Methods, devices, computer equipment, and storage media for acquiring sensory information

Abstract

This invention relates to the field of network communication, and in particular to a method, apparatus, computer equipment, and storage medium for obtaining sensing information. While ensuring the performance of radar and communication, it allows for the free setting and control of transmission power and peak-to-average power ratio to obtain an integrated communication and sensing signal. This enables efficient sharing of spectrum sub-band resources and transmission power, maximizes the utilization of spectrum resources, and minimizes the negative impact of radio frequency circuit transmission signal distortion caused by the possibility that the echo signal noise power may be greater than the echo power, thereby meeting the information sensing needs at longer distances.

Description

Technical Field

[0001] This invention relates to the field of network communication, and in particular to a method, apparatus, computer device, and storage medium for obtaining sensing information. Background Technology

[0002] In the study of spectrum resource allocation for vehicle-mounted radar, the main challenge lies in simultaneously ensuring safe detection by the radar and securing a larger transmission link (VRSD-ETL) for the communication network. Meanwhile, Integrated Sensing and Communication (ISAC) opens up new service possibilities for sixth-generation (6G) systems. In the increasingly automotive environment, the rational utilization of (ISAC) technology can significantly reduce signal interference between vehicles and address a range of issues affecting driving safety, such as spectrum congestion, demonstrating broad application prospects.

[0003] Current technical solutions that utilize the degrees of freedom provided by the communication modulation gap to optimize the sensing function are difficult to overcome the problems that ISAC waveforms with extremely high communication quality often exhibit high peak-to-average power ratio (PAPR) and integral sidelobe level (ISL) of aperiodic autocorrelation function (CAF); or that ISAC waveforms with extremely high sensing quality often exhibit low SNR or low transmission rate (DTR), resulting in reduced information sensing efficiency. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide a method, apparatus, computer device, and storage medium for obtaining sensing information. While ensuring the performance of radar and communication, the transmit power and peak-to-average power ratio can be freely set and controlled to obtain an integrated communication and sensing signal. This enables efficient sharing of spectrum sub-band resources and transmit power, maximizes the utilization rate of spectrum resources, and minimizes the negative impact of radio frequency circuit transmission signal distortion caused by the possibility that the echo signal noise power may be greater than the echo power, so as to meet the information sensing needs at longer distances.

[0005] In a first aspect, embodiments of this application provide a method for obtaining perceived information, comprising the following steps:

[0006] Acquire the sensing modulation signal, and construct a time-delay Doppler ambiguity function matrix based on the ambiguity function;

[0007] Based on the time-delay Doppler ambiguity function matrix, with minimizing the side lobes of the ambiguity function as the optimization objective and channel capacity and peak-to-average power ratio as constraints, an optimization problem function is constructed; a fast convergence algorithm is used to iteratively solve the optimization problem function to obtain the integrated communication and sensing signal;

[0008] The integrated communication and sensing signal is sent to the receiving end to obtain the sensing feedback signal sent by the receiving end. The sensing feedback signal is then analyzed to obtain the sensing information of the receiving end.

[0009] Secondly, embodiments of this application provide a sensing information acquisition device, comprising:

[0010] A matrix construction module is used to acquire the sensing modulation signal and construct a time-delay Doppler ambiguity function matrix based on the ambiguity function;

[0011] The signal acquisition module is used to construct an optimization problem function based on the time-delay Doppler ambiguity function matrix, with minimizing the side lobes of the ambiguity function as the optimization objective and channel capacity and peak-to-average power ratio as constraints; and to iteratively solve the optimization problem function using a fast convergence algorithm to obtain the integrated communication and sensing signal.

[0012] The information sensing module is used to send the integrated communication and sensing signal to the receiving end, obtain the sensing feedback signal sent by the receiving end, and analyze the sensing feedback signal to obtain the sensing information of the receiving end.

[0013] Thirdly, embodiments of this application provide a computer device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, it implements the steps of the perception information acquisition method as described in the first aspect.

[0014] Fourthly, embodiments of this application provide a storage medium storing a computer program that, when executed by a processor, implements the steps of the perception information acquisition method as described in the first aspect.

[0015] In this application embodiment, a method, apparatus, computer device, and storage medium for obtaining sensing information are provided. While ensuring the performance of radar and communication, the transmit power and peak-to-average power ratio can be freely set and controlled to obtain an integrated communication and sensing signal. This enables efficient sharing of spectrum sub-band resources and transmit power, maximizes the utilization rate of spectrum resources, and minimizes the negative impact of radio frequency circuit transmission signal distortion caused by the possibility that the echo signal noise power may be greater than the echo power, so as to meet the information sensing needs at longer distances.

[0016] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating an application scenario of the perception information acquisition method provided in one embodiment of this application;

[0018] Figure 2 A flowchart illustrating a method for obtaining perceived information provided in one embodiment of this application;

[0019] Figure 3 This is a flowchart illustrating step S1 of a method for obtaining perception information according to an embodiment of this application.

[0020] Figure 4 This is a flowchart illustrating step S2 of a method for obtaining perception information according to an embodiment of this application.

[0021] Figure 5 This is a flowchart illustrating step S3 of a method for obtaining perception information according to an embodiment of this application.

[0022] Figure 6 This is a schematic diagram of the structure of a sensing information acquisition device provided in one embodiment of this application;

[0023] Figure 7 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. Detailed Implementation

[0024] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0025] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0026] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0027] Please see Figure 1 , Figure 1This diagram illustrates an application scenario of the perception information acquisition method provided in one embodiment of this application. The application scenario includes a traffic signal tower 1 and a vehicle 2. The traffic signal tower and the vehicle communicate via a V2I link for energy transmission and data reception. The traffic signal tower provides the vehicle with real-time road information, traffic conditions, and road condition updates. The vehicle can make driving decisions based on the received information. The information transmitted by the traffic signal tower in the diagram can help vehicles ahead avoid traffic congestion (e.g., the communication path ① between the vehicle and the traffic signal tower).

[0028] Vehicles transmit energy and receive data via V2V links. Each vehicle is equipped with communication devices, and based on communication paths ⑥ and ⑦, it can send and receive information such as position, speed, and acceleration in real time. The SAC system integrates radar sensing capabilities; vehicles can detect vehicles or obstacles in their surroundings using radars ③ and ④, and avoid collisions. By utilizing ISAC technology, vehicles can automatically adjust their speed and lane based on the received sensing data through radar detection paths between links ① and ②, thus achieving safe driving.

[0029] Please see Figure 2 , Figure 2 The following is a flowchart illustrating a method for obtaining perceived information according to an embodiment of this application. The method includes the following steps:

[0030] S1: Acquire the sensing modulation signal, and construct a time-delay Doppler ambiguity function matrix based on the ambiguity function.

[0031] Both signal towers and vehicles can serve as transmitters. In this embodiment, the transmitter obtains a sensing modulation signal. Specifically, the transmitter performs QPSK / QAM encoding based on the information to be sensed to obtain the sensing modulation signal. The sensing modulation signal includes information vectors x of several subcarriers. m If x m If (n) = 1 + 1i, then it means that the m-th V2V link uses the n-th sub-pulse BPSK constellation binary representation as 00.

[0032] The transmitter constructs a time-delay Doppler ambiguity function matrix based on the sensing modulation signal using a ambiguity function.

[0033] Please see Figure 3 , Figure 3 The flowchart of S1 in the perception information acquisition method provided in one embodiment of this application is shown, including step S11, as follows:

[0034] S11: Obtain the transmission power and information vector of several subcarriers in the sensing modulation signal. Based on the transmission power and information vector of several subcarriers and the preset time delay and Doppler frequency shift, construct the time delay Doppler ambiguity function matrix based on the ambiguity function to obtain the time delay Doppler ambiguity function matrix.

[0035] In this embodiment, the transmitting end obtains the transmit power and information vector of several subcarriers in the sensing modulation signal. Based on the transmit power and information vector of the several subcarriers, and a preset time delay and Doppler frequency shift, a time-delay Doppler ambiguity function matrix is ​​constructed based on the ambiguity function to obtain the time-delay Doppler ambiguity function matrix. The expression of the time-delay Doppler ambiguity function matrix is ​​as follows:

[0036]

[0037] In the formula, χ(τ,f d ) represents the time-delay Doppler ambiguity function matrix, τ represents the time delay, and f represents the distance between the transmitter and receiver. d The Doppler frequency shift is used to represent the velocity angular frequency between the transmitter and receiver, where M is the total number of subcarriers, and w m Let x be the transmit power of the m-th subcarrier. m Let H be the information vector of the m-th subcarrier in the sensing modulation signal, and let H be the conjugate transpose symbol. J is the conjugate transpose of the information vector of the m-th subcarrier. τ This is the autocorrelation matrix.

[0038] S2: Based on the time-delay Doppler ambiguity function matrix, with minimizing the side lobes of the ambiguity function as the optimization objective and channel capacity and peak-to-average power ratio as constraints, an optimization problem function is constructed; a fast convergence algorithm is used to iteratively solve the optimization problem function to obtain the integrated communication and sensing signal.

[0039] To meet the information sensing needs of receivers at greater distances while ensuring radar and communication performance, in this embodiment, the transmitter constructs an optimization problem function based on the time-delay Doppler ambiguity function matrix, with minimizing the sidelobes of the ambiguity function as the optimization objective and channel capacity and peak-to-average power ratio as constraints. The constraints include constraints based on channel capacity and constraints based on peak-to-average power ratio. The constraints based on channel capacity indicate that the phase difference between the channel capacity and channel coding of several channels of several subcarriers in the sensing modulation signal is less than a preset correction factor. The constraints based on peak-to-average power ratio indicate that the peak-to-average power ratio of the sensing modulation signal is greater than a preset upper limit and less than a preset lower limit, as detailed below:

[0040] stC1、|arg(x m,n )-arg(s m,n )|<ε

[0041] ...C2、

[0042]

[0043] In the formula, C1 and C2 are the constraints based on channel capacity and the constraints based on peak-to-average power ratio, respectively, and x m,n Let s be the channel capacity of the nth channel on the mth subcarrier. m,n Let ε be the channel coding for the nth channel of the mth subcarrier, ε be the correction factor, arg(·) be the extremum function, and α be the channel coding for the nth channel of the mth subcarrier. L α is the upper limit of the peak-to-average power ratio. H The lower limit of the peak-to-average power ratio is given by , and PARP is the peak-to-average power ratio.

[0044] The expression for the optimization problem function is:

[0045]

[0046] In the formula, f(τ, f d ) is the optimization problem function, N is the pulse train length of the sensing modulation signal, p is the power of the complementary signal ambiguity function, t is the numerical dilation constraint coefficient, Θ is the number of quantizations in the Doppler frequency shift, and l is the l-th quantization in the Doppler frequency shift.

[0047] The transmitter employs a fast convergence algorithm, specifically the Majorization-Minimization algorithm, to iteratively solve the optimization problem function and obtain an integrated communication and sensing signal. This allows for more flexible control of the transmit power and PAPR, minimizing the negative impact of RF circuit signal distortion caused by the echo signal noise power potentially exceeding the echo power, thus meeting the information sensing needs of receivers at greater distances.

[0048] Please see Figure 4 , Figure 4 The flowchart of S2 in the perception information acquisition method provided in one embodiment of this application is shown, including steps S21 to S22, as follows:

[0049] S21: Obtain the Taylor second-order expansion coefficients of the optimization problem function in the current iteration, and calculate the coefficients of the first approximation Hermite matrix based on the Taylor second-order expansion coefficients of the optimization problem function in the current iteration, thereby obtaining the coefficients of the first approximation Hermite matrix in the current iteration.

[0050] In this embodiment, the transmitting end uses the sensing modulation signal as the input signal for the first iteration according to a preset number of iterations, and performs a Taylor expansion on the optimization problem function constructed based on the sensing modulation signal to obtain the Taylor second-order expansion coefficients of the optimization problem function in the current iteration. The expression for the Taylor second-order expansion coefficients is as follows:

[0051]

[0052] In the formula, i represents the iteration. Let χ(τ,f) be the first-order coefficient in the second-order Taylor expansion of the optimization problem function in the i-th iteration. d ) i Let t be the time-delay Doppler blur function matrix for the i-th iteration. i is the numerical expansion constraint coefficient for the i-th iteration.

[0053] The transmitting end calculates the coefficients of the first approximation Hermitian matrix based on the Taylor second-order expansion coefficients of the optimization problem function in the current iteration, thereby obtaining the coefficients of the first approximation Hermitian matrix in the current iteration. The expression for the coefficients of the first approximation Hermitian matrix is ​​as follows:

[0054]

[0055] In the formula, λ is the coefficient of the Hermitian matrix obtained from the first approximation, and w m Let be the transmit power of the m-th subcarrier.

[0056] S22: Using the sensing modulation signal as the input signal for the current iteration, calculate the coefficients of the second approximation Hermite matrix based on the input signal for the current iteration, and obtain the coefficients of the second approximation Hermite matrix for the current iteration.

[0057] In this embodiment, the transmitting end calculates the coefficients of the second approximation Hermitian matrix based on the sensing modulation signal of the current iteration, thereby obtaining the coefficients of the second approximation Hermitian matrix for the current iteration. The expression for the second approximation Hermitian matrix is:

[0058]

[0059] In the formula, H m The matrix is ​​the reciprocal of the m-th subcarrier. This is the information vector of the m-th subcarrier in the i-th iteration;

[0060] S23: Based on the sensing modulation signal of the current iteration, the coefficients of the first approximation of the Hermite matrix, and the coefficients of the second approximation of the Hermite matrix, the preset upper bound function is iteratively solved to obtain the output signal of the current iteration. The output signal of the current iteration is used as the sensing modulation signal of the next iteration, and the iterative solution is repeated to obtain the integrated communication and sensing signal.

[0061] To transform a non-convex problem into an easily solvable convex problem, in this embodiment, the transmitter constructs an upper bound function to approximate the original non-convex optimization problem function, ensuring that the upper bound function is consistent with the original optimization problem function at the current solution. The expression of the upper bound function is:

[0062]

[0063] In the formula, U(τ, f) d ) is an upper bound function. Let be the information vector of the m-th subcarrier in the i-th iteration, and λ be the coefficients of the Hermitian matrix in the first approximation. The coefficients are the Hermitian matrix of the second approximation.

[0064] The transmitter iteratively solves a preset upper bound function based on the sensing modulation signal of the current iteration, the coefficients of the first approximation of the Hermite matrix, and the coefficients of the second approximation of the Hermite matrix to obtain the output signal of the current iteration. The output signal of the current iteration is used as the sensing modulation signal of the next iteration, and the iterative solution is repeated until the convergence condition is met, that is, the system performance reaches the expected requirements or the algorithm reaches the preset maximum number of iterations, to obtain the integrated communication and sensing signal, so as to achieve efficient sharing of spectrum sub-band resources and transmission power, maximize the utilization of spectrum resources, and meet the transmission requirements of different power.

[0065] S3: Send the integrated communication and sensing signal to the receiving end, obtain the sensing feedback signal sent by the receiving end, and analyze the sensing feedback signal to obtain the sensing information of the receiving end.

[0066] In this embodiment, the transmitting end sends the integrated communication and sensing signal to the receiving end. In an optional embodiment, the transmitting end can use a coherent accumulation method to preprocess the integrated communication and sensing signal in order to make full use of resources and achieve better radar detection function.

[0067] The transmitting end receives the sensing feedback signal sent by the receiving end, and analyzes the sensing feedback signal to obtain the sensing information of the receiving end. The sensing feedback signal is the signal after the receiving end converts the integrated communication and sensing signal into a communication and sensing integrated signal based on the integrated communication and sensing signal as the sensing modulation signal.

[0068] Please see Figure 5 , Figure 5 The flowchart of S3 in the method for obtaining perception information provided in one embodiment of this application is shown, including step S31, as follows:

[0069] S31: Perform matched filtering on the sensing feedback signal to obtain a matched-filtered sensing feedback signal, and obtain the sensing information of the receiving end based on the matched-filtered sensing feedback signal and a preset data detection algorithm.

[0070] In this embodiment, the transmitting end performs matched filtering on the sensing feedback signal to obtain a matched-filtered sensing feedback signal, thereby maximizing the signal-to-noise ratio (SNR) and improving the target detection capability.

[0071] The transmitting end analyzes the time delay and Doppler shift in the matched-filtered sensing feedback signal based on the preset data detection algorithm to obtain the distance and velocity angular frequency between the transmitting end and the receiving end, thereby capturing the target position and velocity of the transmitting end and obtaining the sensing information of the receiving end. The data detection algorithm is as follows:

[0072]

[0073] In the formula, S represents the perceived information, and y m Let m be the information vector of the m-th subcarrier in the sensing feedback signal. It is the conjugate transpose of the information vector of the m-th subcarrier in the sensing feedback signal.

[0074] While ensuring the performance of radar and communication, the transmit power and peak-to-average power ratio can be freely set and controlled to obtain integrated communication and sensing signals. This enables efficient sharing of spectrum sub-band resources and transmit power, maximizes the utilization of spectrum resources, and minimizes the negative impact of radio frequency circuit transmit signal distortion caused by the possibility that the echo signal noise power may be greater than the echo power, so as to meet the information sensing needs at longer distances.

[0075] Please refer to Figure 6 , Figure 6 This is a schematic diagram of a sensing information acquisition device according to an embodiment of this application. The device can be implemented entirely or partially through software, hardware, or a combination of both. The device 6 includes:

[0076] Matrix construction module 61 is used to acquire the sensing modulation signal and construct a time-delay Doppler ambiguity function matrix based on the ambiguity function.

[0077] The signal acquisition module 62 is used to construct an optimization problem function based on the time-delay Doppler ambiguity function matrix, with minimizing the side lobes of the ambiguity function as the optimization objective and channel capacity and peak-to-average power ratio as constraints; and to iteratively solve the optimization problem function using a fast convergence algorithm to obtain the integrated communication and sensing signal.

[0078] The information sensing module 63 is used to send the integrated communication sensing signal to the receiving end, obtain the sensing feedback signal sent by the receiving end, and analyze the sensing feedback signal to obtain the sensing information of the receiving end.

[0079] In this embodiment, a matrix construction module acquires the sensing modulation signal and constructs a time-delay Doppler ambiguity function matrix based on the ambiguity function. A signal acquisition module, using the time-delay Doppler ambiguity function matrix and minimizing the ambiguity function sidelobes as the optimization objective, and channel capacity and peak-to-average power ratio as constraints, constructs an optimization problem function. A fast convergence algorithm iteratively solves the optimization problem function to obtain an integrated communication-sensing signal. An information sensing module transmits the integrated communication-sensing signal to the receiving end, obtaining the sensing feedback signal. The sensing feedback signal is then analyzed to obtain the sensing information from the receiving end. While ensuring radar and communication performance, the transmit power and peak-to-average power ratio can be freely set and controlled to obtain the integrated communication-sensing signal. This achieves efficient sharing of spectrum sub-band resources and transmit power, maximizing spectrum resource utilization and minimizing the negative impact of RF circuit transmission signal distortion caused by echo signal noise power potentially exceeding echo power, thus meeting the needs of information sensing over longer distances.

[0080] Please refer to Figure 7 , Figure 7 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. The computer device 7 includes: a processor 71, a memory 72, and a computer program 73 stored in the memory 72 and executable on the processor 71. The computer device can store multiple instructions, which are adapted to be loaded and executed by the processor 71. Figures 1 to 5 The method steps shown can be found in the following document for detailed execution process. Figures 1 to 5 The specific details shown will not be repeated here.

[0081] The processor 71 may include one or more processing cores. The processor 71 connects to various parts of the server using various interfaces and lines, and executes various functions and processes data of the sensing information acquisition device 5 by running or executing instructions, programs, code sets, or instruction sets stored in the memory 72, and by calling data stored in the memory 72. Optionally, the processor 71 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 71 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU mainly handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required to be displayed on the touch screen; and the modem is used for wireless communication. It is understood that the modem may also not be integrated into the processor 71 and may be implemented as a separate chip.

[0082] The memory 72 may include random access memory (RAM) or read-only memory. Optionally, the memory 72 may include a non-transitory computer-readable storage medium. The memory 72 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 72 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 72 may also be at least one storage device located remotely from the aforementioned processor 71.

[0083] This application embodiment also provides a storage medium that can store multiple instructions, which are adapted to be loaded and executed by a processor as described above. Figures 1 to 5 The method steps shown can be found in the following document for detailed execution process. Figures 1 to 5 The specific details shown will not be repeated here.

[0084] 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 software functional unit. 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.

[0085] 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.

[0086] 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 computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the algorithm. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0087] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0088] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0089] Furthermore, the functional units in the various embodiments of the present invention 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 software functional unit.

[0090] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms.

[0091] This invention is not limited to the above-described embodiments. If any modifications or variations to this invention do not depart from the spirit and scope of this invention, and if such modifications and variations fall within the scope of the claims and equivalent technologies of this invention, then this invention also intends to include such modifications and variations.

Claims

1. A method for acquiring sensory information, characterized in that, Includes the following steps: Acquire the sensing modulation signal, and construct a time-delay Doppler ambiguity function matrix based on the ambiguity function; Based on the time-delay Doppler ambiguity function matrix, with minimizing the side lobes of the ambiguity function as the optimization objective and channel capacity and peak-to-average power ratio as constraints, an optimization problem function is constructed; a fast convergence algorithm is used to iteratively solve the optimization problem function to obtain the integrated communication and sensing signal; The integrated communication and sensing signal is sent to the receiving end to obtain the sensing feedback signal sent by the receiving end. The sensing feedback signal is then analyzed to obtain the sensing information of the receiving end.

2. The method for obtaining sensory information according to claim 1, characterized in that, The step of constructing a time-delay Doppler ambiguity function matrix based on the sensing modulation signal includes the following steps: The transmit power and information vector of several subcarriers in the sensing modulation signal are obtained. Based on the transmit power and information vector of several subcarriers and the preset time delay and Doppler frequency shift, the time delay Doppler ambiguity function matrix is ​​constructed based on the ambiguity function to obtain the time delay Doppler ambiguity function matrix.

3. The method for obtaining sensory information according to claim 1, characterized in that: The constraints include constraints based on channel capacity and constraints based on peak-to-average power ratio. The constraints based on channel capacity are used to indicate that the phase difference between the channel capacity and channel coding of several channels of several subcarriers in the sensing modulation signal is less than a preset correction factor. The constraints based on peak-to-average power ratio are used to indicate that the peak-to-average power ratio of the sensing modulation signal is greater than a preset upper limit of peak-to-average power ratio and less than a preset lower limit of peak-to-average power ratio.

4. The method for obtaining sensory information according to claim 2, characterized in that: The optimization problem function is used to indicate the minimization of the sidelobes of the fuzzy function, and the expression of the optimization problem function is: In the formula, f(τ, f d ) is the optimization problem function, N is the pulse train length of the sensing modulation signal, p is the power of the ambiguity function, t is the numerical dilation constraint coefficient, Θ is the number of quantizations in the Doppler frequency shift, and l is the l-th quantization in the Doppler frequency shift.

5. The method for obtaining sensory information according to claim 4, characterized in that, The method employs a fast convergence algorithm to iteratively solve the optimization problem function to obtain the integrated communication and sensing signal, including the following steps: Obtain the Taylor second-order expansion coefficients of the optimization problem function in the current iteration, and calculate the coefficients of the first approximation Hermite matrix based on the Taylor second-order expansion coefficients of the optimization problem function in the current iteration, thereby obtaining the coefficients of the first approximation Hermite matrix in the current iteration. Using the sensing modulation signal as the input signal for the current iteration, the coefficients of the second approximation Hermite matrix are calculated based on the input signal for the current iteration, thus obtaining the coefficients of the second approximation Hermite matrix for the current iteration. Based on the sensing modulation signal of the current iteration, the coefficients of the first approximation Hermite matrix, and the coefficients of the second approximation Hermite matrix, the preset upper bound function is iteratively solved to obtain the output signal of the current iteration. The output signal of the current iteration is used as the sensing modulation signal of the next iteration, and the iterative solution is repeated to obtain the integrated communication and sensing signal.

6. The method for obtaining sensory information according to claim 5, characterized in that: The sensing feedback signal is the signal obtained by the receiving end after converting the integrated communication and sensing signal into a sensing integrated signal based on the integrated communication and sensing signal as a sensing modulation signal. The step of parsing the sensing feedback signal to obtain the sensing information from the receiving end includes the following steps: The sensing feedback signal is subjected to matched filtering to obtain a matched-filtered sensing feedback signal. Based on the matched-filtered sensing feedback signal and a preset data detection algorithm, the sensing information of the receiving end is obtained.

7. A device for acquiring sensing information, characterized in that, include: A matrix construction module is used to acquire the sensing modulation signal and construct a time-delay Doppler ambiguity function matrix based on the ambiguity function; The signal acquisition module is used to construct an optimization problem function based on the time-delay Doppler ambiguity function matrix, with minimizing the side lobes of the ambiguity function as the optimization objective and channel capacity and peak-to-average power ratio as constraints; and to iteratively solve the optimization problem function using a fast convergence algorithm to obtain the integrated communication and sensing signal. The information sensing module is used to send the integrated communication and sensing signal to the receiving end, obtain the sensing feedback signal sent by the receiving end, and analyze the sensing feedback signal to obtain the sensing information of the receiving end.

8. A computer device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor; the computer program, when executed by the processor, implements the steps of the method for obtaining perceptual information as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the perceptual information acquisition method as described in any one of claims 1 to 6.

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