Velocity vector sensing method and device, equipment, storage medium and product

Abstract

The invention relates to the technical field of signal processing, and discloses a velocity vector sensing method and device, equipment, a storage medium and a product, and the method comprises the steps: carrying out the down-conversion and matched filtering of a pilot signal transmitted by a user side, obtaining a target discrete signal sequence, carrying out the fast Fourier transform of the target discrete signal sequence, and obtaining a target discrete signal sequence; and determining a signal frequency sequence based on a discrete Fourier transform result, determining a frequency increase rate and a Doppler offset according to the signal frequency sequence, obtaining a signal propagation delay according to the discrete Fourier transform result, and further determining a velocity vector sensing result of the user side. According to the invention, hardware complexity and power consumption are reduced through down-conversion and matched filtering processing, and the signal-to-noise ratio is improved; according to the method, fast Fourier transform and inverse fast Fourier transform are combined, a signal frequency sequence, signal propagation time delay, frequency increase rate and Doppler offset are extracted, the velocity vector of a user side is sensed comprehensively, and the limitation that only part of parameters can be sensed in the prior art is overcome.

Description

Technical Field

[0001] The present application relates to the field of signal processing technology, and in particular to a velocity vector sensing method, apparatus, device, storage medium and product. Background Art

[0002] Integrated communication and perception empowers wireless communication devices with sensing capabilities, enabling efficient sharing of hardware and spectrum resources. In many integrated communication and perception applications, it is necessary to sense the user's velocity and position over time to model their trajectory and predict their next position. The perceived trajectory of the user is often complex; at a specific moment, its motion relative to the base station often involves both radial and tangential motion. However, existing velocity vector sensing technologies primarily focus on radial velocity estimation methods based on Doppler shift, all of which rely on large-scale digital receiving antenna arrays, which are expensive and power-intensive. Summary of the Invention

[0003] The main purpose of this application is to provide a velocity vector sensing method, device, equipment, storage medium and product, aiming to solve the technical problems of incomplete perception and high cost of existing velocity vector sensing methods.

[0004] To achieve the above objectives, the present application proposes a velocity vector sensing method, which includes:

[0005] Receive the pilot signal transmitted by the user terminal, and perform down-conversion and matched filtering on the pilot signal to obtain the target discrete signal sequence;

[0006] Performing a fast Fourier transform on the target discrete signal sequence, and determining a signal frequency sequence based on a discrete Fourier transform result;

[0007] determining a frequency growth rate and a Doppler shift according to the signal frequency sequence;

[0008] Obtaining signal propagation delay by performing inverse fast Fourier transform on the discrete Fourier transform result;

[0009] A velocity vector perception result of a user terminal is determined according to the signal propagation delay, the Doppler shift, and the frequency growth rate.

[0010] In one embodiment, the velocity vector sensing result includes the distance between the base station and the user terminal, the radial velocity of the user terminal, and the tangential velocity of the user terminal. The step of determining the velocity vector sensing result of the user terminal based on the signal propagation delay, the Doppler shift, and the frequency growth rate includes:

[0011] Determining the distance between the base station and the user terminal based on the signal propagation delay and the speed of light;

[0012] Determine a radial velocity of the user terminal according to the Doppler shift, the carrier frequency of the pilot signal, and the speed of light, where the radial velocity is a moving speed of the user terminal in the direction of the base station;

[0013] The tangential velocity of the user terminal is determined according to the frequency growth rate, the distance, the carrier frequency, and the speed of light, where the tangential velocity is the movement speed of the user terminal in a direction perpendicular to the base station.

[0014] In one embodiment, the step of performing a fast Fourier transform on the target discrete signal sequence and determining a signal frequency sequence based on a discrete Fourier transform result includes:

[0015] Select multiple sliding windows and perform fast Fourier transform on each subcarrier of the target discrete signal sequence in the first sliding window to obtain the signal frequency;

[0016] combining the signal frequencies to obtain a signal frequency sequence;

[0017] Determining a newly added time domain segment and an overlapping time domain segment of a next sliding window relative to a previous sliding window, and determining a newly added signal frequency sequence of the newly added time domain segment and an overlapping signal frequency sequence of the overlapping time domain segment;

[0018] The signal frequency sequence corresponding to the next sliding window is obtained by superimposing the signal frequency sequence with the newly added signal frequency sequence and deducting the overlapping signal frequency sequence, completing one iterative update, and repeating the iterative update process until the signal frequency sequences of all sliding windows are calculated;

[0019] After the calculation is completed, the signal frequency sequences corresponding to the multiple sliding windows are obtained.

[0020] In one embodiment, the step of determining the frequency growth rate and the Doppler shift according to the signal frequency sequence includes:

[0021] constructing an observation frequency vector by arranging the signal frequency of each subcarrier in the signal frequency sequence;

[0022] Construct a theoretical frequency vector based on the subcarrier index and carrier frequency interval of the pilot signal;

[0023] Subtracting the observed frequency vector from the theoretical frequency vector to extract the frequency offset caused by motion;

[0024] constructing a linear matrix based on the frequency offset;

[0025] Performing singular value decomposition on the linear matrix to obtain a solution value, and determining a pseudo-inverse value of the solution value;

[0026] A frequency increase rate and a Doppler shift are determined according to the frequency offset and the pseudo-inverse value.

[0027] In one embodiment, the step of obtaining the signal propagation delay by performing an inverse fast Fourier transform on the discrete Fourier transform result includes:

[0028] Performing an inverse fast Fourier transform on the discrete Fourier transform result to obtain an inverse discrete Fourier transform result;

[0029] The signal propagation delay is determined by obtaining the time index corresponding to the amplitude peak of the inverse discrete Fourier transform result.

[0030] In one embodiment, the step of receiving a pilot signal transmitted by a user terminal and performing down-conversion and matched filtering on the pilot signal to obtain a target discrete signal sequence includes:

[0031] Receive a pilot signal transmitted by a user terminal, and obtain a down-converted signal by converting the pilot signal into a low-frequency signal;

[0032] Performing matched filtering on the down-converted signal to obtain a filtered signal;

[0033] Sampling the filtered signal at integer multiples of a period of a symbol in the pilot signal to obtain a discrete signal sequence;

[0034] The discrete signal sequence is divided by the pilot symbol of the pilot signal to obtain a target discrete signal sequence.

[0035] In addition, to achieve the above-mentioned purpose, the present application also proposes a velocity vector sensing device, which includes:

[0036] The signal receiving and preprocessing module is used to receive the pilot signal transmitted by the user terminal, and perform down-conversion and matched filtering on the pilot signal to obtain the target discrete signal sequence;

[0037] a signal conversion module, configured to perform a fast Fourier transform on the target discrete signal sequence and determine a signal frequency sequence based on a discrete Fourier transform result;

[0038] a variation estimation module, configured to determine a frequency growth rate and a Doppler shift according to the signal frequency sequence;

[0039] A delay estimation module, configured to obtain a signal propagation delay by performing an inverse fast Fourier transform on the discrete Fourier transform result;

[0040] The parameter estimation module is used to determine the velocity vector perception result of the user terminal according to the signal propagation delay, the Doppler shift and the frequency growth rate.

[0041] In addition, to achieve the above-mentioned purpose, the present application also proposes a speed vector sensing device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the speed vector sensing method described above.

[0042] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the speed vector perception method described above are implemented.

[0043] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the speed vector perception method described above are implemented.

[0044] The technical solution proposed in this application receives the pilot signal transmitted by the user end, down-converts and matches the pilot signal to obtain a target discrete signal sequence, performs a fast Fourier transform on the target discrete signal sequence, and determines the signal frequency sequence based on the discrete Fourier transform result. The frequency growth rate and Doppler shift are determined according to the signal frequency sequence. The signal propagation delay is obtained by performing an inverse fast Fourier transform on the discrete Fourier transform result. The velocity vector perception result of the user end is determined based on the signal propagation delay, Doppler shift and frequency growth rate. The hardware complexity and power consumption are reduced by down-conversion and matched filtering, while the signal-to-noise ratio is improved. This solves the problems of high cost and low accuracy caused by high-frequency signal processing in traditional methods. By combining fast Fourier transform and inverse fast Fourier transform, the signal frequency sequence, signal propagation delay, frequency growth rate and Doppler shift are extracted to fully perceive the velocity vector (including distance, radial velocity and tangential velocity) of the user end, overcoming the limitation of the existing technology that can only perceive some parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0047] Figure 1 A schematic diagram of a flow chart of the first embodiment of the velocity vector sensing method of the present application;

[0048] Figure 2 A flow chart illustrating the second embodiment of the velocity vector sensing method of this application;

[0049] Figure 3 A flow chart illustrating the third embodiment of the velocity vector sensing method of this application;

[0050] Figure 4 This is a schematic diagram of the module structure of the speed vector sensing device according to an embodiment of the present application;

[0051] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the speed vector perception method in the embodiment of the present application.

[0052] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0053] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0054] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0055] Integrated communication and perception empowers wireless communication devices with sensing capabilities, enabling efficient sharing of hardware and spectrum resources. In many applications of integrated communication and perception, such as drone sensing in the low-altitude economy, it's necessary to perceive the user's velocity and position over time to model their trajectory and predict their next position. The perceived trajectory of a user is often complex. At a specific moment in time, their motion relative to the base station often involves both radial and tangential motion.

[0056] Existing velocity vector sensing technologies mostly focus on radial velocity estimation methods based on Doppler shift. Only a few studies have focused on sensing tangential motion, and all of these rely on large-scale digital receiving antenna arrays. Each element in a digital receiving antenna array requires an independent RF link, making it typically expensive and power-intensive. Therefore, how to perform comprehensive velocity vector sensing within limited hardware and power resources remains a pressing challenge for researchers in this field.

[0057] Therefore, in order to overcome the above-mentioned defects, the present application provides a solution that reduces hardware complexity and power consumption through down-conversion and matched filtering processing, while improving the signal-to-noise ratio, solving the problems of high cost and low precision caused by high-frequency signal processing in traditional methods. By combining fast Fourier transform and inverse fast Fourier transform, the signal frequency sequence, signal propagation delay, frequency growth rate and Doppler shift are extracted to fully perceive the velocity vector of the user end, overcoming the limitation of the existing technology that can only perceive some parameters.

[0058] It should be noted that the execution entity of each embodiment of the present application can be a computing service system with data processing, network communication, and program execution functions, such as an electronic system or a velocity vector sensing system capable of implementing the above functions. The following embodiments are described using the velocity vector sensing system as an example (hereinafter referred to as the "system").

[0059] Based on this, the embodiment of the present application provides a velocity vector sensing method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the velocity vector sensing method of the present application.

[0060] In this embodiment, the velocity vector sensing method includes steps S10 to S50:

[0061] Step S10: receiving a pilot signal transmitted by a user terminal, down-converting and matching filtering the pilot signal to obtain a target discrete signal sequence.

[0062] In wireless communication systems, user terminals (such as drones, vehicles, or other mobile devices) transmit a known, predefined signal pattern—a pilot signal. This signal serves as a reference signal to aid receivers (such as base stations) in signal processing and analysis. Pilot signals typically contain specific frequency, phase, and amplitude information, which is preserved during transmission. Even in complex channel environments, the receiver can use these known characteristics to recover and analyze the signal.

[0063] It should be understood that after receiving the pilot signal at the receiving end of the system, it first needs to be down-converted to convert the high-frequency wireless signal into a low-frequency signal. This process is achieved through a mixer, which mixes the received high-frequency signal with the signal generated by the local oscillator to produce a lower-frequency intermediate frequency signal. This signal retains all the information of the original signal, but in a frequency range more suitable for subsequent processing. The down-converted signal is then subjected to matched filtering. The core concept is to design a filter whose impulse response perfectly matches the shape of the received signal to maximize the signal's energy output while effectively suppressing the effects of noise and interference. The matched filter is designed based on the characteristics of the pilot signal, so it can accurately identify and extract the useful information in the pilot signal.

[0064] As an implementation manner, the above-mentioned step S10 in this embodiment may include: receiving a pilot signal transmitted by the user end, and obtaining a down-converted signal by converting the pilot signal into a low-frequency signal; performing matched filtering on the down-converted signal to obtain a filtered signal; sampling the filtered signal with an integer multiple of the period of the symbol in the pilot signal to obtain a discrete signal sequence; dividing the discrete signal sequence by the pilot symbol of the pilot signal to obtain a target discrete signal sequence.

[0065] In practice, since pilot signals are typically transmitted on high-frequency carriers, the receiver must downconvert the signal, converting it to a low-frequency signal. The downconverted signal is then matched filtered to generate a continuous analog signal (i.e., the filtered signal).

[0066] For further processing, it needs to be converted into a discrete signal. This is achieved through sampling, which discretizes the signal in time according to a certain sampling frequency. In this step, the sampling frequency is an integer multiple of the symbol period in the pilot signal, ensuring that each symbol is fully sampled, resulting in a discrete digital signal sequence.

[0067] Finally, the sampled discrete signal sequence is divided by the pilot symbol of the pilot signal to obtain the target discrete signal sequence. Pilot symbols are known symbols in the pilot signal and serve as references in signal processing. By dividing the discrete signal sequence by the pilot symbol, the influence of the pilot symbol is eliminated, resulting in a target discrete signal sequence containing only the user end's transmitted signal information.

[0068] Step S20: Perform fast Fourier transform on the target discrete signal sequence, and determine the signal frequency sequence based on the discrete Fourier transform result.

[0069] It's important to note that the Fast Fourier Transform (FFT) is used to convert signals from the time domain to the frequency domain. While the time domain describes how a signal changes over time, the frequency domain describes the distribution of different frequency components within the signal. The core of the FFT is to exploit the periodicity and symmetry of signals to decompose a complex computational problem into multiple simpler subproblems, thereby improving computational efficiency.

[0070] In this step, FFT processing is performed on the target discrete signal sequence. The input of FFT is a discrete signal sequence, and the output is a complex sequence (i.e., a signal frequency sequence), which reflects the spectral characteristics of the signal in the frequency domain. In order to obtain a signal frequency sequence (i.e., the amplitude distribution of the signal at different frequencies), the FFT output results need to be further processed. Specifically, the modulus of each frequency component in the complex sequence is first extracted. The signal modulus represents the amplitude of the signal at a specific frequency. It is a non-negative scalar value used to describe the intensity or energy of the signal at that frequency. Finally, according to the modulus of each frequency component, a signal frequency sequence can be constructed. This sequence intuitively reflects the amplitude characteristics of the signal at different frequencies.

[0071] Step S30: determining a frequency growth rate and a Doppler shift according to the signal frequency sequence.

[0072] It's important to note that in wireless communications, changes in signal frequency are often related to the relative motion between the signal source and receiver. The Doppler shift is the change in signal frequency caused by relative motion, reflecting the relative velocity between the source and receiver. The frequency growth rate describes the rate of change of the signal frequency over time, which may be related to the signal modulation method, channel characteristics, or other dynamic changes.

[0073] In this step, the frequency growth rate and Doppler shift can be determined by analyzing the signal frequency sequence. The frequency growth rate is determined by calculating the rate of change of adjacent frequency values ​​in the signal frequency sequence. Specifically, each frequency value in the signal frequency sequence is compared with its previous frequency value, and the difference between them is calculated. Then, these differences are divided by the corresponding time interval to obtain the frequency growth rate; the Doppler shift is determined by analyzing the frequency offset in the signal frequency sequence. The size of the Doppler shift is proportional to the relative speed between the signal source and the receiving end. In practical applications, the frequency values ​​in the signal frequency sequence are affected by the Doppler effect, causing the frequency to change. By comparing the frequency values ​​in the signal frequency sequence with the theoretical frequency value (i.e., the frequency value when there is no relative motion), the Doppler shift can be extracted. The calculation of the Doppler shift usually requires combining the carrier frequency of the signal and the relative speed relationship between the signal source and the receiving end.

[0074] Step S40: Obtain signal propagation delay by performing inverse fast Fourier transform on the discrete Fourier transform result.

[0075] It should be understood that the Inverse Fast Fourier Transform (IFFT) is the inverse process of FFT, which is used to convert the frequency domain signal back to the time domain signal. The specific process of IFFT processing is to convert each frequency component of the frequency domain signal into the corresponding time component of the time domain signal. Through IFFT, the representation of the signal in the time domain can be obtained, which includes the amplitude and phase information of the signal. In the time domain signal, the signal propagation delay can be determined by analyzing the amplitude peak of the signal. Specifically, the signal will experience a certain delay during the propagation process. This delay will cause the signal to shift on the time axis. By finding the amplitude peak of the time domain signal and recording its corresponding time index, the signal propagation delay can be determined.

[0076] Step S50: Determine a velocity vector perception result of the user terminal according to the signal propagation delay, the Doppler shift, and the frequency growth rate.

[0077] After the above signal processing and parameter acquisition steps, the corresponding algorithm can be used to obtain the velocity vector perception result of the user end.

[0078] This embodiment reduces hardware complexity and power consumption through down-conversion and matched filtering, while also improving the signal-to-noise ratio. This addresses the high cost and low precision issues inherent in high-frequency signal processing in traditional methods. By combining fast Fourier transforms and inverse fast Fourier transforms, the system extracts the signal frequency sequence, signal propagation delay, frequency growth rate, and Doppler shift, enabling comprehensive perception of the user's velocity vector, overcoming the limitations of existing technologies that only perceive partial parameters. Furthermore, down-conversion converts high-frequency signals into low-frequency signals, reducing signal processing complexity. Matched filtering improves the signal-to-noise ratio, suppresses noise interference, and ensures signal quality. Sampling at integer multiples of the symbol period avoids sampling bias and improves the accuracy of discrete signal sequences. Normalization (dividing by pilot symbols) eliminates the effects of channel fading.

[0079] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 , the step S50 may include steps S501 to S503:

[0080] Step S501: Determine the distance between the base station and the user terminal according to the signal propagation delay and the speed of light.

[0081] It should be understood that the velocity vector sensing result includes the distance between the base station and the user terminal, the radial velocity of the user terminal, and the tangential velocity of the user terminal. In wireless communication and positioning systems, the motion of the user terminal relative to the base station can be decomposed into two main velocity components: radial velocity and tangential velocity.

[0082] Radial velocity refers to the speed of a user terminal's movement in the direction of the base station, that is, the speed of the user terminal's movement directly toward or away from the base station. It is a direct reflection of the relative motion between the user terminal and the base station. For example, if the user terminal is moving toward the base station, its radial velocity is positive; if the user terminal is moving away from the base station, its radial velocity is negative. Specifically, when the user terminal moves toward the base station, the received signal frequency increases (blue shift); when the user terminal moves away from the base station, the received signal frequency decreases (red shift). By analyzing this frequency change, the radial velocity of the user terminal can be calculated.

[0083] Tangential velocity refers to the speed of a user terminal moving perpendicular to the base station, that is, the speed of the user terminal moving in a plane perpendicular to the line connecting the base station. It reflects the user terminal's movement in a circular path around the base station. For example, if the user terminal moves in a circular motion centered on the base station, its tangential velocity will remain constant, while its radial velocity will be zero. The magnitude of the tangential velocity can be determined by analyzing the rate of change of the signal frequency (i.e., the frequency growth rate) and the distance between the user terminal and the base station.

[0084] It's important to note that signal propagation delay refers to the time between a signal being transmitted from a user terminal and being received by a base station. Because signals propagate through air at speeds close to the speed of light, the distance between the user terminal and the base station can be calculated by measuring signal propagation delay.

[0085] In practice, the receiving end (base station) determines the signal propagation delay by detecting the signal's arrival time. This can be determined by analyzing the signal's peak amplitude or phase change. The signal propagation delay multiplied by the speed of light yields the distance between the user end and the base station. The speed of light is a known constant, approximately 300,000 kilometers per second. For example, if the signal propagation delay is 1 microsecond (one millionth of a second), the distance between the user end and the base station is 300 meters (1 microsecond x 300,000 kilometers per second ≈ 300 meters).

[0086] Step S502: Determine the radial velocity of the user terminal according to the Doppler shift, the carrier frequency of the pilot signal, and the speed of light, where the radial velocity is the moving speed of the user terminal in the direction of the base station.

[0087] In practice, radial velocity can be calculated using the formula: Radial velocity = Speed ​​of light × Doppler shift / Carrier frequency. Carrier frequency is the center frequency of the signal, and the speed of light is the speed at which the signal propagates. For example, if the Doppler shift is 100 Hz and the carrier frequency is 10 GHz, the radial velocity is 300,000 km / s × 100 Hz / 10 GHz, which is ≈ 3 m / s.

[0088] Step S503 : determining the tangential velocity of the user terminal according to the frequency growth rate, the distance, the carrier frequency, and the speed of light, where the tangential velocity is the movement speed of the user terminal in a direction perpendicular to the base station.

[0089] It should be understood that the calculation of tangential velocity requires a combination of the frequency growth rate, the distance between the user end and the base station, the carrier frequency, and the speed of light. The radial velocity can be calculated using the formula: Tangential velocity = √(2 × speed of light × frequency growth rate × distance / carrier frequency). For example, assuming a frequency growth rate of 0.1 Hz / s, the distance between the user end and the base station is 1000 meters, the carrier frequency is 10 GHz, and the speed of light is 300,000 km / s, then the tangential velocity = √(2 × 300,000 km / s × 0.1 Hz / s × 1000 meters / 10 GHz) ≈ 2.45 m / s.

[0090] This embodiment directly calculates the distance between the base station and the user end using signal propagation delay and the speed of light, avoiding complex Doppler compensation algorithms, simplifying the calculation process, and reducing hardware costs. Furthermore, the linear relationship between Doppler shift and carrier frequency is exploited to accurately calculate radial velocity. Combining the geometric relationship between frequency growth rate and distance, tangential velocity is derived, overcoming the limitation of traditional methods that only sense radial velocity.

[0091] As an implementation method, step S20 may include: selecting multiple sliding windows, performing fast Fourier transform on each subcarrier of the target discrete signal sequence in the first sliding window to obtain a signal frequency; combining the signal frequencies to obtain a signal frequency sequence; determining the newly added time domain segments and overlapping time domain segments of the next sliding window relative to the previous sliding window, and determining the newly added signal frequency sequence of the newly added time domain segments and the overlapping signal frequency sequence of the overlapping time domain segments; obtaining the signal frequency sequence corresponding to the next sliding window by superimposing the signal frequency sequence on the newly added signal frequency sequence and deducting the overlapping signal frequency sequence, completing an iterative update, and repeating the iterative update process until the signal frequency sequences of all sliding windows are calculated; after the calculation is completed, obtaining the signal frequency sequences corresponding to the multiple sliding windows.

[0092] It should be noted that in signal processing, sliding windows are a technique used to analyze the local characteristics of a signal. They divide a longer signal into multiple shorter segments, each of which is called a "window." The definition of a sliding window includes two key parameters: window length and sliding step size. The window length refers to the number of signal samples covered by each window and determines the time span of the signal within the window. Longer windows provide more stable frequency estimates but lower temporal resolution, while shorter windows provide higher temporal resolution but less stable frequency estimates. The sliding step size refers to the step size of the window's movement across the signal and determines the degree of overlap between windows. When the sliding step size is equal to the window length, there is no overlap between windows. This is called a non-repeating sliding window. Each window is completely independent, and the signal is divided into non-overlapping segments for analysis.

[0093] In this embodiment, a repetitive sliding window is used, where the sliding step size is smaller than the window length. This means that there is overlap between windows. The advantage of repetitive sliding windows is that they provide higher temporal resolution, as the overlap between windows can capture local changes in the signal. Furthermore, the overlap reduces signal distortion caused by window edge effects. This allows the system to more accurately analyze the dynamic characteristics of the signal, especially when the signal frequency changes rapidly.

[0094] In practice, the system selects multiple sliding windows, each covering a portion of the signal sequence. For the target discrete signal sequence within the first sliding window, the system performs an FFT on each subcarrier to obtain the subcarrier's signal frequency within the current sliding window. This frequency information reflects the signal's frequency characteristics within that time segment. After obtaining the signal frequencies within the first sliding window, the system combines these frequencies to form a signal frequency sequence.

[0095] Because sliding windows overlap, the system needs to determine the added and overlapping time domain segments of each new window relative to the previous window. For the added time domain segments, the system also performs FFT processing to obtain the added signal frequency sequence; for the overlapping time domain segments, the system calculates the overlapping signal frequency sequence. This processing method ensures signal continuity and integrity while avoiding information loss or duplicate calculations caused by window movement.

[0096] After processing the newly added and overlapping time-domain segments, the signal frequency sequence for the first sliding window is added to the newly added signal frequency sequence, and the overlapping signal frequency sequence is subtracted to obtain the signal frequency sequence for the next sliding window. This process is an iterative update, and the system repeats the above steps until all sliding windows have been processed. Ultimately, the system obtains signal frequency sequences corresponding to multiple sliding windows, which contain information about the frequency changes of the signal in different time segments.

[0097] This embodiment utilizes an iterative update mechanism for overlapping time-domain segments to reduce computational redundancy and lower hardware resource consumption. Compared to traditional methods that require full-time-domain processing or fixed-window analysis, this step achieves high-precision frequency tracking with lower computational complexity, resolving the resource-intensive nature of existing technologies that hinder deployment on low-cost equipment.

[0098] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the first embodiment can be referred to the above introduction and will not be described in detail later. Figure 3 , the step S30 may include steps S301 to S306:

[0099] Step S301 : constructing an observation frequency vector by arranging the signal frequency of each subcarrier in the signal frequency sequence.

[0100] In this step, the signal frequency of each subcarrier in the signal frequency sequence needs to be arranged to construct an observation frequency vector. The signal frequency sequence contains the signal frequency information measured at different times or different locations. By arranging these frequency values ​​in the order of subcarriers, a vector is formed, called an observation frequency vector, which reflects the actual frequency distribution of the received signal. For example, if there are multiple subcarriers in the signal frequency sequence, and each subcarrier has a corresponding frequency value, then arranging these frequency values ​​in sequence will result in an observation frequency vector. This vector contains various influences on the signal during actual transmission, such as the Doppler effect, channel fading, etc., and can therefore be used to analyze the dynamic changes of the signal.

[0101] Step S302: construct a theoretical frequency vector according to the subcarrier index and carrier frequency interval of the pilot signal.

[0102] The theoretical frequency vector is calculated based on the pilot signal's subcarrier index and carrier frequency spacing, which are predefined. The theoretical frequency vector reflects the frequency distribution of the signal under ideal conditions, without any interference or motion. By combining the pilot signal's subcarrier index and carrier frequency spacing, the ideal frequency value of each subcarrier can be calculated and arranged into a vector, the theoretical frequency vector.

[0103] Step S303: Subtract the observed frequency vector from the theoretical frequency vector to extract the frequency offset caused by the motion.

[0104] It's important to note that frequency offset refers to the difference between the actual received signal frequency and the theoretical frequency. By subtracting the observed frequency vector from the theoretical frequency vector, a difference vector is obtained, reflecting the actual change in signal frequency. This change is typically caused by relative motion between the signal source and receiver, known as the Doppler effect. The magnitude and direction of the frequency offset can provide important information about the motion of the signal source, such as its direction and speed.

[0105] Step S304: construct a linear matrix based on the frequency offset.

[0106] It should be understood that by arranging the frequency offsets in a matrix format, mathematical operations and analysis can be performed more conveniently. The construction of the linear matrix is ​​based on the distribution of the frequency offsets, which generally reflects the frequency variation pattern of the signal on different subcarriers.

[0107] Step S305 , performing singular value decomposition on the linear matrix to obtain a solution value, and determining a pseudo-inverse value of the solution value.

[0108] It should be noted that singular value decomposition is a matrix decomposition method. By performing singular value decomposition on a linear matrix, it can be decomposed into the product of three matrices. The decomposition result can be used to extract the main features of the matrix, such as principal component analysis and dimensionality reduction.

[0109] In this step, the solution to the linear matrix is ​​obtained through singular value decomposition. These solutions reflect the matrix's singular values ​​and corresponding eigenvectors. Furthermore, to more accurately estimate the dynamic changes of the signal, the pseudo-inverse of the solution is calculated to obtain a matrix for the inverse solution. Pseudo-inverse matrices are often used in signal processing to solve linear equations, especially when the matrix is ​​non-invertible.

[0110] Step S306: Determine a frequency growth rate and a Doppler shift according to the frequency offset and the pseudo-inverse value.

[0111] In practice, the frequency growth rate can be determined by analyzing the changing trend of the frequency offset. If the frequency offset gradually increases or decreases over time, the rate of change is the frequency growth rate. By combining the frequency offset with the pseudo-inverse value, the system can more accurately estimate this rate of change, thereby obtaining an estimated value for the frequency growth rate.

[0112] By analyzing the frequency offset and performing mathematical processing using the pseudo-inverse value, the system can accurately calculate the Doppler shift. This process involves further analysis of the frequency offset and using the pseudo-inverse value to solve any linear equations to obtain an estimate of the Doppler shift.

[0113] This embodiment eliminates carrier frequency deviation by subtracting the observed frequency vector from the theoretical frequency vector, retaining only the frequency offset caused by motion. This eliminates the need for complex channel estimation and significantly reduces hardware cost and computational complexity. It uses singular value decomposition to quickly solve the frequency growth rate and Doppler shift, avoiding the high computational overhead of traditional iterative algorithms while improving parameter estimation accuracy, enabling low-cost equipment to achieve high-precision velocity vector perception.

[0114] As an implementation manner, the step S40 may include: performing an inverse fast Fourier transform on the discrete Fourier transform result to obtain an inverse discrete Fourier transform result; and determining the signal propagation delay by obtaining a time index corresponding to an amplitude peak of the inverse discrete Fourier transform result.

[0115] It should be understood that the system first performs a discrete Fourier transform (Discret e The result of the inverse discrete Fourier transform (DFT) is processed by IFFT to obtain the inverse discrete Fourier transform result, which is a time domain signal that reflects the distribution of the signal on the time axis.

[0116] To determine signal propagation delay, the system analyzes the peak amplitudes of the IFFT results. The peak amplitude of a time-domain signal corresponds to the moment when the signal energy is highest, which is typically closely related to the signal's arrival time. By finding the time index corresponding to the peak amplitude, the system can accurately determine the signal's propagation delay. Specifically, the time index refers to the signal's exact location in the time domain, measured in sampling intervals. When the system detects an amplitude peak in the IFFT results, it records the time index at which the peak occurred, representing the time elapsed between the signal's transmission and reception. In this way, the system can accurately measure signal propagation delay.

[0117] This embodiment uses amplitude peak detection to extract signal propagation delay, thereby avoiding the problem that traditional delay estimation algorithms require high sampling rates or complex calculations, and significantly reducing hardware costs and power consumption.

[0118] For ease of understanding, the velocity vector sensing method of the present application is described with reference to the following example, but is not intended to limit the velocity vector sensing method of the present application. The specific steps of the velocity vector sensing method of the present application are as follows:

[0119] 1. The user terminal transmits a pilot signal. Specifically, the transmitted signal is:

[0120]

[0121] Among them, f c is the carrier frequency, N is the number of symbols, M is the number of subcarriers, {S n，m} is the pilot symbol, T is the symbol period, F = 1 / T is the carrier frequency interval, g(t) is the width T and height Rectangular pulse function.

[0122] 2. The received signal of the communication-sensing integrated base station is sampled and discretized at integer multiples of the symbol period after down-conversion and matched filtering. Specifically, let r(t) represent the received signal after down-conversion; the matched filtering method is:

[0123]

[0124] Sampling to obtain discrete signal sequence u n，m =u(nT,mF), n=0,1,...,N-1,m=0,1,...,M-1; calculate x n，m =u n，m / S n，m .

[0125] 3. Select the sliding window length and step size to obtain a set of sliding windows. Specifically, let the odd number U = 2Q + 1 represent the sliding window size, S represents the sliding window moving step size, then the subscript set of the lth sliding window is common A sliding window.

[0126] 4. Calculate the fast Fourier transform of the signal in each sliding window and determine the signal frequency in the sliding window to obtain a set of signal frequency sequences. Specifically, for each m=0, 1, ..., M-1, use the fast Fourier transform to calculate the sequence The DFT of

[0127] For each m=0,1,…,M-1,if the sequence The DFT of has been calculated and recorded as Then iteratively calculate the next set of sliding window sequences The DFT of is:

[0128]

[0129] Combined with the calculated Iterative calculation For each m=0, 1, ..., M-1, the signal frequency of the lth sliding window is estimated to be:

[0130]

[0131] 5. Estimate the frequency growth rate and Doppler shift. Specifically, construct the observation frequency vector Its i-th element is in Constructing a linear matrix Its i-th behavior Constructing theoretical frequency vectors Its i-th element is Calculate the singular value decomposition of A as A=U∑V T ; The estimated Doppler shift and frequency growth rate are

[0132] 6. Estimate the signal propagation delay. Specifically, for each n=0, 1, ..., N-1, use the inverse fast Fourier transform to calculate the sequence The inverse discrete Fourier transform of The estimated signal propagation delay is

[0133] 7. Estimate the distance between the base station and the user terminal, the radial velocity of the user terminal, and the tangential velocity of the user terminal. Specifically, the estimated distance between the base station and the user terminal is where c is the speed of light; the estimated radial velocity is The estimated tangential velocity is

[0134] It should be noted that the velocity vector sensing method of the present application only requires the base station to be equipped with a single antenna to complete the estimation of the user terminal velocity vector.

[0135] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the velocity vector sensing method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0136] This application also provides a velocity vector sensing device, please refer to Figure 4 , the velocity vector sensing device includes:

[0137] The signal receiving and preprocessing module 10 is used to receive the pilot signal transmitted by the user terminal, and perform down-conversion and matched filtering on the pilot signal to obtain the target discrete signal sequence;

[0138] The signal conversion module 20 is used to perform a fast Fourier transform on the target discrete signal sequence and determine a signal frequency sequence based on the discrete Fourier transform result;

[0139] a variation estimation module 30, configured to determine a frequency growth rate and a Doppler shift according to the signal frequency sequence;

[0140] The delay estimation module 40 is configured to obtain the signal propagation delay by performing an inverse fast Fourier transform on the discrete Fourier transform result;

[0141] The parameter estimation module 50 is configured to determine a velocity vector perception result of the user terminal according to the signal propagation delay, the Doppler shift, and the frequency growth rate.

[0142] The velocity vector sensing device provided in this application utilizes the velocity vector sensing method in the aforementioned embodiments, resolving the technical issues of existing velocity vector sensing methods, such as incomplete sensing and high costs. Compared to the prior art, the velocity vector sensing device provided in this application offers the same beneficial effects as the velocity vector sensing method provided in the aforementioned embodiments. Other technical features of the velocity vector sensing device are the same as those disclosed in the aforementioned embodiments and are not further elaborated here.

[0143] The present application provides a speed vector sensing device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the speed vector sensing method of the above-mentioned embodiment 1.

[0144] Reference below Figure 5 , which shows a schematic diagram of the structure of a velocity vector sensing device suitable for implementing the embodiments of the present application. The velocity vector sensing device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Devices), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The speed vector sensing device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0145] like Figure 5 As shown, the velocity vector sensing device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or programs loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the velocity vector sensing device. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and a communication device 1009. Communication device 1009 can allow the velocity vector sensing device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows a velocity vector sensing device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have alternatively.

[0146] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0147] The velocity vector sensing device provided in this application utilizes the velocity vector sensing method in the aforementioned embodiment, resolving the technical issues of existing velocity vector sensing methods, such as incomplete perception and high costs. Compared to the prior art, the velocity vector sensing device provided in this application offers the same beneficial effects as the velocity vector sensing method provided in the aforementioned embodiment. Other technical features of this velocity vector sensing device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0148] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0149] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0150] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer program) stored thereon, wherein the computer-readable program instructions are used to execute the velocity vector sensing method in the above embodiment.

[0151] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0152] The computer-readable storage medium may be included in the velocity vector sensing device; or may exist independently without being assembled into the velocity vector sensing device.

[0153] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the velocity vector sensing device, the velocity vector sensing device: receives the pilot signal transmitted by the user end, and down-converts and matches the pilot signal to obtain a target discrete signal sequence, performs fast Fourier transform on the target discrete signal sequence, and determines the signal frequency sequence based on the discrete Fourier transform result, determines the frequency growth rate and Doppler shift according to the signal frequency sequence, obtains the signal propagation delay by performing an inverse fast Fourier transform on the discrete Fourier transform result, and determines the velocity vector sensing result of the user end according to the signal propagation delay, Doppler shift and frequency growth rate.

[0154] The computer program code for performing the operations of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, entirely on a remote computer or server, or on an ARM (Advanced RISC Machines) development board. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, through the Internet using an Internet service provider).

[0155] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0156] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0157] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned velocity vector sensing method. This computer-readable storage medium addresses the technical issues of existing velocity vector sensing methods, which suffer from incomplete perception and high costs. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the velocity vector sensing method provided in the aforementioned embodiments and are not further elaborated here.

[0158] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned speed vector perception method when executed by a processor.

[0159] The computer program product provided in this application can address the technical issues of existing velocity vector sensing methods, which suffer from incomplete perception and high costs. Compared to existing technologies, the beneficial effects of the computer program product provided in this application are the same as those of the velocity vector sensing methods provided in the aforementioned embodiments, and are not further elaborated here.

[0160] The above descriptions are only some embodiments of the present application and do not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A velocity vector sensing method, characterized in that: The method comprises the following steps: Receive the pilot signal transmitted by the user terminal, and perform down-conversion and matched filtering on the pilot signal to obtain the target discrete signal sequence; Performing a fast Fourier transform on the target discrete signal sequence, and determining a signal frequency sequence based on a discrete Fourier transform result; determining a frequency growth rate and a Doppler shift according to the signal frequency sequence; Obtaining signal propagation delay by performing inverse fast Fourier transform on the discrete Fourier transform result; A velocity vector perception result of a user terminal is determined according to the signal propagation delay, the Doppler shift, and the frequency growth rate.

2. The velocity vector sensing method according to claim 1, wherein: The velocity vector sensing result includes the distance between the base station and the user terminal, the radial velocity of the user terminal, and the tangential velocity of the user terminal. The step of determining the velocity vector sensing result of the user terminal based on the signal propagation delay, the Doppler shift, and the frequency growth rate includes: Determining the distance between the base station and the user terminal based on the signal propagation delay and the speed of light; Determine a radial velocity of the user terminal according to the Doppler shift, the carrier frequency of the pilot signal, and the speed of light, where the radial velocity is a moving speed of the user terminal in the direction of the base station; The tangential velocity of the user terminal is determined according to the frequency growth rate, the distance, the carrier frequency, and the speed of light, where the tangential velocity is the movement speed of the user terminal in a direction perpendicular to the base station.

3. The velocity vector sensing method according to claim 1, wherein: The step of performing fast Fourier transform on the target discrete signal sequence and determining the signal frequency sequence based on the discrete Fourier transform result includes: Select multiple sliding windows and perform fast Fourier transform on each subcarrier of the target discrete signal sequence in the first sliding window to obtain the signal frequency; combining the signal frequencies to obtain a signal frequency sequence; Determining a newly added time domain segment and an overlapping time domain segment of a next sliding window relative to a previous sliding window, and determining a newly added signal frequency sequence of the newly added time domain segment and an overlapping signal frequency sequence of the overlapping time domain segment; The signal frequency sequence corresponding to the next sliding window is obtained by superimposing the signal frequency sequence with the newly added signal frequency sequence and deducting the overlapping signal frequency sequence, completing one iterative update, and repeating the iterative update process until the signal frequency sequences of all sliding windows are calculated; After the calculation is completed, the signal frequency sequences corresponding to the multiple sliding windows are obtained.

4. The velocity vector sensing method according to any one of claims 1 to 3, characterized in that: The step of determining the frequency growth rate and the Doppler shift according to the signal frequency sequence comprises: constructing an observation frequency vector by arranging the signal frequency of each subcarrier in the signal frequency sequence; Construct a theoretical frequency vector based on the subcarrier index and carrier frequency interval of the pilot signal; Subtracting the observed frequency vector from the theoretical frequency vector to extract the frequency offset caused by motion; constructing a linear matrix based on the frequency offset; Performing singular value decomposition on the linear matrix to obtain a solution value, and determining a pseudo-inverse value of the solution value; A frequency increase rate and a Doppler shift are determined according to the frequency offset and the pseudo-inverse value.

5. The velocity vector sensing method according to any one of claims 1 to 3, characterized in that: The step of obtaining the signal propagation delay by performing an inverse fast Fourier transform on the discrete Fourier transform result includes: Performing an inverse fast Fourier transform on the discrete Fourier transform result to obtain an inverse discrete Fourier transform result; The signal propagation delay is determined by obtaining the time index corresponding to the amplitude peak of the inverse discrete Fourier transform result.

6. The velocity vector sensing method according to any one of claims 1 to 3, characterized in that: The step of receiving a pilot signal transmitted by a user terminal, performing down-conversion and matched filtering on the pilot signal to obtain a target discrete signal sequence includes: Receive a pilot signal transmitted by a user terminal, and obtain a down-converted signal by converting the pilot signal into a low-frequency signal; Performing matched filtering on the down-converted signal to obtain a filtered signal; Sampling the filtered signal at integer multiples of a period of a symbol in the pilot signal to obtain a discrete signal sequence; The discrete signal sequence is divided by the pilot symbol of the pilot signal to obtain a target discrete signal sequence.

7. A velocity vector sensing device, characterized in that: The speed vector sensing device comprises: The signal receiving and preprocessing module is used to receive the pilot signal transmitted by the user terminal, and perform down-conversion and matched filtering on the pilot signal to obtain the target discrete signal sequence; a signal conversion module, configured to perform a fast Fourier transform on the target discrete signal sequence and determine a signal frequency sequence based on a discrete Fourier transform result; a variation estimation module, configured to determine a frequency growth rate and a Doppler shift according to the signal frequency sequence; A delay estimation module, configured to obtain a signal propagation delay by performing an inverse fast Fourier transform on the discrete Fourier transform result; The parameter estimation module is used to determine the velocity vector perception result of the user terminal according to the signal propagation delay, the Doppler shift and the frequency growth rate.

8. A velocity vector sensing device, characterized in that: The speed vector sensing device includes: a memory, a processor, and a speed vector sensing program stored in the memory and executable on the processor. When the speed vector sensing program is executed by the processor, the speed vector sensing method according to any one of claims 1 to 6 is implemented.

9. A storage medium, characterized in that: The storage medium stores a speed vector perception program, which, when executed by a processor, implements the speed vector perception method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the velocity vector perception method according to any one of claims 1 to 6 are implemented.

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