Method for estimating speed of high-speed object in closed cavity based on magnetic characteristic cross-correlation time difference

Abstract

The invention belongs to the technical field of target motion parameter detection. The invention provides a method for estimating the speed of a high-speed object in a closed cavity based on magnetic characteristic cross-correlation time difference. According to the embodiment of the invention, the two fluxgate sensors are arranged on the motion path of the measured target, the magnetic field disturbance signals acquired by the two fluxgate sensors are utilized, the propagation time delay of the disturbance signals is analyzed and extracted in combination with the amplitude weighted normalized cross-correlation function, and the target speed is calculated, so that high-precision and high-robustness speed estimation is realized. According to the method, the weighted cross-correlation can still stably extract the time delay information under the condition that the signal is interfered by noise and the background magnetic field drifts, and the problem of misrecognition in feature point recognition is avoided. The method does not need complex modeling, has good universality, real-time performance and anti-interference capability, and is particularly suitable for a complex magnetic environment or an actual scene with violent motion state change.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the technical field of target motion parameter detection, and in particular to a high-speed object velocity estimation method in a closed cavity based on magnetic feature cross-correlation time difference. BACKGROUND

[0002] In application scenarios such as industrial automation, intelligent transportation, and pipeline detection, high-precision and non-contact velocity measurement of moving targets is often required. Currently, common velocity measurement methods mainly include laser velocity measurement, infrared velocity measurement, Doppler radar, and electromagnetic induction velocity measurement. These methods often have high requirements for target shape, material, or operating environment, and have disadvantages such as high cost, high environmental interference, or difficulty in embedded deployment.

[0003] As a kind of weak magnetic detection device with high sensitivity, fast response and simple structure, the fluxgate sensor has been widely used in non-contact metal detection and trajectory tracking. Based on the magnetic disturbance response signal of the fluxgate sensor, in recent years, various velocity measurement methods have been developed, such as non-contact velocity measurement technology based on waveform mutation detection and peak matching. This kind of method usually relies on the magnetic field disturbance characteristics caused by target motion, and determines the target passing time through feature point recognition or signal analysis, and then estimates the velocity.

[0004] However, in actual application, the form of magnetic disturbance signal is often affected by factors such as target material, structure shape, posture, and velocity change, showing strong uncertainty and non-stability, which makes the feature point detection-based method vulnerable to misidentification interference. In addition, the extraction process of mutation points or peak points has high dependence on signal quality, and is easily affected by background magnetic field drift, noise disturbance and other factors, thereby reducing the accuracy and robustness of velocity estimation. At the same time, the existing cross-correlation algorithm often has difficulty in effectively dealing with signal noise and background interference when processing complex signals, which limits its performance in actual application.

[0005] Therefore, it is necessary to improve one or more problems existing in the above-mentioned related technical solutions.

[0006] It should be noted that this part aims to provide background or context for the technical solutions of the present disclosure stated in the claims. The description herein is not admitted to be prior art merely because it is included in this part. SUMMARY

[0007] The purpose of the embodiments of the present disclosure is to provide a high-speed object velocity estimation method in a closed cavity based on magnetic feature cross-correlation time difference, thereby at least overcoming one or more problems caused by the limitations and defects of related technologies.

[0008] According to a first aspect of the embodiments of the present disclosure, a method for estimating the speed of a high-speed object in a closed cavity based on the cross-correlation time difference of magnetic characteristics is provided, comprising: A first magnetic field strength signal is collected by using a first fluxgate sensor, and a second magnetic field strength signal is collected by using a second fluxgate sensor; wherein the distance between the first fluxgate sensor and the second fluxgate sensor is a preset value; The first magnetic field strength signal and the second magnetic field strength signal are respectively preprocessed to obtain a first disturbance signal and a second disturbance signal; The first disturbance signal and the second disturbance signal are time-series compared by using a normalized cross-correlation function, and a delay point corresponding to a delay point that makes the normalized cross-correlation function maximum is obtained by traversing the delay amount; The time delay between the first magnetic field strength signal and the second magnetic field strength signal is calculated according to the delay point corresponding to the delay point that makes the normalized cross-correlation function maximum and the signal sampling time interval; Based on the distance between the first fluxgate sensor and the second fluxgate sensor, the time delay between the first magnetic field strength signal and the second magnetic field strength signal, the instantaneous speed of the target is calculated.

[0009] Further, in the step of preprocessing the first magnetic field strength signal and the second magnetic field strength signal, comprising: The first magnetic field strength signal and the second magnetic field strength signal are subjected to Gaussian weighted moving average filtering; The first magnetic field strength signal and the second magnetic field strength signal are subjected to low-pass Butterworth filtering; The background trend drift of the first magnetic field strength signal and the second magnetic field strength signal is removed, and zero-mean standardization processing is performed; The first magnetic field strength signal and the second magnetic field strength signal are subjected to amplitude weighting to enhance the characteristics of the mutation part in the signal.

[0010] Further, in the step of time-series comparing the first disturbance signal and the second disturbance signal by using the normalized cross-correlation function, traversing the delay amount, and obtaining the delay point corresponding to the delay point that makes the normalized cross-correlation function maximum, comprising: The first disturbance signal and the second disturbance signal are subjected to amplitude-weighted normalized cross-correlation function analysis to improve the accuracy of cross-correlation analysis under low signal-to-noise ratio conditions; The delay point corresponding to the delay point that makes the normalized cross-correlation function maximum is obtained by traversing the delay amount.

[0011] Further, the expression of the normalized cross-correlation function is:

[0012] wherein, is the first disturbance signal, is a sample time delay, is a second disturbance signal at the sample time delay is a value at the sample time delay, is a mean value of the first disturbance signal, is a mean value of the second disturbance signal, and N is a signal length.

[0013] Further, the time delay between the first magnetic field strength signal and the second magnetic field strength signal is:

[0014] wherein, is a delay point corresponding to a delay point at which the normalized cross-correlation function is maximum, is a sampling time interval.

[0015] Further, an expression of the instantaneous velocity of the target is:

[0016] wherein, is a distance between the first fluxgate sensor and the second fluxgate sensor.

[0017] According to a second aspect of the embodiments of the present disclosure, a computer readable storage medium is provided, and a computer program is stored on the computer readable storage medium. The program is executed by a processor to implement the steps of the method for estimating the velocity of a high-speed object in a closed cavity based on the cross-correlation time difference of magnetic characteristics according to any one of the preceding embodiments.

[0018] According to a third aspect of the embodiments of the present disclosure, an electronic device is provided, and the electronic device comprises: a processor; and a memory configured to store executable instructions of the processor; wherein the processor is configured to implement the steps of the method for estimating the velocity of a high-speed object in a closed cavity based on the cross-correlation time difference of magnetic characteristics according to any one of the preceding embodiments by executing the executable instructions.

[0019] The technical solutions provided by the embodiments of the present disclosure can include the following beneficial effects: In the embodiments of the present disclosure, by the above method, on the one hand, by arranging two fluxgate sensors on the motion path of the measured target, the magnetic field disturbance signals collected by the two fluxgate sensors are used to extract the propagation time delay of the disturbance signals and calculate the target speed by combining the amplitude weighted normalized cross-correlation function analysis, so that high-precision and high-robustness speed estimation is realized. On the other hand, the weighted cross-correlation of the method can still extract the time delay information stably in the case that the signal is disturbed by noise and the background magnetic field drifts, and the misidentification problem in feature point identification is avoided. The method does not need complex modeling, and has good universality, real-time performance and anti-interference ability, and is particularly suitable for complex magnetic environment or actual scenes with severe motion state changes. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure. It is apparent that the accompanying drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0021] Figure 1 A step diagram of a high-speed object speed estimation method in a closed cavity based on magnetic feature cross-correlation time difference in an exemplary embodiment of the present disclosure is shown; Figure 2 A specific flowchart of a high-speed object speed estimation method in a closed cavity based on magnetic feature cross-correlation time difference in an exemplary embodiment of the present disclosure is shown; Figure 3 A layout schematic diagram of a first fluxgate sensor and a second fluxgate sensor in an exemplary embodiment of the present disclosure is shown; Figure 4 A waveform diagram of a first magnetic field disturbance signal and a second magnetic field disturbance signal in an exemplary embodiment of the present disclosure is shown; Figure 5 A normalized cross-correlation function curve diagram in an exemplary embodiment of the present disclosure is shown; Figure 6 A structure schematic diagram of a high-speed object speed estimation system in a closed cavity in an exemplary embodiment of the present disclosure is shown; Figure 7 A structure schematic diagram of a speed estimation system of an underwater vehicle in an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0022] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations can be implemented in any

[0023] In addition, the drawings are to be considered in all respects as illustrative and not restrictive; the examples described herein are susceptible to modification in the aspects not specifically described; and it should be understood that the examples described herein are not intended to limit the scope of the disclosure to the particular examples presented.

[0024] A method for estimating the speed of a high-speed object in a closed cavity based on magnetic feature cross-correlation time difference is provided in the present example implementation. Referring to FIG. 1, the method for estimating the speed of a high-speed object in a closed cavity based on magnetic feature cross-correlation time difference can include: Figure 1 Step S101: collecting a first magnetic field intensity signal by using a first fluxgate sensor and collecting a second magnetic field intensity signal by using a second fluxgate sensor; wherein the distance between the first fluxgate sensor and the second fluxgate sensor is a preset value; Step S102: pre-processing the first magnetic field intensity signal and the second magnetic field intensity signal respectively to obtain a first disturbance signal and a second disturbance signal; Step S103: using a normalized cross-correlation function to perform time sequence comparison on the first disturbance signal and the second disturbance signal, traversing the delay amount, and obtaining the delay point corresponding to the delay point at which the normalized cross-correlation function is maximum; Step S104: calculating the time delay between the first magnetic field intensity signal and the second magnetic field intensity signal according to the delay point corresponding to the delay point at which the normalized cross-correlation function is maximum and the signal sampling time interval; Step S105: calculating the instantaneous speed of the target based on the distance between the first fluxgate sensor and the second fluxgate sensor, the time delay between the first magnetic field intensity signal and the second magnetic field intensity signal.

[0025] ​By the above-mentioned high-speed object velocity estimation method in a closed cavity based on magnetic feature cross-correlation time difference, on the one hand, by arranging two fluxgate sensors on the motion path of the measured target, the magnetic field disturbance signals collected by the two fluxgate sensors are used to extract the propagation time delay of the disturbance signals and calculate the target velocity, thereby realizing high-precision and high-robustness velocity estimation. On the other hand, the weighted cross-correlation of the method can still extract the time delay information stably in the case that the signals are disturbed by noise and the background magnetic field drifts, thereby avoiding the misidentification problem in feature point identification. The method does not need complex modeling and has good universality, real-time performance and anti-interference ability, and is particularly suitable for complex magnetic environments or actual scenes with dramatic changes in motion state.

[0026] In the following, reference will be made to Figures 1 to 7 The above-mentioned steps of the high-speed object velocity estimation method in a closed cavity based on magnetic feature cross-correlation time difference in the present example embodiment will be described in more detail.

[0027] In step S101, a first magnetic field intensity signal is collected by a first fluxgate sensor, and a second magnetic field intensity signal is collected by a second fluxgate sensor; wherein the distance between the first fluxgate sensor and the second fluxgate sensor is a preset value.

[0028] Specifically, a first fluxgate sensor and a second fluxgate sensor are arranged on the motion path of the measured target, and the distance between the two is a known quantity d, which is used to collect the magnetic field disturbance signals caused by the target passing through. The first magnetic field intensity signal collected by the first fluxgate sensor is , and the second magnetic field intensity signal collected by the second fluxgate sensor is .

[0029] In step S102, the first magnetic field intensity signal and the second magnetic field intensity signal are preprocessed respectively to obtain a first disturbance signal and a second disturbance signal.

[0030] Specifically, a Gaussian weighted moving average filtering algorithm is used to perform preliminary smoothing processing on the signals to weaken high-frequency noise and local fluctuations; Further, a low-pass Butterworth filter is used for processing to suppress the remaining high-frequency interference; The background trend drift is removed, and the two signals are standardized to reflect the disturbance changes on the basis of zero mean, which is convenient for subsequent correlation analysis; The amplitude is weighted, the signals are weighted according to the amplitude-frequency characteristics of the signals, the amplitude of the signal mutation part is strengthened, and the influence of the stable part and noise is weakened. Through amplitude weighting, the spectral characteristics in the signal are amplified, the obvious features of the disturbance signals are enhanced, and the time delay estimation accuracy under low signal-to-noise ratio conditions is improved.

[0031] In step S103 and step S104, the first disturbance signal and the second disturbance signal are time series compared by using the normalized cross-correlation function, the delay amount is traversed, and the delay point corresponding to the delay point making the normalized cross-correlation function maximum is obtained. According to the delay point corresponding to the delay point making the normalized cross-correlation function maximum and the signal sampling time interval, the time delay between the first magnetic field strength signal and the second magnetic field strength signal is calculated.

[0032] Specifically, the amplitudes of the two preprocessed disturbance signals 、 are weighted, the mutation section in the signal is strengthened, the influence of other unimportant parts is suppressed, and thus the accuracy of the cross-correlation analysis under the condition of low signal-to-noise ratio is improved. The normalized cross-correlation function is defined as:

[0033] Among them, is the sample delay, and N is the signal length; The delay k corresponding to the maximum value of the amplitude cross-correlation function is obtained by traversing the delay amount k. max And the time delay is calculated:

[0034] Among them, is the sampling time interval; In step S105, according to the known sensor spacing d and the time delay , the target instantaneous speed is estimated:

[0035] In this method, two fluxgate sensors are arranged on the motion path of the measured target, the magnetic field disturbance signals collected by the two fluxgate sensors are used, the amplitude weighted normalized cross-correlation function analysis is combined, the disturbance signal propagation time delay is extracted, and the target speed is calculated, so that a non-contact and simple calculation dynamic target speed measurement method is realized. The method can be widely applied to metal pipeline detection, traffic monitoring, industrial logistics and other scenes, and has the advantages of simple structure, stable data processing, no need of additional speed sensor and the like. Through the cross-correlation function time delay analysis of the two fluxgate signals, the disturbance propagation time can be automatically estimated, and then the speed is inversely calculated combined with the known spatial distance, so that complex signal modeling or marker dependence is avoided.

[0036] The method is especially suitable for scenes where it is inconvenient to install mechanical speed measurement equipment or non-destructive testing is required, and has the following advantages: 1. No need to contact the target body: the method is completely based on the processing of the fluxgate signal, and does not need to contact the measured target or add sensors on the surface of the target, so it is suitable for speed measurement tasks in high-speed, closed or inaccessible scenes; 2. Low dependence on input parameters: The method does not rely on complex magnetic field models or material information of the measured object, but only on the spacing of the fluxgate arrangement and the characteristics of the signal itself, reducing the sensitivity to physical parameters such as permeability and shape, ensuring the accuracy and robustness of the speed measurement; 3. Simple and stable data processing: The method uses the extreme value search of the weighted normalized cross-correlation function as the time delay estimation method, avoiding the dependence on complex features such as abrupt points, peaks, and waveform models, with high automation in the calculation process and strong anti-interference ability; 4. No need for multiple measurements: The speed value can be obtained for each passing target through the fluxgate, avoiding the complex operations of trajectory fitting or repeated measurements in traditional speed measurement, with high real-time performance; 5. Suitable for long-distance and complex environments: Even in the case of large sensor spacing or background magnetic field disturbance, the weighted cross-correlation function can still extract effective time delay information, with good environmental adaptability and practicality; In summary, the application constructs a closed cavity target magnetic signal speed estimation method based on weighted cross-correlation, combining filter preprocessing and high correlation point time delay detection, realizing a low-cost, stable and reliable non-contact speed measurement method, providing a general and effective solution for non-contact dynamic parameter extraction in multiple scenarios.

[0037] In one specific embodiment, as shown in Figure 2 , the application arranges two groups of fluxgate sensors to collect the magnetic flux disturbance signals caused by the target passing through, extracts the time delay between the two signals through normalized cross-correlation analysis, and then estimates the non-contact speed of the target by combining the fixed distance between the sensors. This method has good robustness and signal processing stability, and is particularly suitable for complex environments with background interference or magnetic drift.

[0038] As shown in Figure 3 , in actual application scenarios, the measured target moves at a constant speed along a fixed channel, and a first fluxgate sensor and a second fluxgate sensor are arranged on its path, with a distance d between them in meters. Let represent the fluxgate sampling frequency in Hz, and the signal sampling time interval is s .

[0039] Let the magnetic field intensity discrete signals collected by the two sensors be the first magnetic field intensity signal and the second magnetic field intensity signal , where i∈{1,2,…,N} and N is the signal length.

[0040] Signal preprocessing Firstly, the original magnetic field signal collected is double-filtered. To ensure the sensitivity and accuracy of subsequent mutation detection, the signal is first smoothed using Gaussian weighted moving average filtering. The window length is set to The weighted coefficient is generated according to the standard Gaussian kernel:

[0041] wherein, is the weighted average signal value; is the sum of the half window width; is the weight function, is the value of the original signal at index ; The weight function is:

[0042] wherein, is the standard deviation parameter controlling the width of the weight distribution.

[0043] A symmetric smoothing weighted kernel is thus constructed to improve the smoothness of the signal. Subsequently, a Butterworth low-pass filter is applied to further suppress high-frequency interference, with a cutoff frequency set to and a filter order n to obtain a smoother disturbance curve while preserving the mutation characteristics in the signal. The filter design uses a normalized frequency .

[0044] To eliminate the background geomagnetic drift trend, a sliding window detrending process is used to keep the disturbance signal stable and zero-mean distributed in the entire time domain, improving the accuracy of mutation detection.

[0045] The power spectrum of the signal is calculated, and the amplitude weighting factor is defined:

[0046] wherein, and are the Fourier transforms of the disturbance signals and representing the spectral characteristics of the signal, is the amplitude weighting function.

[0047] The signal amplitude is weighted to expand the amplitude of the signal at the main frequency components (i.e., the signal characteristic part) and compress the amplitude of other unimportant frequencies, improving the contribution of the target disturbance part in cross-correlation analysis and suppressing noise and small peaks, optimizing the cross-correlation power spectrum and improving the accuracy of time delay estimation.

[0048] As shown in Figure 4 , it is the waveform diagram of the first magnetic field disturbance signal and the second magnetic field disturbance signal.

[0049] Normalized cross-correlation calculation and time delay extraction The present application uses normalized cross-correlation function to compare the time sequence of two signals, and the goal is to find the time delay of the maximum similar time between two groups of signals, and to realize non-contact time delay extraction.

[0050] The normalized cross-correlation function is defined as:

[0051] Wherein, is the first disturbance signal, is the second disturbance signal, is the second disturbance signal The value at the sample time delay , is the mean value of the first disturbance signal, is the mean value of the second disturbance signal, and N is the signal length, is the sample time delay, and this normalized form ensures that the cross-correlation result is not affected by the amplitude and bias of the two signals.

[0052] The sliding delay k takes values within a certain window, that is, the second signal is slid within a reasonable time window, and the correlation calculation is performed with respect to the first signal. Within all k ranges, find the delay point that makes the maximum, and find the time delay corresponding to this value.

[0053] As shown in Figure 5 , it is a normalized cross-correlation function curve. Wherein, the time delay: =0.402477 seconds.

[0054] Speed estimation Given that the distance between the two fluxgate sensors is d=30cm, and the time delay experienced by the target between the two maximum slope points is:

[0055] Thus, the non-contact speed estimation is completed.

[0056] ​The application combines the magnetic flux gate sensor technology and the amplitude-weighted cross-correlation sliding time delay detection method, uses a double magnetic flux gate sensor structure, synchronously collects and processes the magnetic disturbance signals caused by the target motion, extracts the time delay of the maximum correlation point in the sliding window based on the amplitude-weighted cross-correlation function, and realizes high-precision estimation of the target passing speed. The application estimates the speed of the high-speed target in a non-contact manner by calculating the strongest correlation position time delay between the magnetic disturbance signals detected by the double sensors and combining the known distance between the two sensors. The method is suitable for passive motion monitoring in various scenes such as high-speed object speed estimation in a closed cavity, underwater vehicle speed estimation, and rail transportation.

[0057] In one specific embodiment, the speed of a high-speed object in a closed cavity is measured: In a high-speed object speed measurement system in a closed cavity, the motion speed of a target object (such as a high-speed object) is measured by the magnetic signal passing characteristics and correlation of two magnetic flux gate sensors. Figure 6 The basic structure of the system is shown, in which the first magnetic flux gate and the second magnetic flux gate are located on the motion path of the target object, and the distance between them is a known value d. When the target object passes through the first magnetic flux gate, the magnetic field is disturbed, and the magnetic flux gate sensor collects the signal change; similarly, when the target object passes through the second magnetic flux gate, a disturbance signal is also generated.

[0058] By calculating the cross-correlation function between the signals collected by the two magnetic flux gate sensors, the time delay between the signals can be accurately measured. The time delay of the maximum cross-correlation point reflects the propagation time of the target object from the first magnetic flux gate to the second magnetic flux gate. According to the known distance d between the two magnetic flux gates and the time delay , the speed is estimated. This method realizes high-precision target object speed measurement through the time delay characteristics of the signal, and has the advantages of strong real-time performance, non-contact, and strong anti-interference ability.

[0059] In one specific embodiment, the speed estimation and detection of an underwater vehicle: Figure 7 The structure of the underwater vehicle speed measurement system is shown, in which two magnetic flux gate sensors are arranged at different positions on the motion path of the underwater vehicle to estimate the speed of the vehicle through the magnetic field disturbance signal. The first magnetic flux gate and the second magnetic flux gate collect the magnetic field changes when the target object (such as the underwater vehicle) passes through.

[0060] In the system, the motion speed of the target is estimated by calculating the time delay between the two magnetic flux gates. The specific process is that when the underwater vehicle passes through the first magnetic flux gate, the magnetic field changes, and the magnetic flux gate records this change; similarly, when the underwater vehicle passes through the second magnetic flux gate, the magnetic field changes again. The system calculates the time difference (i.e. time delay) between the two magnetic field disturbances , combined with the known distance d between the two fluxgates, the velocity is estimated, which is suitable for the velocity monitoring of underwater vehicles, and has the advantages of high precision, non-contact, and strong real-time performance.

[0061] The application provides a time delay extraction method based on improved signal preprocessing and normalized cross-correlation, which effectively suppresses background drift and transient noise by combining Gaussian weighted sliding filtering and Butterworth low-pass filtering, and improves the clarity of the time domain characteristics of the magnetic disturbance signal, thereby providing high-quality input for subsequent cross-correlation calculation. Compared with traditional methods based on feature point matching or fixed template delay estimation, the application has stronger stability and time delay analysis capability in a noisy environment.

[0062] The application dynamically extracts the maximum correlation delay point of the two magnetic signals by performing weighted normalized cross-correlation analysis in a certain sliding window, automatically determines the transmission time of the target magnetic disturbance between the two sensors, avoids manual intervention or experience threshold setting, and improves the precision and robustness of time delay detection, and is especially suitable for complex targets with variable speed motion or obvious disturbance form changes.

[0063] The application estimates the velocity based on the amplitude-weighted cross-correlation time delay, has a simple structure and clear principle, does not need to introduce a target magnetic characteristic model, and does not depend on the electronic marking or structural cooperation of the target object, and can realize passive, low-power and high-efficiency dynamic speed measurement. The application adopts a standard correlation function sliding window matching strategy, has small calculation overhead, is suitable for real-time deployment and online monitoring of embedded systems, and is especially suitable for actual scenes with limited space or strong environmental electromagnetic interference.

[0064] In the example embodiments of the present disclosure, a computer readable storage medium having a computer program stored thereon is also provided, and the program can implement the steps of the above-mentioned magnetic feature cross-correlation time difference based speed estimation method of a high-speed object in a closed cavity when executed by a processor. In some possible implementation manners, various aspects of the application can also be implemented in the form of a program product, which includes program code for causing a terminal device to perform the steps described in the above-mentioned magnetic feature cross-correlation time difference based speed estimation method of a high-speed object in a closed cavity according to various example embodiments of the application when the program product is executed on the terminal device.

[0065] In the example embodiments of the present disclosure, an electronic device is also provided, which can include a processor and a memory for storing executable instructions of the processor. The processor is configured to execute the steps of the above-mentioned magnetic feature cross-correlation time difference based speed estimation method of a high-speed object in a closed cavity via execution of the executable instructions.

[0066] Those skilled in the art can understand that the various aspects of the present application can be implemented as a system, a method or a program product. Therefore, the various aspects of the present application can be embodied in a form of entirely hardware, entirely software (including firmware, microcode, etc.), or a combination of hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "system".

[0067] It should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like indicating the orientation or positional relationship in the above description mean the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the embodiments of the present disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the embodiments of the present disclosure.

[0068] In addition, the terms "first", "second" are only for descriptive purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, the meaning of "plurality" is two or more, unless otherwise explicitly specified and limited.

[0069] In the embodiments of the present disclosure, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connecting", "fixing" and the like should be understood in a broad sense, for example, can be fixed connection, can also be detachable connection, or integral; can be mechanical connection, can also be electrical connection; can be direct connection, can also be indirect connection through an intermediate medium, can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the present disclosure can be understood according to the specific circumstances.

[0070] In the embodiments of the present disclosure, unless otherwise explicitly specified and limited, "on" or "under" of the first feature to the second feature can include that the first and second features are in direct contact, or that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, "on", "above" and "on" of the first feature to the second feature include that the first feature is directly above and obliquely above the second feature, or only indicates that the horizontal height of the first feature is higher than that of the second feature. "Below", "under" and "under" of the first feature to the second feature include that the first feature is directly below and obliquely below the second feature, or only indicates that the horizontal height of the first feature is less than that of the second feature.

[0071] In the description of the disclosure, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the disclosure. In the description of the disclosure, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in the specification.

[0072] Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses or adaptive changes of the disclosure that follow the general principles of the disclosure and include known or customary technical means in the art not disclosed in the disclosure. The specification and examples are only considered to be exemplary, and the true scope and spirit of the disclosure are indicated by the appended claims.

Claims

1. A method for estimating the velocity of a high-speed object within a closed cavity based on the cross-correlation time difference of magnetic features, characterized in that, include: A first magnetic field strength signal is acquired using a first fluxgate sensor, and a second magnetic field strength signal is acquired using a second fluxgate sensor; wherein the distance between the first fluxgate sensor and the second fluxgate sensor is a preset value; The first magnetic field strength signal and the second magnetic field strength signal are preprocessed respectively to obtain the first disturbance signal and the second disturbance signal; The time sequence of the first and second disturbance signals is compared using the normalized cross-correlation function. The delay is then iterated through to find the delay point that maximizes the normalized cross-correlation function. The time delay between the first magnetic field strength signal and the second magnetic field strength signal is calculated based on the delay point corresponding to the maximum delay point of the normalized cross-correlation function and the signal sampling time interval. The instantaneous velocity of the target is calculated based on the distance between the first fluxgate sensor and the second fluxgate sensor, and the time delay between the first magnetic field strength signal and the second magnetic field strength signal.

2. The velocity estimation method for high-speed objects in a closed cavity based on the cross-correlation time difference of magnetic features according to claim 1, characterized in that, The preprocessing steps for the first magnetic field strength signal and the second magnetic field strength signal include: Gaussian weighted moving average filtering is applied to the first and second magnetic field strength signals. Low-pass Butterworth filtering is applied to the first and second magnetic field strength signals. Background trend drift of the first and second magnetic field strength signals is removed, and zero-mean standardization is performed. The first and second magnetic field strength signals are amplitude-weighted to enhance the characteristics of abrupt changes in the signals.

3. The velocity estimation method for high-speed objects in a closed cavity based on the cross-correlation time difference of magnetic features according to claim 2, characterized in that, The step of comparing the time sequence of the first and second disturbance signals using the normalized cross-correlation function, traversing the delay amounts, and obtaining the delay point corresponding to the delay point that maximizes the normalized cross-correlation function includes: Amplitude-weighted normalized cross-correlation function analysis was performed on the first and second perturbation signals to improve the accuracy of cross-correlation analysis under low signal-to-noise ratio conditions; Iterate through the delay values ​​and find the delay point that maximizes the normalized cross-correlation function.

4. The velocity estimation method for high-speed objects in a closed cavity based on the cross-correlation time difference of magnetic features according to claim 3, characterized in that, The expression for the normalized cross-correlation function is: in, This is the first disturbance signal. For the time delay of the sampling points, The second disturbance signal Sampling time delay The value at that location, The mean of the first disturbance signal. Let N be the mean of the second disturbance signal, and N be the signal length.

5. The velocity estimation method for high-speed objects in a closed cavity based on the cross-correlation time difference of magnetic features according to claim 4, characterized in that, The time delay between the first magnetic field strength signal and the second magnetic field strength signal is: in, The delay point corresponding to the delay point that maximizes the normalized cross-correlation function. This represents the sampling time interval.

6. The velocity estimation method for high-speed objects in a closed cavity based on the cross-correlation time difference of magnetic features according to claim 5, characterized in that, The expression for the instantaneous velocity of the target is: in, The distance between the first fluxgate sensor and the second fluxgate sensor is denoted as .

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method for estimating the velocity of a high-speed object in a closed cavity based on the cross-correlation time difference of magnetic features as described in any one of claims 1 to 6.

8. 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 perform the steps of the method for estimating the velocity of a high-speed object in a closed cavity based on the cross-correlation time difference of magnetic features as described in any one of claims 1 to 6 by executing the executable instructions.

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