Method for estimating speed of high-speed object in closed cavity based on magnetic signal feature point time difference

Abstract

The invention belongs to the technical field of electronic measurement and detection. The invention provides a method for estimating the speed of a high-speed object in a closed cavity based on magnetic signal feature point time difference. According to the embodiment of the invention, two groups of fluxgate magnetic sensors are arranged to obtain a disturbance signal generated in a local magnetic field when a target passes through; according to the method, speed measurement estimation of a target is carried out by extracting feature points in a signal, slope difference feature quantity is taken as an example, slope difference analysis is carried out on the signal, a mutation section is identified, a maximum slope point is extracted in the mutation section, and the speed measurement of the target is calculated by combining a known space distance between sensors and a time difference occurring at the maximum slope point. And the instantaneous speed of the target is accurately estimated. The whole process does not need to be in contact with a moving target, does not depend on attached marks or active signal emission, and has the remarkable advantages of low cost, high real-time performance and high robustness.

Description

TECHNICAL FIELD

[0001] The embodiment of the present disclosure relates to the technical field of electronic measurement and detection, in particular to a high-speed object velocity estimation method in a closed cavity based on a magnetic signal feature point time difference. BACKGROUND

[0002] Velocity measurement technology has important application value in the fields of industrial automation, transportation, and life monitoring. Target motion velocity is a key parameter for describing its dynamic behavior, and it is of fundamental significance for state judgment, intelligent decision-making, and control. Existing velocity measurement methods mainly include contact type and non-contact type.

[0003] Contact type velocity measurement methods such as tachogenerator and speed motor, although high precision, but rely on the physical connection of the target object, not suitable for high temperature, high speed, strong electromagnetic interference or biological tissue and other environments that are not suitable for contact, and have problems such as short service life, difficult maintenance, poor adaptability. Non-contact velocity measurement technology acquires target motion information through laser, Doppler radar, image recognition and other means, and has a certain flexibility, but is significantly affected by environmental light, shielding, reflectivity, background noise and other factors in complex scenes, and the measurement accuracy and stability are difficult to guarantee.

[0004] With the wide application of high-sensitivity magnetic sensors such as fluxgate, non-contact velocity measurement technology based on magnetic signal passing characteristics has attracted more and more attention. This kind of method realizes the perception of motion state by collecting the signal change caused by the target disturbance magnetic field, and has the characteristics of anti-shielding, non-radiation, strong concealment, etc., especially suitable for passive detection in complex environments such as closed space and underground structure. However, the current methods based on magnetic signal passing characteristics mostly only realize the judgment of target existence or behavior trend analysis, and it is difficult to directly quantify the target motion velocity, especially in the scenes of weak signal, multi-channel interference, high frequency change, etc. Traditional algorithms have deficiencies in real-time and robustness.

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

[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 embodiment of the present disclosure is to provide a high-speed object velocity estimation method in a closed cavity based on a magnetic signal feature point time difference, thereby at least overcoming one or more problems caused by the limitations and defects of the related art.

[0008] According to the embodiment of the present disclosure, a method for estimating the speed of a high-speed object in a closed cavity based on the time difference of magnetic signal feature points is provided, comprising: A first magnetic field intensity signal is collected by using a first fluxgate sensor, and a second magnetic field intensity 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 intensity signal and the second magnetic field intensity 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 subjected to mutation point detection to obtain a first mutation segment and a second mutation segment; Curve feature points are identified in the first mutation segment and the second mutation segment to obtain a first time point and a second time point; According to the first time point and the second time point, the time difference of the two curve feature points is calculated, and the instantaneous speed of the target is calculated in combination with the distance between the first fluxgate sensor and the second fluxgate sensor.

[0009] Further, the preprocessing step comprises: The first magnetic field intensity signal and the second magnetic field intensity signal are subjected to preliminary smoothing processing by using a Gaussian weighted moving average filtering algorithm to weaken high-frequency noise and local fluctuations; The first magnetic field intensity signal and the second magnetic field intensity signal are processed by using a low-pass Butterworth filter to suppress residual high-frequency interference; The first magnetic field intensity signal and the second magnetic field intensity signal are subjected to detrending processing to eliminate background geomagnetic drift and baseline offset, so that the disturbance signal stably changes with zero mean value.

[0010] Further, in the step of detecting the mutation points of the first disturbance signal and the second disturbance signal to obtain the first mutation segment and the second mutation segment, the step comprises: For the first disturbance signal, a first-order forward difference method is used to calculate the instantaneous slope of each sampling point; The absolute value |s[i]| of all instantaneous slopes and the maximum value s of all instantaneous slope absolute values are calculated max , and the maximum value s max is set as a slope threshold T; All continuous sampling points satisfying |s[i]|≥T are identified to form the first mutation segment; Similarly, based on the second disturbance signal, the second mutation segment is obtained.

[0011] Further, the expression of the instantaneous slope is:

[0012] wherein, is the first a signal value of the i-th sampling point, a signal value of the i-th sampling point, a signal value of the i-th sampling point, a sampling time interval; The expression of the slope threshold T is:

[0013] wherein, is a set percentage coefficient.

[0014] Further, in the step of identifying the curve feature points in the first and second mutation segments to obtain the first and second time points, comprising: identifying the first curve feature point in the first mutation segment of the first disturbance signal, and recording the first time point corresponding to the first curve feature point . identifying the second curve feature point in the second mutation segment of the second disturbance signal, and recording the second time point corresponding to the second curve feature point .

[0015] Further, the first time point is:

[0016] The second time point is:

[0017] wherein, is the index corresponding to the first curve feature point in the first mutation segment, is the index corresponding to the second curve feature point in the second mutation segment.

[0018] Further, in the step of calculating the time difference of the two curve feature points according to the first and second time points, and calculating the instantaneous speed of the target in combination with the distance between the first and second magnetic flux gate sensors, comprising: According to the first and second time points, the time difference between the first and second curve feature points is calculated:

[0019] According to the time difference between the first and second curve feature points, the distance between the first and second magnetic flux gate sensors, the instantaneous speed of the target is calculated:

[0020] wherein, is the distance between the first and second magnetic flux gate sensors.

[0021] The technical scheme provided by the embodiment of the present disclosure can include the following beneficial effects: In the embodiment of the present disclosure, by the above method, on the one hand, by arranging two groups of magnetic flux gate magnetic sensors, the disturbance signal generated by the target in the local magnetic field when passing through is obtained; the speed of the target is estimated by extracting the feature points in the signal, for example, by taking the slope difference feature quantity as an example, the mutation section is identified by performing slope difference analysis on the signal, and the maximum slope point in the mutation section is extracted, and then the spatial distance between the sensors and the time difference of the appearance of the maximum slope point are combined to accurately estimate the instantaneous speed of the target. On the other hand, the whole process does not need to contact with the moving target, and does not depend on the attached mark or active signal emission, and has the significant advantages of low cost, high real-time and high robustness. BRIEF DESCRIPTION OF DRAWINGS

[0022] The drawings incorporated into the specification and constituting a part of the specification show embodiments consistent with the present disclosure and, together with the specification, serve to explain the principles of the present disclosure. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained from these drawings without creative labor for those skilled in the art.

[0023] Figure 1 A step diagram of a high-speed object speed estimation method in a closed cavity based on a magnetic signal feature point 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 a magnetic signal feature point time difference in an exemplary embodiment of the present disclosure is shown; Figure 3 A principle diagram of a high-speed object speed estimation method in a closed cavity based on a magnetic feature pulse width time difference in an exemplary embodiment of the present disclosure is shown; Figure 4 A curve diagram of a first disturbance signal in an exemplary embodiment of the present disclosure is shown; Figure 5 A curve diagram of a second disturbance signal in an exemplary embodiment of the present disclosure is shown; Figure 6 A schematic diagram of first feature point extraction in an exemplary embodiment of the present disclosure is shown; Figure 7 A schematic diagram of second feature point extraction in an exemplary embodiment of the present disclosure is shown; Figure 8 A schematic diagram of high-speed object speed measurement in a closed cavity in an exemplary embodiment of the present disclosure is shown; Figure 9 A schematic diagram of underwater vehicle speed measurement in an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0024] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations can be implemented in any

[0025] Moreover, the drawings are not necessarily to scale. Like reference numerals can be used to denote like parts throughout the various figures. Some embodiments can be used in conjunction with various

[0026] A method for estimating the speed of a high-speed object in a closed cavity based on the time difference of characteristic points of a magnetic signal 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 the time difference of characteristic points of a magnetic signal can include: Figure 1 Step S101: Collecting a first magnetic field intensity signal using a first fluxgate sensor and a second magnetic field intensity signal using a second fluxgate sensor; wherein the distance between the first fluxgate sensor and the second fluxgate sensor is a preset value; Step S102: Preprocessing 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: Detecting the mutation points of the first disturbance signal and the second disturbance signal to obtain a first mutation segment and a second mutation segment; Step S104: Identifying the curve characteristic points in the first mutation segment and the second mutation segment to obtain a first time point and a second time point; Step S105: Calculating the time difference of the two curve characteristic points according to the first time point and the second time point, and calculating the instantaneous speed of the target in combination with the distance between the first fluxgate sensor and the second fluxgate sensor.

[0027] Through the above method for estimating the speed of a high-speed object in a closed cavity based on the time difference of characteristic points of a magnetic signal, on the one hand, by arranging two groups of fluxgate magnetic sensors, the disturbance signal generated by the target in the local magnetic field when passing through is obtained; the characteristic points in the signal are extracted to estimate the speed of the target; taking the slope difference feature quantity as an example, the mutation segment is identified by performing slope difference analysis on the signal, and the maximum slope point is extracted in the mutation segment, and then the instantaneous speed of the target is accurately estimated in combination with the known spatial distance between the sensors and the time difference of the appearance of the maximum slope point. On the other hand, the entire process does not need to be in contact with the moving target, does not depend on the attached markers or active signal emission, and has the significant advantages of low cost, high real-time performance, and high robustness.​

[0028] The following will be described with reference to Figures 1 to 9 The above-mentioned steps of the method for estimating the speed of a high-speed object in a closed cavity based on the time difference of the feature points of the magnetic signal in the present example embodiment will be described in more detail.

[0029] 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.

[0030] Specifically, the first fluxgate sensor and the second fluxgate sensor are arranged on the motion path of the target to be measured, and the distance between them 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 and the second magnetic field intensity signal collected by the second fluxgate sensor are obtained.

[0031] 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.

[0032] Specifically, the magnetic field intensity signals collected by the first and second fluxgate sensors are obtained, and the signals are preprocessed: The signals are preliminarily smoothed by using a Gaussian weighted moving average filtering algorithm 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; De-trend processing is performed to eliminate background geomagnetic drift and baseline offset, so that the disturbance signal stably changes to zero mean value, which is convenient for subsequent analysis; In step S103, the first disturbance signal and the second disturbance signal are subjected to mutation point detection to obtain a first mutation segment and a second mutation segment.

[0033] Specifically, the preprocessed signals are subjected to mutation segment extraction: The instantaneous slope at each time point is calculated by using the difference method, and the formula is:

[0034] where x[i] is the signal value of the i-th sampling point, is the sampling time interval.

[0035] The absolute values |s[i]| of all slopes are counted, and the maximum value s max .

[0036] The slope threshold is set as:

[0037] wherein, is a percentage coefficient set empirically, and the points satisfying the condition |s[i]|>T are identified as mutation points, and the continuous mutation points are marked as a mutation segment.

[0038] All points satisfying |s[i]|>T are identified as mutation points, and the continuous mutation points are marked as a mutation segment.

[0039] In step S104, curve feature points are identified in the first and second mutation segments to obtain a first time point and a second time point.

[0040] Specifically, in the mutation segment, curve feature points (such as maximum slope difference, maximum amplitude, zero-crossing point, etc.) in the target magnetic signal are identified, and the times t1 and t2 corresponding to the curve feature points are recorded in the first and second fluxgate signals, respectively. In step S105, the time difference of the two curve feature points is calculated according to the first time point and the second time point, and the instantaneous speed of the target is calculated in combination with the distance between the first fluxgate sensor and the second fluxgate sensor.

[0041] Specifically, the time difference of the two curve feature points is calculated as the propagation time scale of the magnetic disturbance signal across the sensors.

[0042] According to the fluxgate spacing d and the time difference , the instantaneous speed of the measured target is calculated:

[0043] In this application, the velocity is estimated by combining the slope difference, maximum peak point and zero-crossing point and other signal characteristic quantities. Two groups of fluxgate sensors are arranged on the motion path of the measured target. By analyzing the magnetic field disturbance signals collected by the sensors and combining different characteristic quantities, the instantaneous velocity of the disturbance source can be more accurately estimated. Slope difference detection: by performing slope difference analysis on the signal, the mutation segment in the signal is identified, and the maximum slope point is extracted within the mutation segment. By calculating the time difference between the maximum slope points and combining the known distance between the two fluxgates, the instantaneous velocity of the target is accurately estimated. Maximum peak point detection: by analyzing the local maximum value of the signal, the time when the signal changes sharply is identified, which usually corresponds to the moment when the target passes through the fluxgate. By combining the time stamp of the maximum peak point and the spatial distance between the sensors, the velocity estimation is further optimized. Zero-crossing point detection: by detecting the zero-crossing point of the signal, the change from positive to negative or from negative to positive is captured, so that the specific moment of the target passing through is judged. By calculating the time difference between the two zero-crossing points and combining the known distance between the sensors, the accuracy of the velocity estimation is further improved. By combining the advantages of the three characteristic quantities, this application automatically identifies the disturbance time and the maximum disturbance position through the local signal change characteristics, and then calculates the target velocity through the time difference between the maximum slope point, the peak point and the zero-crossing point. This application is especially suitable for scenes where it is inconvenient to install mechanical speed measurement devices or non-destructive testing, and has the following advantages: 1. No need to contact the target: this application is completely based on the processing of fluxgate signals, without the need to contact the measured target or attach sensors to its surface, so it is suitable for speed measurement tasks in high-speed, sealed or inaccessible scenes; 2. Low dependence on input parameters: this application does not depend on complex magnetic field models or measured material information, but only relies on the signal itself and the distance between the fluxgates, reducing the sensitivity to physical parameters such as pipe permeability and equivalent radius, ensuring the accuracy and robustness of the speed measurement; 3. Simple and stable data processing: this application uses a slope difference and multi-feature point combination analysis strategy to achieve adaptive detection of mutation segments by setting a relative threshold, and extracts key feature points, avoiding complex manual intervention and improving processing stability; 4. No need for multiple measurements: the velocity value can be obtained each time the measured target passes through the fluxgate, avoiding the complex operations of traditional speed measurement relying on continuous sampling or trajectory fitting, and completing the speed measurement task in one pass; 5. Suitable for long-distance and complex environments: even in scenes with large sensor spacing and strong interference magnetic field, this application can still rely on signal mutation characteristics to complete effective identification, with good environmental adaptability and practicality; In summary, this application constructs a target velocity estimation method in a closed cavity based on multiple signal features (slope difference, maximum peak point, and zero crossing point, etc.), and combines adaptive signal change detection, filtering preprocessing, and velocity estimation model to provide an efficient, stable, and universal solution for non-contact dynamic parameter extraction.

[0044] In one embodiment, to further clarify the technical solution of this application, the following detailed description is provided in conjunction with an embodiment. This application uses two sets of fluxgate sensors to collect the magnetic flux disturbance signal caused by the passage of a target. Through slope difference calculation and abrupt change segment identification, feature points in the signal (such as the maximum slope difference, maximum amplitude, zero-crossing point, etc.) are extracted. Then, the time difference between the feature points of the two sensors is used for non-contact velocity estimation. This scheme exhibits strong robustness and good estimation accuracy in actual testing. Figure 2 The diagram shows a flowchart of a method for estimating the velocity of a high-speed object in a closed cavity based on the time difference of magnetic signal feature points.

[0045] In a specific embodiment, such as Figure 3 As shown, in a practical application scenario, the target being measured moves at a constant speed along a fixed channel. A first fluxgate sensor and a second fluxgate sensor are arranged along its path, with a distance of d between them (in meters). This represents the fluxgate sampling frequency, in Hz, and the signal sampling time interval is... .

[0046] Let the discrete magnetic field strength signals collected by the two sensors be x1[i] and x2[i], where i∈{1,2,…,N} and N is the signal length.

[0047] Signal preprocessing First, the acquired raw magnetic field signal undergoes dual filtering. To ensure the sensitivity and accuracy of subsequent abrupt change detection, a Gaussian weighted moving average filter is first used to smooth the signal. The window length is set to... The weighting coefficients are generated based on the standard Gaussian kernel:

[0048] in, This is the signal value after weighted averaging; To achieve a sum of half the window width; For the weight function, For the original signal at the index The value at; The weighting function is:

[0049] in, The standard deviation parameter of the width of the weight distribution is controlled.

[0050] The symmetric smooth weighting kernel is thus constructed, improving the stationarity of the signal.

[0051] A Butterworth low-pass filter is then applied to further suppress high-frequency interference, with a cutoff frequency set to and a filter order set to n to obtain a smoother disturbance curve while preserving the abrupt characteristics in the signal. The normalized frequency is used in filter design.

[0052] To eliminate the background geomagnetic drift trend, a sliding window detrending process is used to keep the disturbance signal in a stable zero-mean distribution throughout the time domain, improving the accuracy of the abrupt change detection.

[0053] Abrupt segment identification and maximum slope point extraction The first-order forward difference is used to calculate the instantaneous slope of the signal, defined as follows:

[0054] The abrupt change identification threshold is set as a percentage of the maximum slope value:

[0055] where θ is an empirical constant, usually set to 0.1≤θ≤0.2. When the instantaneous slope at a certain time satisfies |s[i]|≥T and this condition exists continuously for more than a preset length (such as 5 sampling points), it is determined that the region is an abrupt segment, and the first and last points in the segment are extracted as the start and end times of the abrupt segment.

[0056] As shown in Figure 4 , it is the curve graph of the first disturbance signal; as shown in Figure 5 , it is the curve graph of the second disturbance signal. Figure 4 and Figure 5 The red segment in is the abrupt segment detection result extracted by the slope difference method: First fluxgate sensor: abrupt start time: 5.82926172 seconds; abrupt end time: 6.22115234 seconds; abrupt duration: 0.39189062 seconds.

[0057] Second fluxgate sensor: abrupt start time: 5.85807422 seconds; abrupt end time: 6.29510547 seconds; abrupt duration: 0.43703125 seconds.

[0058] Further analysis inside the mutation segment, looking for curve feature points (such as maximum slope difference, maximum amplitude, zero crossing point, etc.) as the most significant position of signal change in this segment. Among them, the maximum slope difference is preferred, if the maximum slope difference is not good, then the maximum amplitude is selected, and finally the zero crossing point is considered. Two groups of magnetic flux gate signal feature point times are obtained respectively: The first magnetic flux gate signal feature point time in the mutation segment:

[0059] The second magnetic flux gate signal feature point time in the mutation segment:

[0060] Among them, , The index corresponding to the feature point in the mutation segment of the two signals respectively.

[0061] As shown in Figure 6 , it is a schematic diagram of the first feature point extraction; as shown in Figure 7 , it is a schematic diagram of the second feature point extraction. When extracting the slope feature point in the mutation segment, the maximum slope point is identified as the feature time: The first magnetic flux sensor: maximum slope value: 0.1608 T / s; feature point time: t1=5.98825391 seconds.

[0062] The second magnetic flux sensor: maximum slope value: 0.1428 T / s; feature point time: t2=5.98903906 seconds.

[0063] Speed estimation The distance between the two magnetic flux sensors is d, and the time difference experienced by the target between the two feature points is: =5.98903906-5.98825391=0.00078515 seconds The distance between the two magnetic flux sensors is d=40.5cm, then the target speed is estimated as:

[0064] The present application combines magnetic flux gate sensor technology and multi-signal feature quantity detection method, uses double magnetic flux gate sensor structure, through real-time acquisition and processing of magnetic disturbance signal caused by target motion, based on slope difference maximum value, maximum peak point and zero crossing point and other signal feature quantities, high precision estimation of target speed. By calculating the time difference between the feature points detected by the double sensor, and combining the known distance between the two sensors, the present application can efficiently and non-contact measure the speed of high-speed target.

[0065] The application provides a speed estimation method based on improved signal preprocessing and multi-feature quantity detection. The combination of Gaussian weighted moving average filtering and Butterworth low-pass filtering effectively suppresses noise interference and improves the robustness and sensitivity of feature recognition. Compared with the traditional speed measurement algorithm based on template comparison, the method is more suitable for complex magnetic environments with background drift and noise disturbance.

[0066] The application reduces the dependence on human intervention and heuristic judgment by accurately extracting the maximum change point time within the mutation segment, improving the automation degree and time resolution of speed estimation, and is particularly suitable for dynamic tracking and precise speed measurement of high-speed targets.

[0067] The application calculates the speed based on the time difference of feature points, has a simple structure, does not need to introduce a complex magnetic field model, does not need to install additional markers or electronic devices on the target object, and can realize low-cost and high-efficiency passive speed measurement. The application only extracts feature points in the target signal, has small algorithm calculation amount and fast processing speed, and is suitable for real-time operation of embedded devices.

[0068] The embodiments described in the application are only used to illustrate the key steps, algorithm logic and processing flow of the application, and cannot be understood as limiting the protection scope of the application. Any equivalent improvement, adjustment or replacement scheme made within the basic concept of the application shall be considered to fall within the protection scope of the application.

[0069] In a 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 magnetic signals of two fluxgate sensors are used to measure the movement speed of a target object (such as a high-speed object) through the characteristics and differential feature quantities. Figure 8 The basic structure of the system is shown, wherein the first fluxgate and the second fluxgate are located on the movement path of the target object, and the distance between the two is a known value d. When the target object passes through the first fluxgate, the magnetic field is disturbed, and the signal change is collected by the fluxgate sensor; similarly, when the target object passes through the second fluxgate, a disturbance signal is also generated.

[0070] Through twice signal feature quantity detection, the signal feature points in the signal can be accurately identified. According to the known distance d between the two sensors and the time difference of the signal feature points , the speed is estimated, and the method has the advantages of high precision, strong real-time performance and non-contact measurement.

[0071] In a specific embodiment, the speed estimation and detection of an underwater vehicle are performed. Figure 9The structure of the underwater vehicle speed measurement system is shown, wherein two magnetic flux gate sensors are arranged at different positions of the movement path of the underwater vehicle respectively, and are used to estimate the speed of the vehicle through the magnetic field disturbance signal. The first magnetic flux gate and the second magnetic flux gate respectively collect the magnetic field change when the target object (such as an underwater vehicle) passes.

[0072] In the system, the movement 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 the change; similarly, when the underwater vehicle passes through the second magnetic flux gate, the magnetic field changes again. The system estimates the speed by calculating the time difference between the two magnetic field disturbances , combined with the known distance d between the two magnetic flux gates, which is suitable for underwater vehicle speed monitoring and has the advantages of high precision, non-contact, strong real-time performance, etc.

[0073] Through the above-mentioned closed cavity high-speed object speed estimation method based on the time difference of magnetic signal feature points, on the one hand, two groups of magnetic flux gate magnetic sensors are arranged to obtain the disturbance signal generated by the target in the local magnetic field when the target passes through; the target speed is estimated by extracting the feature points in the signal. Taking the slope difference feature quantity as an example, the mutation section is identified by performing slope difference analysis on the signal, and the maximum slope point is extracted in the mutation section, and then the known spatial distance between the sensors and the time difference of the appearance of the maximum slope point are combined to accurately estimate the instantaneous speed of the target. On the other hand, the whole process does not need to contact with the moving target, does not depend on the attached markers or active signal emission, and has the significant advantages of low cost, high real-time performance and high robustness.

[0074] It should be understood that the directions or positional relationships indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like in the above description are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the embodiments of the present disclosure and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements indicated must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the embodiments of the present disclosure.

[0075] In addition, the terms "first" and "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first" and "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 "multiple" is two or more, unless otherwise specifically limited.

[0076] In the embodiments of the present disclosure, unless specifically defined and limited otherwise, the terms "mount", "connect", "connection", "fixed", and the like should be understood broadly, for example, can be fixed connection, can also be detachable connection, or integrated; can be mechanical connection, can also be electrical connection; can be direct connection, can also be indirect connection through an intermediate medium, can be internal communication of two elements or interaction relationship between two elements. For those skilled in the art, the specific meanings of the above terms in the present disclosure can be understood according to the specific circumstances.

[0077] In the embodiments of the present disclosure, unless specifically defined and limited otherwise, the first feature "on" or "under" 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, the first feature "on", "above" and "over" the second feature includes 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. The first feature "under", "below" and "under" the second feature includes 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.

[0078] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" and the like 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 present disclosure. In the specification, 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.

[0079] Other embodiments of the present disclosure will be apparent to those skilled in the art upon consideration of the specification and practice of the applications disclosed. The present application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles thereof and include the known or customary practice of the art to which the present disclosure pertains. The specification and examples are to be regarded as illustrative only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A method for estimating the velocity of a high-speed object in a closed cavity based on the time difference of characteristic points of a magnetic signal, characterized in that, The method comprises the following steps: 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; Preprocessing 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; Detecting the first disturbance signal and the second disturbance signal to obtain a first mutation segment and a second mutation segment; Identifying curve feature points in the first mutation segment and the second mutation segment to obtain a first time point and a second time point; According to the first time point and the second time point, calculating the time difference of the two curve feature points, and combining the distance between the first fluxgate sensor and the second fluxgate sensor to calculate the instantaneous speed of the target.

2. The method of claim 1, wherein, The preprocessing step comprises: Using a Gaussian weighted moving average filtering algorithm to preliminarily smooth the first magnetic field intensity signal and the second magnetic field intensity signal to weaken high-frequency noise and local fluctuations; Using a low-pass Butterworth filter to process the first magnetic field intensity signal and the second magnetic field intensity signal to suppress residual high-frequency interference; Performing detrend processing on the first magnetic field intensity signal and the second magnetic field intensity signal to eliminate background geomagnetic drift and baseline offset, so that the disturbance signal stably changes with zero mean value.

3. The method of claim 2, wherein the time difference between the characteristic points of the magnetic signal is determined by: ###0001### where T is the time difference between the characteristic points of the magnetic signal, t1 is the time of the first characteristic point, t2 is the time of the second characteristic point, and f is the frequency of the magnetic signal. In the step of detecting the first disturbance signal and the second disturbance signal to obtain the first mutation segment and the second mutation segment, the following steps are included: For the first disturbance signal, use a first-order forward difference method to calculate the instantaneous slope of each sampling point; calculating all instantaneous slope absolute values |s[i]| and a maximum value s of all instantaneous slope absolute values max and according to the maximum value s max setting a slope threshold value T; Identify all continuous sampling points that satisfy |s[i]|≥T to form the first mutation segment; Similarly, based on the second disturbance signal, the second mutation segment is obtained.

4. The method of claim 3, wherein the time difference between the characteristic points of the magnetic signal is determined by: ###0001### where T is the time difference between the characteristic points of the magnetic signal, t1 is the time of the first characteristic point, t2 is the time of the second characteristic point, and f is the frequency of the magnetic signal. The expression of the instantaneous slope is: wherein is the signal value for the th sampling point, is the signal value for the th sampling point, is the sampling time interval; The expression of the slope threshold T is: wherein, is a set percentage factor.

5. The method of claim 4, wherein, In the step of identifying curve feature points in the first mutation segment and the second mutation segment to obtain the first time point and the second time point, the following steps are included: identify a first curve feature point in the first mutation section of the first disturbance signal, and record a first time point corresponding to the first curve feature point ; identify a second curve feature point in a second abrupt change segment of the second disturbance signal, and record a second time point corresponding to the second curve feature point .

6. The method of claim 5, wherein the time difference between the characteristic points of the magnetic signal is determined by: ###0001### where T is the time difference between the characteristic points of the magnetic signal, t1 is the time of the first characteristic point, t2 is the time of the second characteristic point, and f is the frequency of the magnetic signal. The first time point is: The second time point is: wherein, is an index corresponding to the first curve feature point in the first mutation section, is an index corresponding to the second curve feature point in the second mutation section.

7. The method of claim 6, wherein the time difference between the characteristic points of the magnetic signal is determined by: ###0001### where T is the time difference between the characteristic points of the magnetic signal, t1 is the time of the first characteristic point, t2 is the time of the second characteristic point, and f is the frequency of the magnetic signal. In the step of calculating the time difference of the two curve feature points according to the first time point and the second time point, and combining the distance between the first fluxgate sensor and the second fluxgate sensor to calculate the instantaneous speed of the target, the following steps are included: According to the first time point and the second time point, the time difference between the first curve feature point and the second curve feature point is calculated: According to the time difference between the first curve feature point and the second curve feature point, the distance between the first fluxgate sensor and the second fluxgate sensor, the instantaneous speed of the target is calculated: wherein is the distance between the first fluxgate sensor and the second fluxgate sensor.

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