adaptive transfer function equalization vector sensor noise floor measurement method, and the measurement system comprises:

[0030] Acquisition module: Acquires the output signal of the vector sensor under test and the reference signal of the reference sensor;

[0031] Time-varying transfer function model building module: The reference signal obtained by the acquisition module is input into the adaptive filter, and the weight coefficients of the adaptive filter are dynamically updated through the adaptive algorithm to build a time-varying transfer function model from the reference sensor to the sensor under test;

[0032] Transfer function compensation module: Based on the time-varying transfer function model obtained by the module, the time-varying transfer function model is used to process the reference signal and obtain the compensation signal of the input signal to the transfer function of the sensor under test through the reference sensor transfer function;

[0033] Sensor noise floor estimation module: The output signal of the vector sensor under test and the signal compensated by the transfer function compensation module are input into the DHCM algorithm to obtain the noise floor estimate of the sensor under test.

[0034] Furthermore, the measurement system operates as follows:

[0035] The background noise of the sensor under test With environmental noise Transfer function of the sensor under test The output signal is then directly measured at the output terminal of the sensor under test.

[0036]

[0037] The background noise of the reference sensor With environmental noise Transfer function of the sensor under test The output signal was then directly measured at the output terminal of the reference sensor.

[0038]

[0039] The output signal is directly measured at the output terminal of the reference sensor. The estimated signal after the adaptive filter is: ;

[0040] The formula for the adaptive filter error signal is:

[0041]

[0042] This is the difference between the output signal of the sensor under test and the compensated reference signal;

[0043] Will and The input to the DHCM algorithm is the noise floor of the sensor under test. .

[0044] Furthermore, the working process of the time-varying transfer function model establishment module is specifically as follows: using the reference sensor output signal... As input, the adaptive filter error signal As feedback, its output is controlled through recursive operations. During the algorithm's convergence process, Approaching The convergence process of this algorithm ensures that the compensated reference signal... It can fit the noise component contained in the output signal of the sensor under test, thus equivalently completing the transfer function from the reference sensor. Transfer function to the sensor under test Compensation.

[0045] Furthermore, the specific operation of the sensor background noise estimation module is as follows: the input quantity is the output signal of the sensor under test. With the reference signal after adaptive filtering compensation The output value is the background noise voltage spectrum of the sensor under test. ;

[0046] Reference signal after adaptive filtering compensation The transfer function experienced and the output signal of the sensor under test Transfer function of the sensor under test Achieve fitting;

[0047] At this point, the background noise voltage spectrum of the sensor under test can be directly calculated by the following formula:

[0048]

[0049] in Output signal for the sensor under test The voltage self-power spectral density, It is the output signal of the sensor under test. With the output signal of the adaptive algorithm The amplitude coherence function in the frequency domain is defined as:

[0050]

[0051] in Output signal for the sensor under test With the output signal of the adaptive algorithm The cross-power spectral density.

[0052] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method described above.

[0053] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0054] The beneficial effects of this invention are:

[0055] This invention ensures that the sensor under test and the reference sensor are co-located and synchronously acquired under the exact same test environment, so that the environmental interference coupled to the input terminals of the two sensors remains consistent. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of the measuring device of the present invention.

[0057] Figure 2 This is a schematic diagram comparing the noise floor estimation results of the sensor under test using the present invention with the noise floor estimation results of the sensor under test using direct DHCM.

[0058] Figure 3 This is a flowchart of the method of the present invention.

[0059] Reference numerals in this invention:

[0060] 1. Inner aluminum cylindrical closed tank; 2. Outer aluminum cylindrical closed tank; 3. Vibration damping platform; 4. Tension spring; 5. Reference sensor; 6. Sensor to be tested; 7. Radial sealing hole of inner aluminum cylindrical closed tank; 8. Signal acquisition device; 9. Vacuum pump; 10. Radial sealing hole of outer aluminum cylindrical closed tank; 11. Water. Detailed Implementation

[0061] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0062] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0063] It should also be understood that the terminology used in this application specification is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this application specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0064] The following is in conjunction with the appendix to this application specification. Figure 1-3 The technical solutions in the embodiments of this application are clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0065] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0066] Implementation Method 1

[0067] This embodiment provides a method for measuring the background noise of a vector sensor with adaptive transfer function equalization.

[0068] The measurement method uses, for example Figure 1 The test device shown has excellent sound insulation, vibration isolation, and electromagnetic shielding characteristics. Its main body consists of an inner aluminum cylindrical sealed tank 1 and an outer aluminum cylindrical sealed tank 2. The outer aluminum cylindrical sealed tank 2 is fixed to a vibration damping platform 3, and is suspended from its top cover by a tension spring 4. Water 11 is filled into the inner aluminum cylindrical sealed tank 1.

[0069] Both the reference sensor 5 and the sensor under test 6 are installed in the inner aluminum cylindrical closed tank 1 filled with water; the reference sensor 5 is adjacent to the sensor under test 6, that is, the two sensors are located at the same site and collect data synchronously.

[0070] The cables for both the reference sensor 5 and the sensor under test 6 are led out through the radial sealing holes 7 and 10 of the inner and outer aluminum cylindrical sealed tanks, respectively, and connected to the high-precision, low-noise signal acquisition unit 8. The signal acquisition unit 8 is equipped with a wireless transmission module, enabling communication with a host computer via a wireless local area network. The outer aluminum cylindrical sealed tank 2 is also connected to the vacuum pump 9, thus forming a complete vector sensor background noise testing device.

[0071] like Figure 3As shown, the measurement method includes the following steps:

[0072] Step 1: Acquire the output signal of the vector sensor under test and the reference signal of the reference sensor;

[0073] Step 2: Input the reference signal from Step 1 into the adaptive filter, and dynamically update the weight coefficients of the adaptive filter through the adaptive algorithm to establish a time-varying transfer function model from the reference sensor to the sensor under test;

[0074] Step 3: Process the reference signal according to the time-varying transfer function model in Step 2 to obtain the compensation signal of the input signal to the sensor under test through the reference sensor transfer function;

[0075] Step 4: Input the output signal of the vector sensor under test from Step 1 and the compensated signal from Step 3 into the DHCM algorithm to obtain the noise floor estimate of the sensor under test, such as... Figure 2 As shown.

[0076] Furthermore, the background noise of the sensor under test... With environmental noise Transfer function of the sensor under test The output signal is then directly measured at the output terminal of the sensor under test.

[0077]

[0078] The background noise of the reference sensor With environmental noise Transfer function of the sensor under test The output signal was then directly measured at the output terminal of the reference sensor.

[0079]

[0080] The output signal is directly measured at the output terminal of the reference sensor. The estimated signal after the adaptive filter is: ;

[0081] Adaptive filter error signal The formula is:

[0082]

[0083] This is the difference between the output signal of the sensor under test and the compensated reference signal, where, The reference signal after compensation;

[0084] Will and The input to the DHCM algorithm is the noise floor of the sensor under test. .

[0085] Furthermore, step 2, the adaptive algorithm, specifically involves using the reference sensor output signal... As input, the adaptive filter error signal As feedback, its output is controlled through recursive operations. During the algorithm's convergence process, Approaching The convergence process of this algorithm ensures that the compensated reference signal... It can fit the noise component contained in the output signal of the sensor under test, thus equivalently completing the transfer function from the reference sensor. Transfer function to the sensor under test Compensation.

[0086] Furthermore, the DHCM algorithm in step 4 specifically involves the input being the output signal of the sensor under test. With the reference signal after adaptive filtering compensation The output value is the background noise voltage spectrum of the sensor under test. ;

[0087] Reference signal after adaptive filtering compensation The transfer function experienced and the output signal of the sensor under test Transfer function of the sensor under test Achieve fitting;

[0088] At this point, the background noise voltage spectrum of the sensor under test can be directly calculated by the following formula:

[0089]

[0090] in Output signal for the sensor under test The voltage self-power spectral density, It is the output signal of the sensor under test. With the output signal of the adaptive algorithm The amplitude coherence function in the frequency domain is defined as:

[0091]

[0092] in Output signal for the sensor under test With the output signal of the adaptive algorithm The cross-power spectral density.

[0093] Implementation Method 2

[0094] This embodiment provides an adaptive transfer function equalization vector sensor noise floor measurement system. The measurement system uses the adaptive transfer function equalization vector sensor noise floor measurement method as described in Embodiment 1. The measurement system includes:

[0095] Acquisition module: Acquires the output signal of the vector sensor under test and the reference signal of the reference sensor;

[0096] Time-varying transfer function model building module: The reference signal obtained by the acquisition module is input into the adaptive filter, and the weight coefficients of the adaptive filter are dynamically updated through the adaptive algorithm to build a time-varying transfer function model from the reference sensor to the sensor under test;

[0097] Transfer function compensation module: Based on the time-varying transfer function model obtained by the module, the time-varying transfer function model is used to process the reference signal and obtain the compensation signal of the input signal to the transfer function of the sensor under test through the reference sensor transfer function;

[0098] Sensor noise floor estimation module: The output signal of the vector sensor under test and the signal compensated by the transfer function compensation module are input into the DHCM algorithm to obtain the noise floor estimate of the sensor under test.

[0099] Furthermore, the measurement system operates as follows:

[0100] The background noise of the sensor under test With environmental noise Transfer function of the sensor under test The output signal is then directly measured at the output terminal of the sensor under test.

[0101]

[0102] The background noise of the reference sensor With environmental noise Transfer function of the sensor under test The output signal was then directly measured at the output terminal of the reference sensor.

[0103]

[0104] The output signal is directly measured at the output terminal of the reference sensor. The estimated signal after the adaptive filter is: ;

[0105] The formula for the adaptive filter error signal is:

[0106]

[0107] This is the difference between the output signal of the sensor under test and the compensated reference signal;

[0108] Will and The input to the DHCM algorithm is the noise floor of the sensor under test. .

[0109] Furthermore, the working process of the time-varying transfer function model establishment module is specifically as follows: using the reference sensor output signal... As input, with error signal For feedback, its output is controlled through recursive operations. During the algorithm's convergence process, Approaching The convergence process of this algorithm ensures that the compensated reference signal... It can fit the noise component contained in the output signal of the sensor under test, thus equivalently completing the transfer function from the reference sensor. Transfer function to the sensor under test Compensation.

[0110] Furthermore, the specific operation of the sensor background noise estimation module is as follows: the input quantity is the output signal of the sensor under test. With the signal after adaptive filtering compensation The output value is the background noise voltage spectrum of the sensor under test. ;

[0111] After adaptive filtering compensation, the signal The transfer function experienced and the output signal of the sensor under test Transfer function Achieve fitting;

[0112] At this point, the background noise voltage spectrum of the sensor under test can be directly calculated by the following formula:

[0113]

[0114] in Output signal for the sensor under test The voltage self-power spectral density, It is the output signal of the sensor under test. With the output signal of the adaptive algorithm The amplitude coherence function in the frequency domain is defined as:

[0115]

[0116] in Output signal for the sensor under test With the output signal of the adaptive algorithm The cross-power spectral density.

[0117] Implementation Method 3

[0118] This invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor. The memory stores software programs and modules, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory and processor are connected via a bus. Specifically, the processor implements any step in Embodiment 1 by running the computer program stored in the memory.

[0119] It should be understood that, in the embodiments of the present invention, the processor may be a Central Processing Unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0120] Memory may include read-only memory, flash memory, and random access memory, and provides instructions and data to the processor. Some or all of the memory may also include non-volatile random access memory.

[0121] It should be understood that if the integrated modules / units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods described above can also be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0122] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0123] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the above device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0124] It should be noted that the methods and detailed examples provided in the above embodiments can be incorporated into the apparatus and devices provided in the embodiments for mutual reference, and will not be repeated here.

[0125] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0126] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / device embodiments described above are merely illustrative. For instance, the division of modules or units described above is merely a logical functional division, and in actual implementation, it can be divided in other ways. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0127] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for measuring the background noise of a vector sensor using adaptive transfer function equalization, characterized in that, The measurement method includes the following steps: Step 1: Acquire the output signal of the vector sensor under test and the reference signal of the reference sensor; Step 2: Input the reference signal from Step 1 into the adaptive filter, and dynamically update the weight coefficients of the adaptive filter through the adaptive algorithm to establish a time-varying transfer function model from the reference sensor to the sensor under test; Step 3: Process the reference signal according to the time-varying transfer function model in Step 2 to obtain the compensation signal of the input signal to the sensor under test through the reference sensor transfer function; Step 4: Input the output signal of the vector sensor under test from Step 1 and the compensated signal from Step 3 into the DHCM algorithm to obtain the noise floor estimate of the sensor under test; The background noise of the sensor under test With environmental noise Transfer function of the sensor under test The output signal is then directly measured at the output terminal of the sensor under test. The background noise of the reference sensor With environmental noise Transfer function of the sensor under test The output signal was then directly measured at the output terminal of the reference sensor. The output signal is directly measured at the output terminal of the reference sensor. The estimated signal after the adaptive filter is: ; Adaptive filter error signal The formula is: This is the difference between the output signal of the sensor under test and the compensated reference signal, where, The reference signal after compensation; Will and The input to the DHCM algorithm is the noise floor of the sensor under test. ; The adaptive algorithm in step 2 specifically involves using the output signal of the reference sensor. As input, the adaptive filter error signal As feedback, its output is controlled through recursive operations. During the algorithm's convergence process, Approaching The convergence process of this algorithm ensures that the compensated reference signal... It can fit the noise component contained in the output signal of the sensor under test, thus equivalently completing the transfer function from the reference sensor. Transfer function to the sensor under test Compensation.

2. The measurement method according to claim 1, characterized in that, The DHCM algorithm in step 4 specifically involves the input being the output signal of the sensor under test. With the reference signal after adaptive filtering compensation The output value is the background noise voltage spectrum of the sensor under test. ; Reference signal after adaptive filtering compensation The transfer function experienced and the output signal of the sensor under test Transfer function of the sensor under test Achieve fitting; At this point, the background noise voltage spectrum of the sensor under test can be directly calculated by the following formula: in Output signal for the sensor under test The voltage self-power spectral density, It is the output signal of the sensor under test. With the output signal of the adaptive algorithm The amplitude coherence function in the frequency domain is defined as: in Output signal for the sensor under test With the output signal of the adaptive algorithm The cross-power spectral density.

3. A vector sensor background noise measurement system with adaptive transfer function equalization, characterized in that, The measurement system uses the vector sensor background noise measurement method with adaptive transfer function equalization as described in any one of claims 1-2. The measurement system includes: Acquisition module: Acquires the output signal of the vector sensor under test and the reference signal of the reference sensor; Time-varying transfer function model building module: The reference signal obtained by the acquisition module is input into the adaptive filter, and the weight coefficients of the adaptive filter are dynamically updated through the adaptive algorithm to build a time-varying transfer function model from the reference sensor to the sensor under test; Transfer function compensation module: Based on the time-varying transfer function model obtained by the module, the time-varying transfer function model is used to process the reference signal and obtain the compensation signal of the input signal to the transfer function of the sensor under test through the reference sensor transfer function; Sensor noise floor estimation module: The output signal of the vector sensor under test and the signal compensated by the transfer function compensation module are input into the DHCM algorithm to obtain the noise floor estimate of the sensor under test; The measurement system operates as follows: The background noise of the sensor under test With environmental noise Transfer function of the sensor under test The output signal is then directly measured at the output terminal of the sensor under test. The background noise of the reference sensor With environmental noise Transfer function of the sensor under test The output signal was then directly measured at the output terminal of the reference sensor. The output signal is directly measured at the output terminal of the reference sensor. The estimated signal after the adaptive filter is: ; Adaptive filter error signal The formula is: This is the difference between the output signal of the sensor under test and the compensated reference signal, where, The reference signal after compensation; Will and The input to the DHCM algorithm is the noise floor of the sensor under test. ; The working process of the time-varying transfer function model establishment module is as follows: taking the output signal of the reference sensor as an example. As input, with error signal For feedback, its output is controlled through recursive operations. During the algorithm's convergence process, Approaching The convergence process of this algorithm ensures that the compensated reference signal... It can fit the noise component contained in the output signal of the sensor under test, thus equivalently completing the transfer function from the reference sensor. Transfer function to the sensor under test Compensation.

4. The measurement system according to claim 3, characterized in that, The specific operation of the sensor background noise estimation module is as follows: the input is the output signal of the sensor under test. With the reference signal after adaptive filtering compensation The output value is the background noise voltage spectrum of the sensor under test. ; Reference signal after adaptive filtering compensation The transfer function experienced and the output signal of the sensor under test Transfer function of the sensor under test Achieve fitting; At this point, the background noise voltage spectrum of the sensor under test can be directly calculated by the following formula: in Output signal for the sensor under test The voltage self-power spectral density, It is the output signal of the sensor under test. With the output signal of the adaptive algorithm The amplitude coherence function in the frequency domain is defined as: in Output signal for the sensor under test With the output signal of the adaptive algorithm The cross-power spectral density.

5. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the method as described in any one of claims 1-2.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-2.

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