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

Abstract

The application belongs to the technical field of electronic measurement and detection. The application provides a closed cavity high-speed object velocity estimation method based on magnetic signal feature point time difference. The disclosure embodiment obtains the disturbance signal generated by the target in the local magnetic field by laying two groups of fluxgate magnetic sensors; the velocity of the target 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 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 velocity of the target. The whole process does not need to contact with the moving target, does not depend on the attached label or active signal emission, and has the significant advantages of low cost, high real-time and high robustness.

Description

Technical Field

[0001] This disclosure relates to the field of electronic measurement and detection technology, and in particular to 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. Background Technology

[0002] Speed ​​measurement technology has significant applications in industrial automation, transportation, and life monitoring. The speed of a moving target is a key parameter describing its dynamic behavior and is fundamental for state assessment, intelligent decision-making, and control. Existing speed measurement methods mainly fall into two categories: contact speed measurement and non-contact speed measurement.

[0003] Contact-based speed measurement methods, such as speed encoders and tachometers, while offering high accuracy, rely on a physical connection to the target object. This makes them unsuitable for environments with poor contact, such as high temperatures, high speeds, strong electromagnetic interference, or biological tissues. They also suffer from short lifespans, difficult maintenance, and poor adaptability. Non-contact speed measurement technologies, on the other hand, acquire target motion information through lasers, Doppler radar, and image recognition. While offering some flexibility, they are significantly affected by ambient light, obstructions, reflectivity, and background noise in complex scenarios, making it difficult to guarantee measurement accuracy and stability.

[0004] With the widespread application of high-sensitivity magnetic sensors such as fluxgate magnetometers, non-contact speed measurement technology based on the magnetic signal transmission characteristics has attracted increasing attention. This type of method senses the motion state by collecting signal changes caused by the perturbed magnetic field of a target. It features anti-obstruction, non-radiation, and strong concealment, making it particularly suitable for passive detection in complex environments such as enclosed spaces and underground structures. However, current methods based on magnetic signal transmission characteristics mostly only determine the existence of a target or analyze behavioral trends, making it difficult to directly quantify the target's speed. Especially in scenarios with weak signals, multi-channel interference, and high-frequency changes, traditional algorithms suffer from shortcomings in real-time performance and robustness.

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

[0006] It should be noted that this section is intended to provide background or context for the technical solutions of this disclosure as set forth in the claims. The description herein does not constitute an admission that it is prior art simply because it is included in this section. Summary of the Invention

[0007] The purpose of this disclosure is to provide 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, thereby overcoming, to at least some extent, one or more problems caused by the limitations and defects of related technologies.

[0008] According to embodiments of this disclosure, a method for estimating the velocity of a high-speed object within a closed cavity based on the time difference of magnetic signal feature points is provided, including:

[0009] 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;

[0010] 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;

[0011] Abrupt change points are detected in the first and second disturbance signals to obtain the first and second abrupt change segments.

[0012] Feature points of the curve are identified within the first and second mutation segments to obtain the first and second time points.

[0013] Based on the first and second time points, the time difference between the two curve feature points is calculated, and the instantaneous velocity of the target is calculated in combination with the distance between the first fluxgate sensor and the second fluxgate sensor.

[0014] Furthermore, the preprocessing steps include:

[0015] The first and second magnetic field strength signals are initially smoothed using a Gaussian weighted moving average filtering algorithm to reduce high-frequency noise and local fluctuations.

[0016] The first and second magnetic field strength signals are processed using a low-pass Butterworth filter to suppress the remaining high-frequency interference.

[0017] The first and second magnetic field strength signals are detrended to eliminate background geomagnetic drift and baseline offset, so that the disturbance signal is stable with zero mean variation.

[0018] Further, the step of detecting abrupt change points in the first and second perturbation signals to obtain the first and second abrupt change segments includes:

[0019] For the first disturbance signal, the instantaneous slope of each sampling point is calculated using the first-order forward difference method;

[0020] Calculate the absolute value of all instantaneous slopes |s[i]| and the maximum value s among all the absolute values ​​of instantaneous slopes. max And based on the maximum value s max Set the slope threshold T;

[0021] Identify all consecutive sampling points that satisfy |s[i]|≥T, and form the first mutation segment;

[0022] Similarly, based on the second perturbation signal, the second mutation segment is obtained.

[0023] Furthermore, the expression for the instantaneous slope is:

[0024]

[0025] in, For the first The signal value at each sampling point For the first The signal value at each sampling point The sampling time interval;

[0026] The expression for the slope threshold T is:

[0027]

[0028] in, The set percentage coefficient.

[0029] Further, the step of identifying curve feature points within the first and second mutation segments to obtain the first and second time points includes:

[0030] Identify the first curve feature point in the first abrupt change segment of the first disturbance signal, and record the first time point corresponding to the first curve feature point. ;

[0031] Identify the second curve feature points in the second abrupt change segment of the second disturbance signal, and record the second time points corresponding to the second curve feature points. .

[0032] Furthermore, the first point in time is:

[0033]

[0034] The second time point is:

[0035]

[0036] in, This is the index corresponding to the first curve feature point in the first mutation segment. This is the index corresponding to the second curve feature point in the second mutation segment.

[0037] Further, the step of calculating the time difference between two curve feature points based on the first and second time points, and calculating the instantaneous velocity of the target in conjunction with the distance between the first and second fluxgate sensors, includes:

[0038] Based on the first and second time points, calculate the time difference between the first curve feature point and the second curve feature point:

[0039]

[0040] The instantaneous velocity of the target is calculated based on the time difference between the feature points of the first and second curves and the distance between the first and second fluxgate sensors.

[0041]

[0042] in, The distance between the first fluxgate sensor and the second fluxgate sensor is denoted as .

[0043] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0044] In the embodiments of this disclosure, the above method achieves several advantages. First, by deploying two sets of fluxgate magnetic sensors, the disturbance signal generated in the local magnetic field when the target passes through is acquired. The target's velocity is estimated by extracting feature points from the signal. Taking slope difference features as an example, slope difference analysis is performed on the signal to identify abrupt change segments, and the maximum slope point is extracted within these segments. Then, by combining the known spatial distance between the sensors with the time difference between the occurrence of the maximum slope point, the instantaneous velocity of the target is accurately estimated. Second, the entire process does not require contact with the moving target and does not rely on attached markers or active signal transmission, offering significant advantages such as low cost, high real-time performance, and high robustness. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0046] Figure 1 A flowchart illustrating the steps 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 in an exemplary embodiment of this disclosure is shown.

[0047] Figure 2 A flowchart illustrating the specific process of velocity estimation method for high-speed objects in a closed cavity based on the time difference of magnetic signal feature points in an exemplary embodiment of this disclosure is shown.

[0048] Figure 3 A schematic diagram illustrating the principle of a velocity estimation method for high-speed objects in a closed cavity based on magnetic feature pulse width time difference in an exemplary embodiment of this disclosure;

[0049] Figure 4 A graph of the first disturbance signal in an exemplary embodiment of this disclosure is shown;

[0050] Figure 5 A graph of the second disturbance signal in an exemplary embodiment of this disclosure is shown;

[0051] Figure 6 This diagram illustrates the extraction of the first feature point in an exemplary embodiment of this disclosure;

[0052] Figure 7 This diagram illustrates the extraction of the second feature point in an exemplary embodiment of this disclosure.

[0053] Figure 8 A schematic diagram illustrating the velocity measurement of a high-speed object within a closed cavity in an exemplary embodiment of this disclosure;

[0054] Figure 9 This diagram illustrates the speed measurement of an underwater vehicle in an exemplary embodiment of this disclosure. Detailed Implementation

[0055] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0056] Furthermore, the accompanying drawings are merely illustrative diagrams of embodiments of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities.

[0057] This example implementation provides a method for estimating the velocity of a high-speed object within a closed cavity based on the time difference of magnetic signal feature points. (Reference) Figure 1 As shown, the velocity estimation method for high-speed objects within a closed cavity based on the time difference of magnetic signal feature points may include:

[0058] Step S101: Acquire a first magnetic field strength signal using a first fluxgate sensor and acquire a second magnetic field strength signal using a second fluxgate sensor; wherein the distance between the first fluxgate sensor and the second fluxgate sensor is a preset value;

[0059] Step S102: Preprocess the first magnetic field strength signal and the second magnetic field strength signal respectively to obtain the first disturbance signal and the second disturbance signal;

[0060] Step S103: Detect abrupt change points in the first and second disturbance signals to obtain the first and second abrupt change segments;

[0061] Step S104: Identify curve feature points within the first and second mutation segments to obtain the first and second time points;

[0062] Step S105: Calculate the time difference between the two curve feature points based on the first time point and the second time point, and calculate the instantaneous velocity of the target by combining the distance between the first fluxgate sensor and the second fluxgate sensor.

[0063] The aforementioned method for estimating the velocity of high-speed objects within a closed cavity based on the time difference of magnetic signal feature points offers several advantages. Firstly, by deploying two sets of fluxgate magnetic sensors, the perturbation signal generated in the local magnetic field as the target passes through is acquired. Feature points in the signal are extracted for velocity estimation. Taking slope difference features as an example, slope difference analysis is performed on the signal to identify abrupt change segments, and the maximum slope point is extracted within these segments. Then, by combining the known spatial distance between the sensors with the time difference of the maximum slope point, the instantaneous velocity of the target is accurately estimated. Secondly, the entire process requires no contact with the moving target and does not rely on attached markers or active signal transmission, offering significant advantages such as low cost, high real-time performance, and high robustness.

[0064] Below, we will refer to Figures 1 to 9 The steps of the above-described method for estimating the velocity of a high-speed object in a closed cavity based on the time difference of magnetic signal feature points in this example embodiment will be explained in more detail.

[0065] In step S101, 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.

[0066] Specifically, a first fluxgate sensor and a second fluxgate sensor are set along the movement path of the target, with a known distance d between them, to collect the magnetic field disturbance signals caused by the target's passage. The first magnetic field strength signal collected by the first fluxgate sensor and the second magnetic field strength signal collected by the second fluxgate sensor are obtained.

[0067] In step S102, the first magnetic field strength signal and the second magnetic field strength signal are preprocessed to obtain the first disturbance signal and the second disturbance signal.

[0068] Specifically, the magnetic field strength signals acquired by the first and second fluxgate sensors are obtained, and the signals are preprocessed:

[0069] The signal is initially smoothed using a Gaussian weighted moving average filtering algorithm to reduce high-frequency noise and local fluctuations.

[0070] Further processing using a low-pass Butterworth filter is employed to suppress remaining high-frequency interference;

[0071] Detrending processing is performed to eliminate background geomagnetic drift and baseline shift, making the disturbance signal stable with zero mean variation, which facilitates subsequent analysis;

[0072] In step S103, abrupt change point detection is performed on the first disturbance signal and the second disturbance signal to obtain the first abrupt change segment and the second abrupt change segment.

[0073] Specifically, abrupt changes are extracted from the preprocessed signal:

[0074] The instantaneous slope at each time point is calculated using the finite difference method, and the formula is as follows:

[0075]

[0076] Where x[i] is the signal value of the i-th sampling point. This represents the sampling time interval.

[0077] Calculate the absolute value of all slopes |s[i]| and then calculate the maximum value s. max .

[0078] Set the slope threshold as follows:

[0079]

[0080] in, The percentage coefficient set for experience is used to identify points that satisfy the condition |s[i]|>T as mutation points, and consecutive mutation points are marked as mutation segments.

[0081] Identify all points that satisfy |s[i]|>T as mutation points, and mark consecutive mutation points as mutation segments.

[0082] In step S104, curve feature points are identified within the first mutation segment and the second mutation segment to obtain the first time point and the second time point.

[0083] Specifically, within the abrupt change segment, curve feature points (such as the 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.

[0084] In step S105, the time difference between the two curve feature points is calculated based on the first time point and the second time point, and the instantaneous velocity of the target is calculated in combination with the distance between the first fluxgate sensor and the second fluxgate sensor.

[0085] Specifically, calculate the time difference between the characteristic points of the two curves. , which serves as the time scale for the propagation of magnetic disturbance signals across sensors.

[0086] Based on the fluxgate spacing d and the time difference Calculate the instantaneous velocity of the target being measured:

[0087]

[0088] In this application, velocity estimation is performed by combining multiple signal features such as slope difference, maximum peak point, and zero-crossing point. Two sets of fluxgate sensors are deployed along the motion path of the target. By utilizing the magnetic field disturbance signals collected by these sensors and combining the analysis of different feature quantities, the instantaneous velocity of the disturbance source can be estimated more accurately. Slope difference detection: By performing slope difference analysis on the signal, abrupt changes in the signal are identified, and the maximum slope point is extracted within these abrupt changes. By calculating the time difference between the maximum slope points and combining it with the known distance between the two fluxgate sensors, the instantaneous velocity of the target is accurately estimated. Maximum peak point detection: By analyzing the local maximum values ​​of the signal, the moment of drastic change in the signal is identified, usually corresponding to the instant the target passes through the fluxgate. The velocity estimation is further optimized by using the timestamp of the maximum peak point and the spatial distance between the sensors. Zero-crossing point detection: By detecting the zero-crossing points of the signal, the change of the signal from positive to negative or from negative to positive is captured, thereby determining the specific moment the target passes through. By calculating the time difference between the two zero-crossing points and combining it with the known sensor distance, the accuracy of the velocity estimation is further improved. Combining the advantages of these three characteristic quantities, this application automatically identifies the time of disturbance and the location of maximum disturbance by utilizing the local signal change characteristics, and then calculates the target velocity using the time difference between the maximum slope point, peak point, and zero-crossing point. This application is particularly suitable for scenarios where it is inconvenient to install mechanical speed measuring equipment or where non-destructive testing is required, and has the following advantages:

[0089] 1. No contact with the target: This application is based entirely on fluxgate signals for processing, without contact with the target being measured or attaching sensors to its surface. Therefore, it is suitable for speed measurement tasks in high-speed, enclosed or inaccessible scenarios.

[0090] 2. Low dependence on input parameters: This application does not rely on complex magnetic field models or material information of the measured object, but only on the spacing of the fluxgates and the characteristics of the signal itself, which reduces the sensitivity to physical parameters such as the permeability and equivalent radius of the pipe, thus ensuring the speed measurement accuracy and robustness.

[0091] 3. Simple and stable data processing: This application adopts an analysis strategy that combines slope difference with multiple feature points. By setting a relative threshold, it achieves adaptive detection of abrupt change segments and extracts key feature points, avoiding complex manual intervention and improving processing stability.

[0092] 4. No need for multiple measurements: The velocity value can be obtained each time the target passes through the fluxgate, avoiding the complex operation of traditional speed measurement that relies on continuous sampling or trajectory fitting. The speed measurement task can be completed in one pass.

[0093] 5. Applicable to long-distance and complex environments: Even in scenarios with large sensor spacing and strong interfering magnetic fields, this application can still achieve effective identification by relying on signal change characteristics, and has good environmental adaptability and practicality;

[0094] 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.

[0095] 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.

[0096] 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... .

[0097] 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.

[0098] Signal preprocessing

[0099] 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:

[0100]

[0101] 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;

[0102] The weighting function is:

[0103]

[0104] in, The standard deviation parameter is used to control the width of the weight distribution.

[0105] This allows for the construction of a symmetrical, smooth weighted kernel, thereby improving signal stability.

[0106] Subsequently, a Butterworth low-pass filter was applied to further suppress high-frequency interference, with the cutoff frequency set to [value missing]. The filter order is set to n to obtain a smoother perturbation curve. While preserving the abrupt changes in the signal, the filter design employs a normalized frequency. .

[0107] To eliminate the background geomagnetic drift trend, a sliding window detrending process is used to maintain a stable zero-mean distribution of the disturbance signal throughout the time domain, thereby improving the accuracy of abrupt change detection.

[0108] Mutation segment identification and maximum slope point extraction

[0109] The instantaneous slope of the signal is calculated using first-order forward difference, defined as follows:

[0110]

[0111] Set the mutation identification threshold as a percentage of the maximum slope value:

[0112]

[0113] Where θ is an empirical constant, usually set to 0.1≤θ≤0.2. When the instantaneous slope at a certain moment satisfies |s[i]|≥T, and this condition exists continuously for more than a preset length (such as 5 sampling points), the region is determined to be a mutation segment, and the first and last points are extracted as the start and end times of the mutation segment.

[0114] like Figure 4 The image shown is a graph of the first disturbance signal; as shown... Figure 5 The figure shown is a graph of the second disturbance signal. Figure 4 and Figure 5The highlighted red segments represent the detection results of mutation segments extracted using the slope difference method.

[0115] First fluxgate sensor: Start time of mutation: 5.82926172 seconds; End time of mutation: 6.22115234 seconds; Duration of mutation: 0.39189062 seconds.

[0116] Second fluxgate sensor: Abrupt start time: 5.85807422 seconds; Abrupt end time: 6.29510547 seconds; Abrupt duration: 0.43703125 seconds.

[0117] Further analysis within the abrupt change segment identifies curve feature points (such as the maximum slope difference, maximum amplitude, and zero-crossing points) as the locations where the signal change is most significant. The maximum slope difference is the first choice; if this is insufficient, the maximum amplitude is used, and zero-crossing points are considered last. The times of the two sets of fluxgate signal feature points are obtained separately:

[0118] The time of characteristic points within the abrupt change segment of the first fluxgate signal:

[0119]

[0120] The time of characteristic points within the abrupt change segment of the second fluxgate signal:

[0121]

[0122] in, , These are the indices corresponding to the feature points within the abrupt change segments of the two signals.

[0123] like Figure 6 The image shown is a schematic diagram of the first feature point extraction; as shown... Figure 7 The diagram shown illustrates the extraction of the second feature point. Slope feature points are extracted within the abrupt change segment, and the point of maximum slope is identified as the feature time point.

[0124] First fluxgate sensor: maximum slope value: 0.1608 T / s; feature point time: t1=5.98825391 seconds.

[0125] Second fluxgate sensor: Maximum slope: 0.1428T / s; Feature point time: t2=5.98903906 seconds.

[0126] Speed ​​estimation

[0127] Given that the distance between the two fluxgate sensors is d, the time difference for the target to propagate between the two feature points is:

[0128] =5.98903906-5.98825391=0.00078515 seconds

[0129] If the distance between the two fluxgate sensors is d = 40.5 cm, then the target velocity is estimated as follows:

[0130]

[0131] This application combines fluxgate sensing technology with a multi-signal feature quantity detection method. Utilizing a dual fluxgate sensor structure, it acquires and processes magnetic disturbance signals caused by target motion in real time. Based on multiple signal features such as the maximum slope difference, maximum peak point, and zero-crossing point, it performs high-precision estimation of the target velocity. By calculating the time difference between feature points detected by the two sensors and combining this with the known distance between the two sensors, this application can efficiently and non-contactly measure the velocity of high-speed targets.

[0132] This application proposes a velocity estimation method based on improved signal preprocessing and multi-feature detection. By combining Gaussian weighted moving average filtering with Butterworth low-pass filtering, noise interference is effectively suppressed, improving the robustness and sensitivity of feature recognition. Compared with traditional template-based velocity measurement algorithms, the proposed method is more suitable for complex magnetic environments with background drift and noise disturbances.

[0133] This application reduces reliance on human intervention and heuristic judgment by accurately extracting the time of the maximum change point within the mutation segment, thereby improving the automation and time resolution of velocity estimation, and is especially suitable for dynamic tracking and precision velocity measurement tasks of high-speed targets.

[0134] This application calculates velocity based on the time difference of feature points. It features a simple structure, requires no complex magnetic field model, and eliminates the need for additional markers or electronic devices on the target object, enabling low-cost, high-efficiency passive velocity measurement. Furthermore, this application extracts only abrupt change feature points from the target signal, resulting in a computationally intensive and fast algorithm suitable for real-time operation in embedded devices.

[0135] The embodiments described in this application are only used to illustrate the key steps, algorithm logic, and processing flow of this application, and should not be construed as limiting the scope of protection of this application. All equivalent improvements, adjustments, or substitutions made under the basic concept of this application should be considered to fall within the scope of protection of this application.

[0136] In one specific embodiment, velocity measurement of a high-speed object inside a closed cavity:

[0137] In a high-speed object velocity measurement system within a closed cavity, the magnetic signal transmission characteristics and differential features of two fluxgate sensors are used to measure the velocity of the target object (such as a high-speed object). Figure 8The basic structure of the system is shown, where the first and second fluxgates are located on the path of the target object, and the distance between them is a known value d. When the target object passes through the first fluxgate, the magnetic field is disturbed, and the fluxgate sensor collects the signal change; similarly, when the target object passes through the second fluxgate, a disturbance signal is also generated.

[0138] By performing two signal feature measurements, signal feature points can be accurately identified. This is based on the known distance d between the two sensors and the time difference between the signal feature points. This method, which estimates speed, has the advantages of high accuracy, strong real-time performance, and non-contact measurement.

[0139] In one specific embodiment, underwater vehicle speed estimation and detection:

[0140] Figure 9 The structure of an underwater vehicle speed measurement system is shown, in which two fluxgate sensors are positioned at different locations along the underwater vehicle's path to estimate its speed using magnetic field disturbance signals. The first and second fluxgates collect the changes in the magnetic field as a target object (such as an underwater vehicle) passes by.

[0141] In the system, the target's velocity is estimated by calculating the time delay between two fluxgates. Specifically, when the underwater vehicle passes through the first fluxgate, the magnetic field changes, and the fluxgate records this change; similarly, when the underwater vehicle passes through the second fluxgate, the magnetic field changes again. The system calculates the time difference between the two magnetic field disturbances. By combining the known distance d between the two fluxgates, the speed is estimated. This method is suitable for speed monitoring of underwater vehicles and has the advantages of high precision, non-contact operation, and strong real-time performance.

[0142] The aforementioned method for estimating the velocity of high-speed objects within a closed cavity based on the time difference of magnetic signal feature points offers several advantages. Firstly, by deploying two sets of fluxgate magnetic sensors, the perturbation signal generated in the local magnetic field as the target passes through is acquired. Feature points in the signal are extracted for velocity estimation. Taking slope difference features as an example, slope difference analysis is performed on the signal to identify abrupt change segments, and the maximum slope point is extracted within these segments. Then, by combining the known spatial distance between the sensors with the time difference of the maximum slope point, the instantaneous velocity of the target is accurately estimated. Secondly, the entire process requires no contact with the moving target and does not rely on attached markers or active signal transmission, offering significant advantages such as low cost, high real-time performance, and high robustness.

[0143] It should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise" in the above description indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this disclosure and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this disclosure.

[0144] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of this disclosure, "a plurality of" means two or more, unless otherwise explicitly specified.

[0145] In the embodiments of this disclosure, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this disclosure according to the specific circumstances.

[0146] In embodiments of this disclosure, unless otherwise expressly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature being directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0147] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0148] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this 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 magnetic signal feature points, 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; Abrupt change points are detected in the first and second disturbance signals to obtain the first and second abrupt change segments. Feature points of the curve are identified within the first and second mutation segments to obtain the first and second time points. Based on the first and second time points, the time difference between the two curve feature points is calculated, and the instantaneous velocity of the target is calculated by combining the distance between the first and second fluxgate sensors; where, The step of detecting abrupt change points in the first and second perturbation signals to obtain the first and second abrupt change segments includes: For the first disturbance signal, the instantaneous slope of each sampling point is calculated using the first-order forward difference method; Calculate the absolute value of all instantaneous slopes |s[i]| and the maximum value s among all the absolute values ​​of instantaneous slopes. max And based on the maximum value s max Set the slope threshold T; Identify all consecutive sampling points that satisfy |s[i]|≥T, and form the first mutation segment; Similarly, based on the second perturbation signal, the second mutation segment is obtained; The expression for the instantaneous slope is: In the formula, For the first The signal value at each sampling point For the first The signal value at each sampling point The sampling time interval; The expression for the slope threshold T is: In the formula, The set percentage coefficient; The step of identifying curve feature points within the first and second mutation segments to obtain the first and second time points includes: Identify the first curve feature point in the first abrupt change segment of the first disturbance signal, and record the first time point corresponding to the first curve feature point. ; Identify the second curve feature points in the second abrupt change segment of the second disturbance signal, and record the second time points corresponding to the second curve feature points. ; The first point in time is: The second time point is: In the formula, This is the index corresponding to the first curve feature point in the first mutation segment. This is the index corresponding to the feature point of the second curve in the second mutation segment; The steps of calculating the time difference between two curve feature points based on the first and second time points, and calculating the instantaneous velocity of the target by combining the distance between the first and second fluxgate sensors, include: Based on the first and second time points, calculate the time difference between the first curve feature point and the second curve feature point: The instantaneous velocity of the target is calculated based on the time difference between the feature points of the first and second curves and the distance between the first and second fluxgate sensors. In the formula, The distance between the first fluxgate sensor and the second fluxgate sensor is denoted as .

2. The method for estimating the velocity of a high-speed object in a closed cavity based on the time difference of magnetic signal feature points according to claim 1, characterized in that, The preprocessing steps include: The first and second magnetic field strength signals are initially smoothed using a Gaussian weighted moving average filtering algorithm to reduce high-frequency noise and local fluctuations. The first and second magnetic field strength signals are processed using a low-pass Butterworth filter to suppress the remaining high-frequency interference. The first and second magnetic field strength signals are detrended to eliminate background geomagnetic drift and baseline offset, so that the disturbance signal is stable with zero mean variation.

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