Sampling and positioning method for airborne impact synchronous monitoring

Abstract

The invention belongs to the technical field of impact monitoring of structural components, particularly relates to a sampling and positioning method for airborne impact synchronous monitoring, and solves the problem that an impact source cannot be identified and positioned due to poor sampling time synchronism in an existing airborne impact monitoring system method; the technical problem that an existing health monitoring system can only scan a single structural part and cannot monitor the whole structure in all time is solved. The method comprises the following steps: firstly, establishing an aircraft airborne impact monitoring device, wherein the impact monitoring device comprises a measurement module, a signal conditioning module, an AD conversion module, an FPGA control module, an eMMC storage module, an RS422 communication module and a power supply module for supplying power to the whole device; secondly, on the basis of an impact recognition algorithm of threshold judgment and an impact source positioning algorithm of RSSI + TDOA, the propagation time difference and attenuation characteristics of impact guided wave signals among the sensors are analyzed, and finally the optimal position of an impact source is obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of the testing method of structural components, and specifically relates to the impact monitoring of structural components, in particular to a sampling and positioning method for airborne impact synchronous monitoring. Background Art

[0002] In recent years, with the annual increase in the global air passenger volume, the number of daily flight takeoffs and landings has exceeded 100,000 sorties. This has brought a severe test to the safety of aircraft. According to incomplete statistics, about 480 foreign object strikes occur globally every day, and about 2% of these strikes will cause serious damage to the aircraft structure, thus threatening flight safety. In response to the increasingly severe threat, Boeing and Airbus have developed airborne SHM (Structural Health Monitoring) equipment for bird strike identification during takeoff and landing; third-party equipment suppliers such as Honeywell have also specifically launched the HUMS (Health and Usage Monitoring System) system for the health monitoring and life management of key aircraft components. Corresponding monitoring methods have also been proposed in the domestic field of aircraft impact monitoring.

[0003] "Dai Yu, Zhang Jie. A multi-channel flexible strain sensing system with autonomous temperature compensation [J]. Transducer and Microsystem Technologies, 2024, 43(02): 97-100. DOI: 10.13873" proposed a multi-channel sensor acquisition and storage device. Its analog acquisition part uses a similar front-end architecture of "multi-channel matrix switch + ADC alternating sampling". This type of architecture is convenient for expanding sensor channels to support a huge number of sensor arrays. However, this type of system relies on a multi-channel matrix switch to balance the contradiction between a large number of data channels and fewer ADC sampling channels. Due to the switching time of the multi-channel matrix switch, the sampling time bases between multiple channels are different. For an impact event recognition system, determining a unified time base is the basic guarantee for subsequent data processing and position recognition. Even if the waiting time in the T / H (sample / hold) stage can be reduced by increasing the ADC sampling rate, the final sampling time error can only be reduced to the microsecond level. In particular, the device in "Jin Lu, Miao Sien. Design of a multi-channel high-speed sampling system based on FPGA [J]. Electronic Technology, 2014, 43(02): 22-26" uses multiple ADCs for parallel sampling to improve the synchronous sampling time accuracy. However, due to the existence of the sensor signal front-end processing circuit, parameter mismatches of any discrete components may cause phase shifts in the signal during transmission. Simply using hardware methods can only adjust the sampling time error of the ADC and cannot eliminate the error brought by the analog signal during transmission. Especially considering the deviation of the line length during sensor installation and layout, the actual sampling time error between channels is difficult to eliminate, which seriously affects the subsequent identification and positioning of the impact source.

[0004] In addition, the airborne structural health monitoring systems developed by companies such as Boeing mentioned above all use active monitoring methods. The basic principle is to use active excitation sources such as piezoelectric guided waves and acoustic emissions to apply excitation to structural components, receive signals by a sensor array, and detect potential damages in the structural components by analyzing the signals. However, this method is only suitable for ground maintenance and cannot achieve real-time monitoring. If a foreign object impact occurs during flight, the pilot cannot obtain the impact information in time, which has limited improvement on flight safety; and it can only scan a single structural component at a time and cannot monitor the overall structure all the time, so there are relatively large limitations in practical applications. Summary of the Invention

[0005] To overcome the technical defects in the existing methods of airborne impact monitoring systems, where the sampling time error is relatively large, seriously affecting the subsequent identification and positioning of the impact source; and the existing airborne health monitoring systems cannot perform real-time monitoring, can only scan a single structural component, and cannot monitor the overall structure all the time, the present invention provides a sampling and positioning method for airborne impact synchronous monitoring.

[0006] The present invention provides a sampling and positioning method for airborne shock synchronization monitoring, and constructs an airborne shock monitoring device. The shock monitoring device includes a measurement module, a signal conditioning module, an AD conversion module, an FPGA control module, an eMMC storage module, an RS422 communication module, and a power supply module for powering the entire shock monitoring device; the measurement module includes a plurality of piezoelectric sensors attached to the surfaces of the fuselage, wings, and aircraft engines for detecting shock waves. When the corresponding structural members are impacted by a shock source, shock waves are generated on the surface of the structural members, and the piezoelectric sensors can convert the stress signals transmitted by the shock waves on the structural members into charge signals based on the piezoelectric effect; the charge signals output by the measurement module are sequentially transmitted to the FPGA control module through the signal conditioning module and the AD conversion module. The FPGA control module controls the AD conversion module to synchronously sample the plurality of piezoelectric sensors of the measurement module. The sampled data is filtered and framed inside the FPGA control module and then sent to the FIFO buffer to be triggered. When the signal of a certain piezoelectric sensor reaches the preset trigger threshold, the FPGA control module intercepts a complete signal of 500 ms before and after the shock as the shock signal and stores it in the eMMC storage module to provide support for subsequent shock position analysis and feature extraction. Subsequently, the FPGA control module analyzes the stored shock signals using a positioning algorithm based on TDOA and RSSI to calculate the position of the shock source. Finally, the FPGA control module frames the time of shock occurrence, the shock magnitude, and the calculated shock source position information, and sends them to the on-board control system through the RS422 communication module; the steps of the positioning algorithm based on TDOA and RSSI are as follows:

[0007] S1. First, determine the monitoring area coordinate system and the corresponding coordinates of the piezoelectric sensors according to the placement positions of the piezoelectric sensors on the aircraft. Assume that the positions of the piezoelectric sensors are respectively , , … ;

[0008] S2. According to the 500-ms shock signal obtained by the FPGA control module, extract the shock time and shock amplitude characteristics. Record the sampling time when the amplitude of each piezoelectric sensor first exceeds the trigger threshold as the time when the shock wave reaches the corresponding piezoelectric sensor. Among them, the times when the shock wave reaches the piezoelectric sensors are respectively , , … ; Record the maximum value of the shock wave received by each piezoelectric sensor as the shock amplitude. Then the shock amplitudes of the piezoelectric sensors are respectively , , … , The maximum impact amplitude among the impact amplitudes corresponding to the piezoelectric sensors is denoted as ; assuming that the propagation speed of the impact guided wave on the aircraft is , and the position of the impact source is ;

[0009] S3, The TDOA algorithm calculates the position of the impact source by measuring the time difference of the impact guided wave arriving at different piezoelectric sensors and then combining the position information of the piezoelectric sensors. Therefore, first calculate the distance between each piezoelectric sensor as:

[0010] ;

[0011] Formula in, is the th piezoelectric sensor, is the th piezoelectric sensor;

[0012] The ideal time difference between the th piezoelectric sensor and the th piezoelectric sensor is :

[0013] ;

[0014] The actual time difference of the impact guided wave arriving at the i-th piezoelectric sensor and the j-th piezoelectric sensor is :

[0015] ;

[0016] Formula in, is the actual time when the impact guided wave arrives at the th piezoelectric sensor, the actual time when the impact guided wave arrives at the th piezoelectric sensor;

[0017] Then the error between the ideal time difference and the actual time difference is:

[0018] ;

[0019] For piezoelectric sensors, through the above steps, cycle and calculate the th piezoelectric sensor and the The error between piezoelectric sensors is obtained, and the sum of the squares of the total error is as follows:

[0020] ;

[0021] S4. Calculate the distance from the impact source to each piezoelectric sensor according to the planar distance formula:

[0022] ;

[0023] During the transmission process, the impact source attenuates due to the damping effect. According to the RSSI algorithm, the signal amplitude is inversely proportional to the square of the distance. Then the error function Based on the above relationship, it is expressed as the difference between the signal intensity predicted by the model and the actually measured signal intensity at the th piezoelectric sensor. The calculation formula of the error function is as follows:

[0024] ;

[0025] In the formula , is the impact amplitude of the th piezoelectric sensor; , is the total number of piezoelectric sensors;

[0026] For piezoelectric sensors, use the above steps to calculate its error function in a loop, and the sum of the squares of the total error obtained is as follows:

[0027] ;

[0028] S5. Weighted sum the sum of the squares of the errors obtained by the TDOA algorithm and the RSSI algorithm in a certain proportion to obtain the total error sum:

[0029] ;

[0030] In the formula , is the weight of the sum of the squares of the errors obtained by the RSSI algorithm, is the weight of the sum of the squares of the errors obtained by the TDOA algorithm;

[0031] S6. Use the nonlinear least squares method to iteratively solve the total error sum and finally obtain the optimal position of the impact source.

[0032] The method of the present invention greatly improves the time accuracy of multi-channel synchronous sampling, determines a unified sampling time base for multi-sensor data, and facilitates the analysis of impact events; it can monitor and identify impact events in real time throughout the flight of the aircraft, and can further accurately locate the position of the impact source.

[0033] Preferably, the signal conditioning module includes a signal acquisition and filtering processing circuit and charge-voltage conversion units corresponding to piezoelectric sensors. The signal acquisition and filtering processing circuit includes a filtering circuit, an amplifying circuit, and a limiting circuit. The charge signals of piezoelectric sensors are converted into voltage signals through the corresponding

[0034] charge-voltage conversion units. The voltage signals are filtered by the filtering circuit to remove low-frequency interference and high-frequency resonance signals, and the filtered signals are processed by the amplifying circuit and the limiting circuit to obtain voltage signals that meet the input requirements of the AD conversion module. Brief Description of the Drawings

[0035] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention.

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0037] Figure 1 It is the overall system block diagram of an impact monitoring device on an aircraft in an embodiment of the present invention;

[0038] Figure 2 It is the schematic diagram of the phase difference adjusted by a sampling and positioning method for airborne impact synchronous monitoring in an embodiment of the present invention. Detailed Embodiments

[0039] In order to more clearly understand the above objects, features and advantages of the present invention, the solution of the present invention will be further described below. It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0040] In the following description, many specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways different from those described herein; obviously, the embodiments in the specification are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0041] The specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0042] In one embodiment, as Figure 1 shown, a sampling and positioning method for airborne impact synchronization monitoring is disclosed. First, an impact monitoring device for an aircraft is built. The impact monitoring device includes a measurement module, a signal conditioning module, an AD conversion module, an FPGA control module, an eMMC storage module, an RS422 communication module, and a power supply module for powering the entire impact monitoring device. The measurement module includes a plurality of piezoelectric sensors attached to the surfaces of the fuselage, wings, and aircraft engines for detecting impact guided waves. When the corresponding structural member is impacted by an impact source, an impact guided wave is generated on the surface of the structural member, and the piezoelectric sensor can convert the stress signal transmitted by the impact guided wave on the structural member into a charge signal based on the piezoelectric effect. The charge signal output by the measurement module is transmitted to the FPGA control module through the signal conditioning module and the AD conversion module in sequence. The signal conditioning module includes a signal acquisition and filtering processing circuit and a number of charge-voltage conversion units corresponding to the a number of piezoelectric sensors. The signal acquisition and filtering processing circuit includes a filtering circuit, an amplifying circuit, and a limiting circuit. An individual charge-voltage conversion unit converts it into a voltage signal. The voltage signal passes through a filtering circuit to remove low-frequency interference and high-frequency resonance signals. The filtered signal is processed by an amplification circuit and a limiting circuit to obtain a voltage signal that meets the input requirements of the AD conversion module. The AD conversion module is composed of multiple cascaded eight-channel analog-to-digital converters and its peripheral circuits. Inside, it consists of an independent 16-bit resolution SAR-type ADC and eight individually controlled T / H components. All eight T / H components are controlled by a unified conversion start signal CONVST. In this architecture, the T / H circuit samples and holds the input signal at the same moment, and then sequentially sends it to the ADC for conversion. The FPGA controller, as the core control module of the system, is responsible for managing the entire process of sampling, storing, analyzing, and transmitting. The FPGA control module controls the AD conversion module to synchronously sample multiple piezoelectric sensors of the measurement module. The sampled data is filtered and framed inside the FPGA control module and then sent to the FIFO buffer to be triggered. When the charge signal of a certain piezoelectric sensor reaches the preset trigger threshold, the FPGA control module intercepts a complete signal of 500 ms before and after the impact, including pre-trigger data, as the impact signal and stores it in the eMMC storage module to support subsequent impact position analysis and feature extraction. Subsequently, the FPGA control module uses the positioning algorithm based on TDOA and RSSI to analyze the stored impact signal and calculate the position of the impact source. Finally, the FPGA control module frames the time of impact occurrence, the impact magnitude, and the calculated impact source position information, and sends it to the on-board control system through the RS422 communication module. The steps of the positioning algorithm based on TDOA and RSSI are as follows:

[0043] S1. First, determine the monitoring area coordinate system and the corresponding coordinates of the piezoelectric sensors according to the placement positions of the piezoelectric sensors on the aircraft. Assume the positions of the piezoelectric sensors are respectively , , … ;

[0044] S2. According to the 500 ms impact signal obtained by the FPGA control module, extract the impact time and impact amplitude characteristics. Record the sampling time when the amplitude of each piezoelectric sensor first exceeds the trigger threshold as the time when the impact guided wave reaches the corresponding piezoelectric sensor. Among them, the times when the impact guided wave reaches the piezoelectric sensors are respectively , , … ; Record the maximum value of the impact guided wave received by each piezoelectric sensor as the impact amplitude. Then the impact amplitudes of the piezoelectric sensors are respectively , , … , The maximum impact amplitude among the impact amplitudes corresponding to the piezoelectric sensors is denoted as ; Assume that the propagation speed of the impact guided wave on the aircraft is , and the position of the impact source is ;

[0045] S3. The TDOA algorithm calculates the time difference of the impact guided wave arriving at different piezoelectric sensors, and then combines the position information of the piezoelectric sensors to estimate the position of the impact source. Therefore, first calculate the distance between each piezoelectric sensor as:

[0046] ;

[0047] In the formula , is the th piezoelectric sensor, is the th piezoelectric sensor;

[0048] The ideal time difference between the th piezoelectric sensor and the th piezoelectric sensor is :

[0049] ;

[0050] The actual time difference of the impact guided wave arriving at the i-th piezoelectric sensor and the j-th piezoelectric sensor is :

[0051] ;

[0052] In the formula , is the actual time for the impact guided wave to reach the th piezoelectric sensor, the actual time for the impact guided wave to reach the th piezoelectric sensor;

[0053] Then the error between the ideal time difference and the actual time difference is:

[0054] ;

[0055] For piezoelectric sensors, through the above steps, cycle to calculate the th piezoelectric sensor and the The error between piezoelectric sensors is obtained, and the total sum of squared errors is as follows:

[0056] ;

[0057] S4. Calculate the distance from the impact source to each piezoelectric sensor according to the planar distance formula:

[0058] ;

[0059] During the transmission process, the impact source attenuates due to the damping effect. According to the RSSI algorithm, the signal amplitude is inversely proportional to the square of the distance. Then the error function Based on the above relationship, it is expressed as the difference between the signal intensity predicted by the model and the actually measured signal intensity at the th piezoelectric sensor. The calculation formula of the error function is as follows:

[0060] ;

[0061] In the formula , is the impact amplitude of the th piezoelectric sensor; , is the total number of piezoelectric sensors;

[0062] For the th piezoelectric sensor, use the above steps to calculate its error function in a loop, and the total sum of squared errors obtained is as follows:

[0063] ;

[0064] S5. Weighted sum the sum of squared errors obtained by the TDOA algorithm and the RSSI algorithm in a certain proportion to obtain the total error sum:

[0065] ;

[0066] In the formula , is the weight of the sum of squared errors obtained by the RSSI algorithm, is the weight of the sum of squared errors obtained by the TDOA algorithm;

[0067] S6. Use the nonlinear least squares method to iteratively solve the total error sum to finally obtain the optimal position of the impact source.

[0068] Specifically, to reduce the sampling time error between multiple AD conversion modules, the AD conversion modules and the main control FPGA are connected in parallel and share a trigger signal, and the signal line length, routing method, and impedance are all kept as consistent as possible to ensure that the delay of the trigger signal reaching each device is the same. In addition, to solve the phase error caused by the transmission line length and the front-end filter circuit, a delay correction module is added to the FPGA, and a programmable delay line based on FIFO is used to dynamically adjust the sampling time difference between channels. The sampled data stream of each channel is written into an independent FIFO module using a synchronous clock, and then the data of each channel is read separately using a read pointer with an offset address and written into a new FIFO to complete the data offset correction process. See specifically Figure 2 for details. The offset address here is the number of samples of the delay required for each channel, which is usually determined by measuring the phase difference of signals in different channels. By setting the offset value of the read pointer for each channel in the delay correction module, it is possible to ensure that the signals of each channel are aligned on the time axis, thereby eliminating the phase error caused by hardware differences. The above-mentioned hardware design + software correction method greatly reduces the sampling error between multiple channels. At a sampling rate of 800K, the sampling time error of multiple channels is only a few nanoseconds, laying a foundation for the subsequent time-based impact location method.

[0069] Impact events usually manifest as high-amplitude high-frequency signals within a short period of time, posing a potential threat to the structure and system performance of the aircraft. Monitoring and recording impact events is crucial for evaluating the health status of the aircraft. To effectively identify abnormal impact events occurring on the fuselage and then locate the impact events. In this invention, an impact identification algorithm based on threshold discrimination and an impact location algorithm based on TDOA + RSSI are innovatively proposed.

[0070] The core idea of the impact identification algorithm based on threshold discrimination is to set a predetermined threshold. When the signal of a certain piezoelectric sensor exceeds this threshold, the system determines that an impact event has occurred. During the operation of the aircraft, the data of the multi-channel piezoelectric sensors are collected in real time and preliminarily processed, and temporarily stored in the FIFO pre-trigger memory in a cyclic storage manner. After each sampling, the system will compare the data of each channel with the threshold one by one. If the signal of a certain channel exceeds the threshold, the determination of the impact event is triggered.

[0071] After completing the analysis of the original sampled data and extracting the impact event, the next step is to estimate the location of the impact source. This process aims to calculate the specific location where the impact occurs by analyzing the propagation characteristics of the impact guided wave signal, combined with the layout of piezoelectric sensors and the aircraft structure information. For this purpose, in this invention, a positioning algorithm combining TDOA and RSSI is innovatively used. By analyzing the propagation time difference and attenuation characteristics of the impact guided wave signal between each sensor, the location of the impact source is comprehensively calculated.

[0072] The method described in the present invention combines the error functions of the TDOA algorithm and the RSSI algorithm, and assigns different weights according to the characteristics of the monitoring area (when the monitoring area is large and the area is complex, the TDOA weight is dominant; when the monitoring area is small and high-precision monitoring is required, the RSSI weight is dominant), and can achieve more accurate positioning under different environmental and distance conditions.

[0073] By adopting the above technical measures, the method described in the present invention realizes high-precision synchronous sampling of multi-channel piezoelectric sensor signals through a hardware + software correction method, and can comprehensively monitor and record the impact conditions of the aircraft from takeoff to landing. The system real-time collects the impact data of key parts of the aircraft, and frames and locally stores the collected data. During the data processing process, a threshold discrimination algorithm is used to identify impact events. In order to further locate the impact source, the TDOA algorithm and the RSSI algorithm are combined to accurately locate the position of the impact source by analyzing the signals of multiple sensors, providing support for aircraft safety monitoring and fault diagnosis.

[0074] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Although the foregoing embodiments have been described in detail, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the foregoing embodiments, and they should all be covered by the protection scope of the claims.

Claims

1. A sampling and positioning method for airborne shock synchronization monitoring, characterized in that Build an impact monitoring device for an aircraft. The impact monitoring device includes a measurement module, a signal conditioning module, an AD conversion module, an FPGA control module, an eMMC storage module, an RS422 communication module, and a power supply module for powering the entire impact monitoring device. The measurement module includes a plurality of piezoelectric sensors attached to the surface of the fuselage, wings, and aircraft engine for detecting impact guided waves. When the corresponding structural member is impacted by an impact source, impact guided waves are generated on the surface of the structural member, and the piezoelectric sensors can convert the stress signal transmitted by the impact guided waves on the structural member into a charge signal based on the piezoelectric effect. The charge signal output by the measurement module is sequentially transmitted to the FPGA control module through the signal conditioning module and the AD conversion module. The FPGA control module controls the AD conversion module to synchronously sample the plurality of piezoelectric sensors of the measurement module. The sampled data is filtered and framed inside the FPGA control module and then sent to the FIFO buffer to be triggered. When the charge signal of a certain piezoelectric sensor reaches the preset trigger threshold, the FPGA control module intercepts the complete signal for 500 ms before and after the impact as the impact signal and stores it in the eMMC storage module to provide support for subsequent impact position analysis and feature extraction. Subsequently, the FPGA control module uses the positioning algorithm based on TDOA and RSSI to analyze the stored impact signal and calculate the position of the impact source. Finally, the FPGA control module frames the impact occurrence time, impact magnitude, and the calculated impact source position information, and sends them to the on-board control system through the RS422 communication module. The steps of the positioning algorithm based on TDOA and RSSI are as follows: S1. First, determine the monitoring area coordinate system and the corresponding coordinates of the impact guided wave sensors according to the placement positions of the impact guided wave sensors on the aircraft. Assume that the positions of the impact guided wave sensors are respectively , , … ; S2. According to the 500 ms impact signal obtained by the FPGA control module, extract the impact time and impact amplitude characteristics. Record the sampling time when the amplitude of each piezoelectric sensor first exceeds the trigger threshold as the time when the impact guided wave arrives at the corresponding piezoelectric sensor. Among them, the time when the impact guided wave arrives at each piezoelectric sensor is , , … ; Record the maximum value of the impact guided wave received by each piezoelectric sensor as the impact amplitude. Then, the impact amplitudes of each piezoelectric sensor are , , … , and record the maximum impact amplitude among the impact amplitudes corresponding to each piezoelectric sensor as ; Assume that the propagation speed of the impact guided wave on the aircraft is , and the position of the impact source is . S3. The TDOA algorithm calculates the time difference of the impact guided wave arriving at different piezoelectric sensors, and then combines the position information of the piezoelectric sensors to estimate the position of the impact source. Therefore, the distance between each piezoelectric sensor is calculated first. It is: ； Formula wherein is the th piezoelectric sensor, and is the th piezoelectric sensor; The ideal time difference between the first piezoelectric sensor and the second piezoelectric sensor is ； The time difference between the actual impact guided wave reaching the i-th piezoelectric sensor and the j-th piezoelectric sensor is :[[-END]] ； Formula In is the actual time when the impact guided wave reaches the th piezoelectric sensor, and is the actual time when the impact guided wave reaches the th piezoelectric sensor; Then the error between the ideal time difference and the actual time difference is: ； For piezoelectric sensors, the error between the th piezoelectric sensor and the th piezoelectric sensor is calculated in a loop through the above steps, and the total sum of squared errors is obtained as: ； S4. According to the plane distance formula, calculate the distances from the impact source to each piezoelectric sensor: ； The impact source attenuates during transmission due to the damping effect. According to the RSSI algorithm, the signal amplitude is inversely proportional to the square of the distance, so the error function Based on the above relationship, it is expressed as the difference between the signal intensity predicted by the model at the th piezoelectric sensor and the actually measured signal intensity. The calculation formula of the error function is as follows: ； Formula In is the impact amplitude of the th piezoelectric sensor; , is the total number of piezoelectric sensors; For piezoelectric sensors, the error function is calculated cyclically using the above steps , and the total sum of squared errors is obtained as follows: ； S5. Weighted sum the sum of the squared errors obtained by the TDOA algorithm and the RSSI algorithm in a certain proportion to obtain the total error sum: ； Formula In is the weight of the sum of squared errors obtained by the RSSI algorithm, is the weight of the sum of squared errors obtained by the TDOA algorithm; S6. Use the non - linear least - squares method to perform iterative solution on the total error and and finally obtain the optimal position of the impact source .

2. The sampling and positioning method for airborne impact synchronization monitoring according to claim 1, wherein The signal conditioning module includes a signal acquisition and filtering processing circuit and corresponding to charge-voltage conversion units for piezoelectric sensors. The signal acquisition and filtering processing circuit includes a filtering circuit, an amplifying circuit, and a limiting circuit. The charge signals of piezoelectric sensors are converted into voltage signals through the corresponding charge-voltage conversion units. The voltage signals are passed through the filtering circuit to remove low-frequency interference and high-frequency resonance signals. The filtered signals are processed by the amplifying circuit and the limiting circuit to obtain voltage signals that meet the input requirements of the AD conversion module.

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