Multi-frame medium signal-to-noise ratio channel equalization method based on measured data

Abstract

The invention particularly relates to a multi-frame medium signal-to-noise ratio channel equalization method based on measured data, and the method comprises the steps: carrying out the MTD of single-frame CPI data based on a multi-frame pulse coherent accumulation principle, achieving the clutter separation of a moving target and a ground object, extracting a target echo, carrying out the pulse compression processing, carrying out the envelope alignment and peak value normalization of multi-frame pulse pressure data, and carrying out the equalization of a multi-frame medium signal-to-noise ratio channel. Inter-frame complex number accumulation is carried out after normalization, on the premise that extra hardware cost input is not needed, the main lobe and side lobe ratio gain of the target pulse pressure result is remarkably improved, meanwhile, accurate channel equalization is achieved with the ideal pulse pressure result as the reference, dependence of a traditional channel equalization algorithm on the high signal-to-noise ratio is avoided, and MTD moving target detection of each frame of data can be taken into consideration. The actual measurement result shows that the algorithm can effectively improve the echo signal-to-noise ratio through coherent accumulation of multi-frame target pulse pressure results under the signal-to-noise ratio in a single frame, then carries out channel equalization, is convenient to implement, and can greatly improve the experiment efficiency of an external field.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar signal processing, and particularly relates to a multi-frame medium SNR channel equalization method based on measured data. BACKGROUND

[0002] When there is in-band amplitude and phase imbalance in a radar receiving channel, the in-band amplitude and phase low-frequency components will cause the main lobe of the pulse compression envelope to spread, and the high-frequency components will cause the sidelobe to be lifted, so that the signal-to-noise ratio is reduced, which affects target detection and target recognition; at the same time, the in-band amplitude and phase imbalance will also cause the single-pulse angle measurement accuracy of the radar system to decrease. Therefore, channel equalization must be performed on the radar channel to ensure that the performance of the radar system meets the design requirements.

[0003] Channel equalization coefficient acquisition usually has laboratory signal injection method, field strong point target test method and the like. The laboratory signal injection method cannot monitor the amplitude and phase characteristic change law of the receiving channel in real time; the field strong point target test method has a very high requirement on the clutter distribution of the experimental site, and requires a signal-to-clutter ratio with sufficient strength.

[0004] The core logic of the MTD technology is based on the motion characteristic difference between the target and the clutter: the target usually has a radial motion speed, and the echo signal thereof will produce a recognizable Doppler shift; and the radial speed of the clutter (such as ground objects, clouds and rain, etc.) is close to zero, and the Doppler shift is concentrated around zero frequency. By performing Fourier transform on different pulse data received by a single frame cpi, the time domain signal is mapped to the Doppler frequency domain, so that the target and the clutter form different energy peaks in the frequency domain, and then a targeted frequency domain filter is designed to realize the separation of the target and the clutter and the extraction of the target echo.

[0005] The pulse compression technology has been widely applied in the fields of radar, communication and the like by transmitting a wide pulse signal to ensure energy and receiving the signal to compress the pulse width through matched filtering, thereby improving the signal resolution and enhancing the anti-interference capability. In the prior art, the pulse compression is usually only used as a "signal preprocessing link" to improve the signal-to-noise ratio of a single frame signal. The channel equalization method based on the pulse compression result has a higher requirement on the main-to-sidelobe ratio: it needs to be performed when the main-to-sidelobe ratio is above 40db, so as to equalize the channel based on the pulse compression result of the ideal transmitted signal, to analyze the distortion characteristics of the received signal of the channel, and to construct an accurate equalization filter. However, under the condition of weak signal-to-noise ratio, the traditional channel method is difficult to accurately extract the distortion information of the channel, and the equalization performance of the equalization filter is prone to deterioration and failure.

[0006] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present application, and therefore can include information which does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0007] In order to solve the problems of the dependence of the traditional channel equalization on high signal-to-noise ratio and the failure of equalization under weak signal, the application provides a multi-frame medium signal-to-noise ratio channel equalization method based on measured data, aiming at solving the problem that the channel equalization coefficient is difficult to be stably obtained under the condition of medium signal-to-noise ratio.

[0008] Other characteristics and advantages of the present application will become apparent from the following detailed description, or will be learned by practice of the present application.

[0009] According to a first aspect of the present application, a multi-frame medium signal-to-noise ratio channel equalization method based on measured data is provided, and the method comprises: Performing moving target detection (MTD) processing on the original signal received by the channel, utilizing the difference in Doppler frequency domain characteristics between the target and the clutter, suppressing the fixed clutter and the low-speed clutter through a filter group, separating the target signal and the clutter, and obtaining a preliminary target signal sequence; Performing pulse compression processing on the preliminary target signal sequence in a matched filter mode to obtain a single-frame target pulse compression result; and performing normalization processing on the multi-frame target pulse compression results respectively, so that the amplitude of each frame of pulse compression result is in a unified order of magnitude; According to the time delay deviation, the envelope of the normalized multi-frame pulse compression results is aligned to ensure that the target is consistent in different frames; and based on the principle of coherent accumulation, the multi-frame pulse compression results after alignment and normalization processing are superimposed; Using a time domain or frequency domain equalization algorithm, the target pulse compression comprehensive result after coherent accumulation is compared with an ideal pulse compression result to realize channel equalization calibration.

[0010] In some exemplary embodiments, the receiving method of the original signal is to collect N frames of original echo signals in a coherent processing interval (CPI) of a digital array radar.

[0011] In some exemplary embodiments, the original signal expression is

[0012] Among them, , , Target signal, clutter, and noise, respectively; After filtering, the signal only includes the target and the noise: .

[0013] In some exemplary embodiments, the pulse compression processing on the preliminary target signal sequence in a matched filter mode to obtain a single-frame target pulse compression result is specifically: Using matched filter coefficients symmetric with the conjugate to each frame of target echo after MTD processing The pulse pressure is completed by convolution operation, and a single-frame target pulse pressure sequence is obtained .

[0014] In some example embodiments, the multiple frames of target pulse pressure results are respectively normalized, specifically: The main lobe peak amplitude of the single pulse pressure signal is taken as a reference to calculate the normalization coefficient of each frame:

[0015] Wherein represents the time corresponding to the pulse pressure peak value; The nth frame of pulse pressure sequence is adjusted to: .

[0016] In some example embodiments, the multiple frames of pulse pressure results after alignment and normalization are superimposed based on the principle of coherent accumulation, specifically: The envelope of each frame of pulse pressure signal is aligned, that is, the same sequence length is taken as the final normalized pulse pressure result of each frame based on , and the coherent accumulation is realized by superimposition to obtain the accumulated pulse pressure signal:

[0017] Wherein, is a normalization factor.

[0018] In some example embodiments, the time domain or frequency domain equalization algorithm is used to compare the accumulated target pulse pressure comprehensive result with the ideal pulse pressure result to realize channel equalization calibration; specifically: If time domain equalization is used, an order FIR filter is constructed, and the weight coefficient is solved by least square criterion to make the equalization output

[0019] is the tap input vector of , and the weight value

[0020] is the autocorrelation matrix, is the cross-correlation matrix; is an ideal pulse pressure sequence; If frequency domain equalization method is used, the distortion characteristics are subjected to FFT transformation, and a frequency domain equalization filter is designed to realize frequency domain compensation of channel mismatch.

[0021] ​According to a second aspect of the present application, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the method for equalizing a channel of a plurality of frames with a medium signal-to-noise ratio based on measured data according to the first aspect.

[0022] According to a third aspect of the present application, there is provided a computer program product having stored thereon a computer program which, when executed by a processor, implements the method for equalizing a channel of a plurality of frames with a medium signal-to-noise ratio based on measured data according to the first aspect.

[0023] According to a fourth aspect of the present application, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to implement the method for equalizing a channel of a plurality of frames with a medium signal-to-noise ratio based on measured data according to the first aspect via execution of the executable instructions.

[0024] The method for equalizing a channel of a plurality of frames with a medium signal-to-noise ratio based on measured data provided by the embodiments of the present application first performs MTD on a channel received signal, separates a target and clutter into dynamic and static parts, extracts a target signal for pulse compression, performs normalization and coherent accumulation after aligning the envelope of the target pulse compression results of a plurality of frames, and then performs channel equalization with ideal pulse compression results.

[0025] Compared with the prior art, the present application has the following advantages: (1) Simplify the complexity of equalization algorithm. The present application uses MTD dynamic and static separation + multi-frame coherent accumulation preprocessing to filter out fixed clutter and raise the mainlobe-to-sidelobe ratio of pulse compression to more than 40dB, without additional complex preprocessing steps, thus simplifying the theoretical derivation and engineering implementation logic of the equalization process.

[0026] (2) Break through the bottleneck of weak channel equalization accuracy. The accuracy of the channel equalization method based on pulse compression results is highly dependent on the premise of high mainlobe-to-sidelobe ratio of the signal. In a weak channel, the mainlobe-to-sidelobe ratio of single-frame pulse compression is insufficient due to the mixing of target and clutter, which is prone to distortion feature misjudgment. The present application uses the characteristics of linear superposition of noise energy and square superposition of target energy, and through coherent accumulation (such as 10 CPI accumulation to improve 10dB energy), the mainlobe-to-sidelobe ratio of pulse compression is raised to more than 40dB, so that the accumulated signal is completely aligned with the mainlobe shape of the ideal pulse compression reference, solving the problem of equalization failure in a weak channel.

[0027] (3) Balance flexibility and stability. Adapt to multiple scene requirements: the existing balance method depends on the extraction of the received signal, and there is a problem of insufficient flexibility; the present application can extract the target signal through MTD, and can dynamically adjust the accumulated frame number (10-50 CPI) according to the difference between the target pulse pressure result intensity and the minimum balance requirement intensity, taking into account the flexibility and stability of the algorithm.

[0028] The method can reduce the requirement for external field experiments, does not need to specially fly a cooperative target, can real-time record non-cooperative targets such as flying birds and unmanned aerial vehicles in an experimental site, and can realize the calculation of the channel equalization coefficient of a radar system through corresponding processing.

[0029] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not limiting to the present application. BRIEF DESCRIPTION OF DRAWINGS

[0030] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained from these drawings without creative labor for those skilled in the art.

[0031] Figure 1 The present application is based on a channel equalization method flowchart of inter-frame pulse pressure accumulation. Figure 2 Multi-frame cpi pulse pressure accumulation effect diagram. Figure 3 Effect diagram before and after accumulation and channel equalization. DETAILED DESCRIPTION

[0032] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations, however, can be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of example implementations to those skilled in the art. The described features, structures, or characteristics can be combined in one or more implementations in any suitable manner.

[0033] Moreover, the attached drawings are only schematic and are non- necessarily to scale and the dimensions of the various features can have been exaggerated for the sake of clarity. Like reference numerals designate like structural elements throughout the various drawings, and a detailed description has not been provided with respect to the same to avoid obscuring the present description. Some of the drawings can be schematic or exaggerated representations of concepts not drawn to scale. The functional blocks can be implemented in software or hardware or a combination thereof, in one or more hardware modules or integrated circuits, in different network and / or processor devices and / or microcontroller devices.

[0034] The channel equalization method based on pulse compression in a low signal-to-noise ratio weak channel environment faces two problems: first, in a weak channel, the target signal in the radar echo is mixed with clutter and noise, and direct processing before dynamic and static separation can easily lead to interference of the subsequent pulse compression and equalization accuracy; second, the ideal pulse compression result needs to be used as a reference signal to achieve channel equalization, which requires that the mainlobe-to-sidelobe ratio of the actual received target signal pulse compression result be greater than 40db, and if the mainlobe-to-sidelobe ratio of the single-frame pulse compression result is low, it is difficult to accurately compare with the ideal pulse compression result, resulting in equalization failure.

[0035] In view of the shortcomings and deficiencies of the prior art, a multi-frame medium signal-to-noise ratio channel equalization method based on actual measurement data is provided in the example embodiment, which aligns the envelopes of the multi-frame pulse compression data, normalizes and performs inter-frame coherent accumulation, uses the time correlation of the signal to superimpose target signal energy, and simultaneously suppresses random noise, thereby improving the signal-to-noise ratio of the signal; then, the distortion difference between the accumulated signal and the ideal signal is compared based on the ideal pulse compression result, and an accurate equalization model is constructed. This method does not require additional hardware investment and can break through the bottleneck of weak channel equalization through algorithm optimization, taking into account low cost and high reliability.

[0036] Reference Figure 1 As shown, the method can specifically include the following steps: Step S1, channel received signal MTD processing: realizing target and clutter dynamic and static separation; MTD processing realizes dynamic and static separation. The original signal received by the channel is subjected to moving target detection (MTD) processing, the characteristics of the target and clutter in the Doppler frequency domain are used, and the fixed clutter and low-speed clutter are suppressed through a designed filter group to separate the target signal from the complex clutter background and obtain a preliminary target signal sequence.

[0037] Step S2, target signal pulse compression processing: obtaining a single-frame target pulse compression result; Target signal pulse compression and normalization. The target signal sequence extracted after MTD processing is subjected to pulse compression (pulse compression) processing in a matched filter manner to improve the peak power of the target signal and compress the signal pulse width, thereby obtaining a single-frame target pulse compression result. In order to eliminate the accumulated errors caused by the differences in signal intensity between different frames, the multi-frame target pulse compression results are subjected to normalization processing respectively, so that the amplitude of each frame pulse compression result is in the same order of magnitude.

[0038] Step S3, multi-frame pulse compression result normalization and coherent accumulation; Envelope alignment and coherent accumulation. For the multi-frame pulse compression results, first, the envelope of each frame pulse compression result is aligned according to the time delay deviation to ensure that the position of the target in different frames remains consistent. Then, normalization processing is performed, and then based on the principle of coherent accumulation, the multi-frame pulse compression results after alignment and normalization processing are superimposed, fully utilizing the characteristics that the noise energy superimposes linearly and the target energy superimposes quadratically, so that the main-to-side lobe ratio of the target pulse compression is increased.

[0039] Step S4, channel equalization implementation based on ideal pulse compression result; Channel equalization by comparing with ideal pulse compression result. The ideal pulse compression result under the corresponding scene is generated by theoretical calculation, which is used as the reference benchmark for channel equalization. According to the accumulated target pulse compression comprehensive result and the ideal pulse compression result, time domain or frequency domain equalization algorithm is used to realize equalization calibration.

[0040] In the following, each step in the example embodiment will be described in more detail with reference to the accompanying drawings and examples.

[0041] Step S1 specifically includes: Step S1.1, signal acquisition and preprocessing: N frames (N can be adjusted according to the required improvement gain intensity) of original echo signals are acquired according to the coherent processing interval (CPI) of the digital array radar, and each frame contains M=256 pulses. The original signal expression is

[0042] wherein, , , are target signal, clutter, and noise, respectively.

[0043] Step S1.2, MTD Doppler filtering: a Doppler filtering algorithm based on FFT is used. The Doppler frequency of the clutter (such as the echo of fixed ground objects) is close to 0, and the target signal has a non-zero Doppler frequency. The Doppler frequency difference between the target and the clutter is used to realize dynamic and static separation, thereby obtaining a signal containing only the target and the noise

[0044] This step can avoid the interference of clutter on subsequent pulse compression and equalization.

[0045] Step S2 specifically includes: Step S2.1, reference signal generation: call the known parameters of the transmitting end of the digital array radar to generate a reference signal consistent with the transmitted signal , which serves as the matching reference for pulse compression processing, and provides the basis for subsequent ideal pulse compression results generation.

[0046] Step S2.2, single-frame pulse compression: for each frame of target echo after MTD processing , use the conjugate symmetric matched filter coefficient , complete the pulse compression by convolution operation to obtain a single-frame target pulse compression sequence At this time, the pulse compression signal has preliminarily focused the target energy, but for weak energy target echoes, the main-to-side lobe ratio of single-frame pulse compression is usually only 25-35dB, which does not meet the 40dB equalization reference requirement, and there is amplitude fluctuation caused by channel mismatch. Step S3 specifically includes: Step S3.1, inter-frame normalization processing: because the amplitude and phase characteristics of each received echo signal may differ, and the noise interference factor is large under weak signal-to-noise ratio, directly superimposing multiple frame pulse compression results will cause energy cancellation or amplitude overflow. Take the main lobe peak amplitude of the single pulse compression signal as the reference to calculate the normalization coefficient of each frame

[0047] , wherein represents the time corresponding to the pulse compression peak value; , wherein the nth frame pulse compression sequence is adjusted to

[0048] to complete the normalization processing of all frame pulse compression signals.

[0049] Step S3.2, coherent accumulation calculation: align the envelope of each frame pulse compression signal, i.e. , take the same sequence length as the final normalized pulse compression result of each frame as the reference, and perform accumulation to realize coherent accumulation to obtain the accumulated pulse compression signal:

[0050] , wherein is a normalization factor to avoid amplitude overflow. This process utilizes the coherence of the target signal to achieve quadratic growth, and the incoherence of the noise to achieve linear energy growth. In theory, 10 CPI accumulations can improve the signal gain by 10dB, increasing the main-to-side lobe ratio of pulse compression from about 30dB of single frame to more than 40dB, meeting the requirement of the main-to-side lobe ratio of the equalization reference.

[0051] Step S4 specifically includes: Step S4.1, Ideal Pulse Compression Reference Generation: Under ideal conditions of "no channel mismatch and no noise interference," the reference signal is... Perform the same pulse compression processing to obtain the ideal pulse compression sequence.

[0052] Its main lobe-to-side lobe ratio is strictly controlled above 40dB, providing a reference pulse compression signal for the design of the expected response of the equalizer.

[0053] Step S4.2, Equalization Filter Design: Combining the core idea of ​​the least squares fitting method, channel equalization is achieved using both time-domain and frequency-domain methods. Weight coefficient solution: If time-domain equalization is used, construct... For an FIR filter of order 1, the weight coefficients are solved using the least squares criterion. To achieve balanced output

[0054] for Tap input vector pass Find the weights

[0055] The autocorrelation matrix is... The cross-correlation matrix is ​​given. If the frequency domain equalization method is used, the distortion characteristics are transformed by FFT, and a frequency domain equalization filter is designed to achieve frequency domain compensation for channel mismatch.

[0056] The method of the present invention is further illustrated below through simulation: like Figure 2 The image shows the effect of multi-frame CPI pulse pressure accumulation. Here, 30 frames of CPI pulse pressure are selected for accumulation. Blue represents the intensity distribution before accumulation, and red represents the intensity effect after accumulation.

[0057] like Figure 3 As shown, the images depict the effects before and after channel equalization following accumulation. Before equalization, the main lobe pulse pressure exhibits significant envelope distortion, differing considerably from the ideal pulse pressure result. After equalization, the main lobe distortion has been corrected, and the pulse pressure result is close to the ideal pulse pressure result.

[0058] The application is based on the principle of multi-frame pulse coherent accumulation, first, MTD is realized on single frame CPI data to separate moving targets and ground clutter, then target echoes are extracted for pulse compression processing, then multi-frame pulse compression data are taken for envelope alignment and peak normalization, after normalization, inter-frame complex accumulation is carried out, on the premise of no additional hardware cost investment, the main and side lobe ratio gain of target pulse compression result is significantly improved, and accurate channel equalization is realized based on ideal pulse compression result, which not only avoids the dependence of traditional channel equalization algorithm on high signal-to-noise ratio, but also takes into account the MTD moving target detection of each frame of data. The test results show that the algorithm can effectively improve the echo signal-to-noise ratio by coherent accumulation of multi-frame target pulse compression results under single frame signal-to-noise ratio, and then channel equalization is carried out, which is easy to realize and can greatly improve the experimental efficiency of the field.

[0059] The units described in the embodiments of the application can be implemented in the form of software or hardware, and the described units can also be arranged in a processor. In some cases, the names of these units do not constitute a limitation on the units themselves.

[0060] It should be noted that, as another aspect, the present application also provides a storage medium, which can be included in an electronic device or exist independently without being assembled into the electronic device. The storage medium carries one or more programs, and when the one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiments.

[0061] In one embodiment, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the above method embodiments.

[0062] In addition, the above figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not for limiting purposes. It is easy to understand that the processes shown in the above figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be executed synchronously or asynchronously, for example, in multiple modules.

[0063] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the application be limited only by the claims, which follow, and the equivalents thereof. The specification and examples given are exemplary only and are not intended to limit the true scope and spirit of the application. The true scope and spirit of the application are indicated by the claims.

[0064] It should be understood that the application is not limited to the precise construction which has been described above and which shown in the drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application. The scope of the application should be determined by the claims appended hereto.

Claims

1. A multi-frame medium signal-to-noise ratio channel equalization method based on measured data, characterized in that, The method includes: The raw signal received by the channel is processed by moving target detection (MTD) to utilize the difference in Doppler frequency domain characteristics between the target and clutter. Fixed clutter and low-speed clutter are suppressed by a filter group, the target signal and clutter are separated, and a preliminary target signal sequence is obtained. The initial target signal sequence is pulse compressed using matched filtering to obtain a single-frame target pulse compression result; the pulse compression results of multiple frames are normalized to ensure that the amplitude of each frame's pulse compression result is on the same order of magnitude. The pulse compression results after normalization of multiple frames are envelope aligned based on the time delay deviation to ensure that the target position is consistent in different frames; the pulse compression results after alignment and normalization are superimposed based on the coherent accumulation principle. By employing time-domain or frequency-domain equalization algorithms, the accumulated target pulse compression results are compared with the ideal pulse compression results to achieve channel equalization calibration.

2. The multi-frame medium signal-to-noise ratio channel equalization method based on measured data according to claim 1, characterized in that, The method for receiving the original signal is as follows: N consecutive frames of original echo signals are acquired according to the coherent processing interval (CPI) of the digital array radar.

3. The multi-frame medium signal-to-noise ratio channel equalization method based on measured data according to claim 2, characterized in that, The original signal expression is: in, , , These are the target signal, clutter, and noise, respectively. The filtered signal contains only the target and noise: 。 4. The multi-frame medium signal-to-noise ratio channel equalization method based on measured data according to claim 3, characterized in that, The preliminary target signal sequence is subjected to pulse compression processing using matched filtering to obtain a single-frame target pulse compression result, specifically as follows: Using matched filter coefficients that are symmetric to conjugate, the target echo in each frame after MTD processing is... Pulse compression is performed through convolution operations to obtain a single-frame target pulse compression sequence. .

5. The multi-frame medium signal-to-noise ratio channel equalization method based on measured data according to claim 4, characterized in that, The normalization process for the target pulse compression results of multiple frames is as follows: Using the main lobe peak amplitude of the single pulse compression signal as a benchmark, the normalization coefficient for each frame is calculated: in This represents the time corresponding to the peak pulse pressure. At the corresponding pulse pressure peak time, the pulse pressure sequence of the nth frame is adjusted as follows: 。 6. The multi-frame medium signal-to-noise ratio channel equalization method based on measured data according to claim 5, characterized in that, The method of superimposing the multi-frame pulse compression results after alignment and normalization based on the coherent accumulation principle is as follows: Envelope alignment is performed on each frame of pulse compression signal, i.e. Using the same sequence length as the baseline, the final normalized pulse compression result for each frame is obtained, and the result is accumulated to achieve coherence, thus obtaining the accumulated pulse compression signal: in, This is the normalization factor.

7. The multi-frame medium signal-to-noise ratio channel equalization method based on measured data according to claim 6, characterized in that, The method employs a time-domain or frequency-domain equalization algorithm to compare the accumulated target pulse compression result with the ideal pulse compression result, thereby achieving channel equalization calibration; specifically: If time-domain equalization is used, construct For an FIR filter of order 1, the weight coefficients are solved using the least squares criterion. To achieve balanced output for Tap input vector pass Find the weights The autocorrelation matrix is... This is the cross-correlation matrix; For ideal pulse pressure sequences; If the frequency domain equalization method is used, the distortion characteristics are transformed by FFT, and a frequency domain equalization filter is designed to achieve frequency domain compensation for channel mismatch.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the multi-frame medium signal-to-noise ratio channel equalization method based on measured data as described in any one of claims 1 to 7.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-frame medium signal-to-noise ratio channel equalization method based on measured data as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the multi-frame medium signal-to-noise ratio channel equalization method based on measured data according to any one of claims 1 to 7 by executing the executable instructions.

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