Spectrum synthesis-based point cloud tracking method, device and equipment and storage medium
By using a point cloud tracking method based on spectral synthesis, the sampling time and location range of echo information are obtained. The point cloud information is optimized by using Fourier transform and spectral synthesis algorithms, which solves the tracking error caused by sensor performance limitations and system mutations, and achieves high-precision target tracking.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENZHEN CHENGGU TECH CO LTD
- Filing Date
- 2022-12-20
- Publication Date
- 2026-06-23
AI Technical Summary
Existing technologies for tracking moving targets suffer from poor processing of dense echo information due to sensor performance limitations and sudden changes in system state, resulting in large target tracking errors.
By using a point cloud tracking method based on spectral synthesis, the sampling time and location interval of the echo information are obtained, the probability density distribution is determined, and the point cloud information is optimized by filtering using Fourier transform and spectral synthesis algorithms.
It improves the tracking accuracy of targets, solves the iterative divergence problem caused by system mutations, and realizes continuous tracking of dense echo point cloud information.
Smart Images
Figure CN116125414B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of target detection technology, and in particular to a point cloud tracking method, apparatus, device and storage medium based on spectral synthesis. Background Technology
[0002] Currently, the process of actively detecting moving targets using sensors requires acquiring independent observation features for each measurement point, such as position, velocity, and signal-to-noise ratio. However, in practical applications, due to limitations in sensor performance, the number of available observation features is limited. Furthermore, during data processing, if the system state changes abruptly, it is difficult to adapt to these changes, resulting in poor processing of large amounts of dense echo information and consequently, significant tracking errors. Summary of the Invention
[0003] In view of this, this application provides a point cloud tracking method, apparatus, device and storage medium based on spectral synthesis, which solves the iterative divergence problem caused by the inability of time-domain filters to handle system errors. It can continuously track targets under a large amount of dense echo point cloud information, so as to solve the target tracking error caused by system mutation and improve the tracking accuracy of targets.
[0004] Firstly, this application provides a point cloud tracking method based on spectral synthesis. The method includes: continuously acquiring echo information of a target to be tracked within a preset time period; processing the continuously acquired echo information to obtain a set of echo point cloud information; acquiring the sampling time and sampling location interval of each echo point cloud information; determining the probability density distribution of the echo point cloud information at each sampling time based on the sampling time and sampling location interval; determining the variation law of the echo point cloud information with the sampling frequency according to the probability density distribution; and filtering the echo point cloud information acquired at any time based on a spectral synthesis algorithm according to the variation law of the echo point cloud information with the sampling frequency to obtain optimized point cloud information.
[0005] In one embodiment, multiple discrete grids are obtained by dividing each sampling location interval based on a preset measurement dimension; the probability of each echo point cloud information falling into each discrete grid at each sampling time is calculated; the probability density distribution of the echo point cloud information at each sampling time is determined based on the probability of each echo point cloud information falling into each discrete grid at each sampling time; a Fourier transform is further performed on the probability density distribution to obtain the spectral function of the probability density distribution; the power spectrum corresponding to the probability density distribution is determined based on the spectral function, and the power spectrum represents the variation law of the echo point cloud information with the sampling frequency.
[0006] In one embodiment, the attenuation information of the echo point cloud information during the transmission process is determined by the variation law of the echo point cloud information with the sampling frequency; the transient transformation law of the echo point cloud information is determined by the attenuation information of the echo point cloud information during the transmission process; and the echo point cloud information acquired at any time is filtered based on the transient transformation law of the echo point cloud information to obtain optimized point cloud information.
[0007] In one embodiment, a steady-state relation and attenuation equation for the power spectrum are constructed based on the echo point cloud information. The attenuation equation is solved based on the steady-state relation to obtain the attenuation information of the echo point cloud information during transmission. The attenuation of the power spectrum with sampling time is further determined based on the attenuation information. When the power spectrum attenuates to a steady state with sampling time, the power spectrum corresponding to the steady state is taken as the transient transformation law. The power spectrum corresponding to the steady state is further subjected to an inverse Fourier transform to obtain the transient probability density distribution of the echo point cloud information. Based on the transient probability density distribution of the echo point cloud information, the echo point cloud information acquired at any time is filtered to obtain optimized point cloud information.
[0008] Secondly, embodiments of this application provide a point cloud tracking device based on spectral synthesis. The device includes: a first processing module, configured to continuously acquire echo information of a target to be tracked within a preset time period, and process the continuously acquired echo information to obtain a set of echo point cloud information; a first determining module, configured to acquire the sampling time and sampling position interval of each echo point cloud information, and determine the probability density distribution of the echo point cloud information at each sampling time based on the sampling time and sampling position interval of each echo point cloud information; a second determining module, configured to determine the variation law of the echo point cloud information with the sampling frequency based on the probability density distribution of the echo point cloud information; and a second processing module, configured to perform filtering processing on the echo point cloud information acquired at any time based on a spectral synthesis algorithm, according to the variation law of the echo point cloud information with the sampling frequency, to obtain optimized point cloud information.
[0009] Thirdly, this application provides an electronic device, comprising: a memory for storing a point cloud tracking program based on spectral synthesis; and a processor for implementing the steps of the point cloud tracking method based on spectral synthesis as described in the first aspect when executing the point cloud tracking program based on spectral synthesis.
[0010] Fourthly, this application provides a computer-readable storage medium storing a computer program product that, when run on an electronic device, causes the electronic device to perform the steps of the point cloud tracking method based on spectral synthesis described in the first aspect.
[0011] The point cloud tracking method based on spectral synthesis provided in the first aspect of this application combines the sampling time and sampling location interval of echo point cloud information to determine the probability density distribution of echo point cloud information. This effectively simulates the probability of echo point cloud information being spatially dispersed. Furthermore, based on the probability density distribution, the variation law of echo point cloud information with sampling frequency is determined, enabling filtering processing based on spectral synthesis algorithm in the frequency domain. This solves the iterative divergence problem caused by the inability of time-domain filters to handle system errors. It can continuously track targets under a large amount of dense echo point cloud information, thereby solving the target tracking error caused by system mutations and improving the tracking accuracy of targets.
[0012] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description
[0013] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0014] Figure 1 A flowchart illustrating the point cloud tracking method based on spectral synthesis provided in the application embodiments;
[0015] Figure 2 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application;
[0016] Figure 3 A schematic diagram of the discrete mesh structure provided in the embodiments of this application;
[0017] Figure 4 This is a schematic diagram of a point cloud tracking device based on spectral synthesis provided in an embodiment of this application. Detailed Implementation
[0018] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail. It should be understood that in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.
[0019] It should also be understood that references to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include the specific features, structures, or characteristics described in connection with that embodiment. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings, and not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Moreover, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.
[0020] Please see Figure 1 , Figure 1 This is a flowchart illustrating the point cloud tracking method based on spectral synthesis provided in the embodiments of the application. It should be noted that, in this application, the point cloud tracking method based on spectral synthesis can be applied to electronic devices, including but not limited to various positioning devices, navigation devices, or self-moving devices containing data acquisition modules. The data acquisition module includes sensors or radar modules, etc.
[0021] For example, such as Figure 2 , Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 200 includes a data acquisition module 201, a memory 202, and a processor 203. The data acquisition module 201 is used to acquire echo information of a target to be tracked. The memory 202 stores a point cloud tracking program based on spectral synthesis. The processor 203 executes the point cloud tracking program based on spectral synthesis stored in the memory 202, processes the echo information acquired by the data acquisition module 201 accordingly to obtain optimized point cloud information, and further processes the optimized point cloud information by executing a target tracking program to complete target tracking. The data acquisition module 201 includes, but is not limited to, various sensors, radar modules, etc.
[0022] Depend on Figure 1 As can be seen, the point cloud tracking method based on spectral synthesis provided in this application includes steps S101 to S104. Details are as follows:
[0023] S101: Continuously acquire echo information of the target to be tracked within a preset time period, process the echo information, and obtain a set of echo point cloud information.
[0024] The echo information of the target to be tracked is obtained by continuously acquiring the echo information reflected by the target from sensors such as radar in real time. After intermediate frequency signal processing and threshold detection, the echo information can be used to obtain echo point cloud information. The echo point cloud information at each sampling time within a preset time period constitutes the set of echo point cloud information. The echo point cloud information at each sampling time includes information such as the position and velocity of the echo reflected by the target to be tracked. The echo point cloud information at each sampling time can form an n-dimensional state vector, where n is the observation dimension, composed of preset observation information such as distance, angle, velocity, signal-to-noise ratio, etc. Let the set of echo point cloud information be denoted as . in, This represents the observation dimension vector, where m is the number of samples and k0 is the initial number of samples. It should be noted that the information to be observed contained in the observation dimension vector varies depending on the type of sensor. For example, for a 2D radar sensor, the corresponding observation dimension vector includes information such as distance, angle, velocity, signal-to-noise ratio, and noise floor; for a 3D radar sensor, the corresponding observation dimension vector includes information such as horizontal and vertical angles.
[0025] S102: Obtain the sampling time and sampling location interval of each echo point cloud information, and determine the probability density distribution of the echo point cloud information at each sampling time based on the sampling time and sampling location interval.
[0026] In one embodiment, determining the probability density distribution of echo point cloud information at each sampling time based on the sampling time and sampling location interval includes: dividing each sampling location interval into multiple discrete grids based on a preset measurement dimension; calculating the probability of each echo point cloud information falling into each discrete grid at each sampling time; and determining the probability density distribution of the echo point cloud information at each sampling time based on the probability of each echo point cloud information falling into each discrete grid at each sampling time. For example, as... Figure 3 , Figure 3 This is a schematic diagram of the structure of a discrete mesh provided in an embodiment of this application.
[0027] It should be noted that the set is obtained through continuous observation. That is, k = k0, k0+1, ..., k0+N, representing each sampling time, and N is the total number of samples. Specifically, the sampling time and the total number of samples are preset according to actual needs. Based on the observation set... The probability density distribution of echo point cloud information at each sampling time is obtained within the fused sampling time and sampling location interval, denoted as . Where f(·) represents the observation dimension vector at the k-th sampling time. The corresponding probability density can be determined using the discrete grid statistical method, which involves dividing the observation coordinate system into discrete grids according to the observation dimension vector, generating a common probability density. A discrete grid; where G(i) represents the number of nodes selected on each observation dimension vector. Then, the probability of the sample data falling on each discrete grid point is calculated, i.e., the local probability density is calculated. Finally, the probability density distribution can be expressed as:
[0028] S103: Determine the variation law of echo point cloud information with sampling frequency based on probability density distribution.
[0029] By performing stochastic stationary processing on the probability density distribution, such as Fourier transform, the corresponding N-dimensional spectral function and amplitude phase can be obtained, thus yielding the propagation quantity and wavenumber vector of the probability wave. Furthermore, the power spectrum of the echo point cloud information is determined based on the N-dimensional spectral function. Specifically, the power spectrum is the medium for modeling and analyzing the echo point cloud information acquired by the sensor in the frequency domain, used to describe the variation of probability density power with frequency.
[0030] In one embodiment, determining the variation law of echo point cloud information with sampling frequency based on probability density distribution includes: performing a Fourier transform on the probability density distribution to obtain the spectral function of the probability density distribution; determining the power spectrum corresponding to the probability density distribution based on the spectral function, and using the power spectrum to represent the variation law of echo point cloud information with sampling frequency.
[0031] S104: Based on the variation law of echo point cloud information with sampling frequency, the echo point cloud information acquired at any time is filtered based on the spectral synthesis algorithm to obtain optimized point cloud information.
[0032] Specifically, a second-order tensor of the probability wave is calculated based on the set of echo point cloud information. This second-order tensor represents the attenuation coefficient of the power spectrum observation statistics for any probability wave propagation in any gauge vector space. It should be noted that under specific particle motion patterns, the steady-state relation of the power spectrum can be determined based on the set of echo point cloud information. Furthermore, based on the steady-state relation of the power spectrum, the coefficient tensor in the second-order tensor can be solved to determine the rate of change tensor of the probability density distribution. The rate of change tensor of the probability density distribution describes the propagation and attenuation process of the power spectrum. Specifically, the attenuation equation of the power spectrum can be established using the tensor, and the propagation information corresponding to the echo point cloud information during the propagation process can be obtained by solving for the attenuation variance of the power spectrum. Specifically, the specific particle motion refers to periodic motion in the application, but the actual motion of the target being detected is not periodic. However, in a short time and over a large area, the number of observation points can be considered to be periodically changing. The steady-state relation can be filtered and smoothed in various directions to reduce the impact of abnormal statistical biases.
[0033] In one embodiment, based on the variation law of echo point cloud information with sampling frequency, the echo point cloud information acquired at any time is filtered using a spectral synthesis algorithm to obtain optimized point cloud information. This includes: determining the attenuation information of the echo point cloud information during transmission based on the variation law of echo point cloud information with sampling frequency; determining the transient transformation law of the echo point cloud information based on the attenuation information; and filtering the echo point cloud information acquired at any time based on the transient transformation law of the echo point cloud information to obtain optimized point cloud information.
[0034] Specifically, based on the variation law of echo point cloud information with sampling frequency, the attenuation information corresponding to the echo point cloud information during the transmission process is determined, including: constructing a steady-state relation of the power spectrum and an attenuation equation based on the echo point cloud information; solving the attenuation equation based on the steady-state relation of the power spectrum to obtain the attenuation information corresponding to the echo point cloud information during the transmission process.
[0035] For example, determining the transient transformation law of echo point cloud information based on attenuation information includes: determining the attenuation of the power spectrum with sampling time based on the attenuation information of echo point cloud information; and determining that when the power spectrum attenuates to a stable state with sampling time, the power spectrum corresponding to the stable state is taken as the transient transformation law.
[0036] Specifically, based on the transient transformation law, the echo point cloud information acquired at any time is filtered to obtain optimized point cloud information, including: performing an inverse Fourier transform on the power spectrum corresponding to the steady state to obtain the transient probability density distribution of the echo point cloud information; and based on the transient probability density distribution of the echo point cloud information, the echo point cloud information acquired at any time is filtered to obtain optimized point cloud information.
[0037] As can be seen from the above analysis, the point cloud tracking method based on spectral synthesis provided in this application combines the sampling time and sampling location interval of echo point cloud information to determine the probability density distribution of echo point cloud information. This can effectively simulate the probability of echo point cloud information being spatially dispersed. Furthermore, based on the probability density distribution, the variation law of echo point cloud information with sampling frequency is determined, enabling filtering processing based on spectral synthesis algorithm in the frequency domain. This solves the iterative divergence problem caused by the inability of time-domain filters to handle system errors, and enables continuous tracking of targets under a large amount of dense echo point cloud information, thereby improving the tracking accuracy of targets.
[0038] Based on the point cloud tracking method based on spectral synthesis provided in the above embodiments, the present invention further provides an apparatus embodiment for implementing the above method embodiments.
[0039] like Figure 4 As shown, Figure 4This is a schematic diagram of a point cloud tracking device based on spectral synthesis provided in an embodiment of this application. The included modules are used to perform... Figure 1 The steps in the corresponding embodiments. Please refer to the details. Figure 1 The relevant descriptions in the corresponding embodiments are shown below. For ease of explanation, only the parts relevant to this embodiment are shown. See also... Figure 4 The point cloud tracking device 400 based on spectral synthesis includes:
[0040] The first processing module 401 is used to continuously acquire echo information of the target to be tracked within a preset time period, process the echo information, and obtain a set of echo point cloud information.
[0041] The first determining module 402 is used to obtain the sampling time and sampling location interval of each echo point cloud information, and to determine the probability density distribution of the echo point cloud information at each sampling time based on the sampling time and the sampling location interval.
[0042] The second determining module 403 is used to determine the variation law of echo point cloud information with sampling frequency based on probability density distribution;
[0043] The second processing module 404 is used to perform filtering processing on the echo point cloud information acquired at any time based on the spectral synthesis algorithm according to the variation law of echo point cloud information with sampling frequency, so as to obtain optimized point cloud information.
[0044] In one embodiment, the first determining module 402 includes:
[0045] The partitioning unit is used to divide each sampling location interval based on a preset measurement dimension to obtain multiple discrete grids;
[0046] The statistical unit is used to calculate the probability of each echo point cloud information falling into each discrete grid at each sampling time.
[0047] The first determining unit is used to determine the probability density distribution of echo point cloud information at each sampling time based on the probability that each echo point cloud information falls into each discrete grid at each sampling time.
[0048] In one embodiment, the second determining module 403 includes:
[0049] The transformation unit allows the user to perform a Fourier transform on the probability density distribution to obtain the spectral function of the probability density distribution.
[0050] The second determining unit is used to determine the power spectrum corresponding to the probability density distribution based on the spectral function, and to represent the change law of the echo point cloud information with the sampling frequency using the power spectrum.
[0051] In one embodiment, the second processing module 404 includes:
[0052] The third determining unit is used to determine the attenuation information of the echo point cloud information during the transmission process based on the variation law of the echo point cloud information with the sampling frequency.
[0053] The fourth determining unit is used to determine the transient transformation law of the echo point cloud information based on the attenuation information;
[0054] The processing unit is used to filter the echo point cloud information acquired at any time based on the transient transformation law to obtain optimized point cloud information.
[0055] In one embodiment, the third determining unit includes:
[0056] Construct sub-units to build the steady-state relationship and attenuation equation of the power spectrum based on the echo point cloud information;
[0057] The first sub-unit is used to solve the attenuation equation based on the steady-state relationship, thereby obtaining the attenuation information of the echo point cloud information during the transmission process.
[0058] In one embodiment, the fourth determining unit includes:
[0059] The first determining subunit is used to determine the attenuation of the power spectrum with sampling time based on the attenuation information;
[0060] The second determining subunit is used to determine the transient transformation law when the power spectrum decays to a steady state with the sampling time, with the power spectrum corresponding to the steady state as the transient transformation law.
[0061] In one embodiment, the processing unit includes:
[0062] The second sub-unit is used to perform an inverse Fourier transform on the power spectrum corresponding to the steady state to obtain the transient probability density distribution of the echo point cloud information.
[0063] The processing subunit is used to filter the echo point cloud information acquired at any time based on the transient probability density distribution to obtain optimized point cloud information.
[0064] It should be noted that the information interaction and execution process between the above modules or units are not related to this application. Figure 1 The method embodiments shown are based on the same concept. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.
[0065] The following is combined with Figure 2The electronic device 200 provided in this application embodiment will be further described below. The memory 202 stores a computer program 204, which can run on the processor 203. For example, the computer program 204 is a point cloud tracking program based on spectral synthesis. When the processor 203 executes the computer program 204, it implements the above-described... Figure 1 The steps in the illustrated point cloud tracking method embodiment based on spectral synthesis are shown. Alternatively, the processor 203 may implement the above steps when executing computer program 204. Figure 4 The functions of each module / unit in the embodiment.
[0066] For example, computer program 204 can be divided into one or more modules / units, which are stored in memory 202 and executed by processor 203 to complete this application. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of computer program 204 in electronic device 200. For example, computer program 204 can be divided into a first processing module, a first determining module, a second determining module, and a second processing module; for the specific functions of each module, please refer to [link to relevant documentation]. Figure 4 The relevant descriptions in the corresponding embodiments are not repeated here.
[0067] Electronic device 200 may include, but is not limited to, processor 203 and memory 202. Those skilled in the art will understand that... Figure 2 This is merely an example of electronic device 200 and does not constitute a limitation on electronic device 200. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 2005 may also include input / output devices, network access devices, buses, etc.
[0068] Processor 203 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.
[0069] The memory 202 can be an internal storage unit of the electronic device 200, such as a hard disk or RAM of the electronic device 200. The memory 202 can also be an external storage device of the electronic device 200, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or FlashCard equipped on the electronic device 200. Furthermore, the memory 202 can include both internal and external storage units of the electronic device 200. The memory 202 is used to store the computer program 204 and other programs and data supported by the electronic device 200. The memory 202 can also be used to temporarily store data that has been output or will be output.
[0070] This application also provides a computer-readable storage medium storing a computer program. When the computer program is run on an electronic device, it causes the electronic device to perform the above-described actions. Figure 1 The steps of the point cloud tracking method based on spectral synthesis are shown.
[0071] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a point cloud tracking software functional unit based on spectral synthesis. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0072] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.
[0073] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of software and electronic hardware. Whether these functions are implemented in hardware or vehicle diagnostic software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0074] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.
Claims
1. A point cloud tracking method based on spectral synthesis, characterized in that, The method includes: Continuously acquire echo information of the target to be tracked within a preset time period, process the echo information, and obtain a set of echo point cloud information; The sampling time and sampling location interval of each echo point cloud information are obtained, and the probability density distribution of the echo point cloud information at each sampling time is determined based on the sampling time and the sampling location interval. Based on the probability density distribution, the variation law of the echo point cloud information with the sampling frequency is determined; Based on the variation law of the echo point cloud information with the sampling frequency, the echo point cloud information acquired at any time is filtered based on the spectral synthesis algorithm to obtain optimized point cloud information. The step of determining the variation law of the echo point cloud information with the sampling frequency based on the probability density distribution includes: Perform a Fourier transform on the probability density distribution to obtain the spectral function of the probability density distribution; The power spectrum corresponding to the probability density distribution is determined based on the spectral function, and the power spectrum is used to represent the variation law of the echo point cloud information with the sampling frequency; The step of filtering the echo point cloud information acquired at any given time based on a spectral synthesis algorithm, according to the variation law of the echo point cloud information with the sampling frequency, to obtain optimized point cloud information includes: Based on the variation law of the echo point cloud information with the sampling frequency, the attenuation information of the echo point cloud information during the transmission process is determined; The transient transformation law of the echo point cloud information is determined based on the attenuation information; Based on the transient transformation law, the echo point cloud information acquired at any time is filtered to obtain optimized point cloud information; The step of determining the attenuation information of the echo point cloud information during transmission based on the variation law of the echo point cloud information with the sampling frequency includes: Based on the echo point cloud information, construct the steady-state relationship and attenuation equation of the power spectrum; Solve the attenuation equation according to the steady-state relationship to obtain the attenuation information of the echo point cloud information during the transmission process; Determining the transient transformation law of the echo point cloud information based on the attenuation information includes: The attenuation of the power spectrum with sampling time is determined based on the attenuation information; When the power spectrum decays to a steady state over the sampling time, the power spectrum corresponding to the steady state is taken as the transient transformation law. The step of filtering the echo point cloud information acquired at any given time based on the transient transformation law to obtain optimized point cloud information includes: Perform an inverse Fourier transform on the power spectrum corresponding to the steady state to obtain the transient probability density distribution of the echo point cloud information; Based on the transient probability density distribution, the echo point cloud information acquired at any time is filtered to obtain optimized point cloud information.
2. The method according to claim 1, characterized in that, Determining the probability density distribution of the echo point cloud information at each sampling time based on the sampling time and the sampling location interval includes: Each sampling location interval is divided based on a preset measurement dimension to obtain multiple discrete grids; The probability of each echo point cloud information falling into each discrete grid at each sampling time is calculated separately. Based on the probability that each echo point cloud information falls into each discrete grid at each sampling time, the probability density distribution of the echo point cloud information at each sampling time is determined.
3. A point cloud tracking device based on spectral synthesis, characterized in that, The device includes: The first processing module is used to continuously acquire echo information of the target to be tracked within a preset time period, process the echo information, and obtain a set of echo point cloud information. The first determining module is used to obtain the sampling time and sampling location interval of each echo point cloud information, and to determine the probability density distribution of the echo point cloud information at each sampling time based on the sampling time and the sampling location interval. The second determining module is used to determine the variation law of the echo point cloud information with the sampling frequency based on the probability density distribution; The second processing module is used to perform filtering processing on the echo point cloud information acquired at any time based on the spectral synthesis algorithm according to the variation law of the echo point cloud information with the sampling frequency, so as to obtain optimized point cloud information. The second determining module includes: The transformation unit allows the user to perform a Fourier transform on the probability density distribution to obtain the spectral function of the probability density distribution. The second determining unit is used to determine the power spectrum corresponding to the probability density distribution based on the spectral function, and to represent the change law of the echo point cloud information with the sampling frequency using the power spectrum; The second processing module includes: The third determining unit is used to determine the attenuation information of the echo point cloud information during the transmission process based on the variation law of the echo point cloud information with the sampling frequency. The fourth determining unit is used to determine the transient transformation law of the echo point cloud information based on the attenuation information; The processing unit is used to filter the echo point cloud information acquired at any time based on the transient transformation law to obtain optimized point cloud information. The third determining unit includes: Construct sub-units to build the steady-state relationship and attenuation equation of the power spectrum based on the echo point cloud information; The first sub-unit is used to solve the attenuation equation according to the steady-state relationship, and obtain the attenuation information of the echo point cloud information during the transmission process. The fourth determining unit includes: The first determining subunit is used to determine the attenuation of the power spectrum with sampling time based on the attenuation information; The second determining subunit is used to determine the transient transformation law when the power spectrum decays to a steady state with the sampling time, with the power spectrum corresponding to the steady state as the transient transformation law. Processing unit, including: The second sub-unit is used to perform an inverse Fourier transform on the power spectrum corresponding to the steady state to obtain the transient probability density distribution of the echo point cloud information. The processing subunit is used to filter the echo point cloud information acquired at any time based on the transient probability density distribution to obtain optimized point cloud information.
4. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the steps of the point cloud tracking method based on spectral synthesis as described in claim 1 or 2 when executing the computer program.
5. A computer-readable storage medium storing a computer program product, characterized in that, When the computer program product is run on an electronic device, it causes the electronic device to perform the steps of the point cloud tracking method based on spectral synthesis as described in claim 1 or 2.