A cellular system sensing method, device, product, equipment and medium

Abstract

The application discloses a kind of no-cell system sensing method, device, product, equipment and medium, comprising: establishing the probability model of system parameter in no-cell system, multiple distributed sending APs, multiple receiving APs and CPU are deployed in no-cell system, system parameter includes the receiving signal of any receiving AP, the probability model of the receiving signal of receiving AP is determined based on the angle and time delay corresponding to the path of sampling point;Through each receiving AP, calculate local information based on the probability model, local information contains the probability that the receiving AP determines the existence of scatterer on different sampling points;Through CPU, receive the local information sent by each receiving AP and calculate global information based on the local information, global information contains the probability that CPU determines the existence of scatterer on different sampling points;The position of the scatterer perceived based on the global information is determined.Such, can use multiple AP cooperation to improve the position sensing accuracy, reduce sensing cost.

Description

Technical Field

[0001] This application relates to the field of wireless communication transmission technology, and in particular to a non-cellular system sensing method, apparatus, product, equipment and medium. Background Technology

[0002] With the continuous development of communication technology and the increasing resolution across various signal domains, the design of integrated communication and sensing technologies that can share bandwidth and hardware has become a research hotspot, aiming to improve bandwidth utilization and reduce hardware requirements. Currently, methods for angular domain cooperative sensing, which primarily rely on high angular resolution provided by a large number of antennas, may require configuring a fully digital beamforming array for each access point (AP), leading to excessively high costs in acellular systems. Therefore, developing sensing methods suitable for acellular systems with a smaller number of antennas per AP, improving sensing flexibility, and reducing sensing costs are urgent problems to be solved. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a non-cellular system sensing method, apparatus, product, equipment, and medium to improve sensing flexibility and reduce sensing costs. The specific solution is as follows:

[0004] In a first aspect, this application discloses a sensing method for a cellular-free system, comprising:

[0005] A probabilistic model of system parameters is established in a non-cellular system, wherein multiple distributed access points (APs) and CPUs connected to the multiple distributed APs are deployed in the non-cellular system, the multiple distributed APs include transmitting APs and multiple receiving APs, the system parameters include the received signals of any receiving AP, and the probabilistic model of the received signals of the receiving APs is determined based on the angle and time delay corresponding to the path passing through the sampling points.

[0006] For each of the receiving APs, local information is calculated based on the probability model, wherein the local information includes the probability that the receiving AP determines that a scatterer exists at different sampling points;

[0007] The CPU receives local information sent by each receiving AP and calculates global information based on the local information. The global information includes the probability that the CPU determines the presence of a scatterer at different sampling points.

[0008] The location of the sensed scatterer is determined based on the global information.

[0009] Optional, also includes:

[0010] The first iteration logic is executed, wherein the first iteration logic includes: each receiving AP estimating the probability distribution information of local latent variables according to the variational Bayes principle, the local latent variables including: target gain and the accuracy of target gain, the target gain being a vector composed of the LOS path and the gain of the propagation path formed through different sampling points, and the accuracy of target gain being the accuracy of the gain of the corresponding LOS path and the propagation path formed through different sampling points; the CPU estimating the probability distribution information of the accuracy of common noise;

[0011] When the first iterative logic satisfies the first preset convergence condition, the second iterative logic is executed. When the second iterative logic satisfies the second preset convergence condition, the step of determining the position of the perceived scatterer based on the global information is triggered.

[0012] The second iterative logic includes: calculating local information based on the accuracy estimation result of the target gain for each receiving AP; receiving the local information sent by each receiving AP and calculating global information and a first probability based on the local information, wherein the first probability is the probability that a scatterer exists at the sampling point location as determined by the CPU; calculating a second probability based on the first probability and the local information for each receiving AP, wherein the second probability represents the probability that a scatterer exists at the sampling point location as determined by the receiving AP, and the second probability is used as the probability distribution information for estimating the accuracy of the target gain when executing the first iterative logic in the next round; and updating the location of the sampling point based on the target gain estimation result and the common noise accuracy estimation result.

[0013] Optionally, updating the sampling point position based on the estimation result of the target gain and the estimation result of the common noise accuracy includes:

[0014] A surrogate function is constructed based on the estimation results of the target gain and the estimation results of the common noise accuracy.

[0015] Calculate the partial derivatives of the surrogate function with respect to the position of the sampling point in different dimensions, and update the position of the sampling point based on the partial derivatives.

[0016] Optionally, determining the location of the sensed scatterer based on the global information includes:

[0017] Target sampling points are extracted based on the global information, wherein the target sampling points are sampling points on which the CPU determines that the probability of a scatterer is greater than a preset threshold.

[0018] The location of the target sampling point is determined as the location of the sensed scatterer.

[0019] Optionally, the first preset convergence condition is reaching a first preset number of iterations or the change in the estimation result between two adjacent iterations is less than a first preset threshold, and the second preset convergence condition is reaching a second preset number of iterations or the estimation result between two adjacent iterations is less than a second preset threshold.

[0020] Optionally, the probability model of the received signal of the receiving AP is determined based on the time delay and angle of arrival corresponding to the path passing through the sampling point and the time delay and angle of arrival corresponding to the LOS path.

[0021] Optional, also includes:

[0022] Arbitrarily select two receiving APs, and estimate the time delay using the received signals of the two receiving APs to obtain the estimated time delay;

[0023] The initial position of the sampling point is determined based on the estimated time delay.

[0024] Secondly, this application discloses a non-cellular system sensing device, comprising:

[0025] The probability model building module is used to build a probability model of system parameters in a non-cellular system. The non-cellular system is equipped with multiple distributed APs and CPUs connected to the multiple distributed APs. The multiple distributed APs include transmitting APs and multiple receiving APs. The system parameters include the received signals of any of the receiving APs. The probability model of the received signals of the receiving APs is determined based on the angle and time delay corresponding to the path passing through the sampling points.

[0026] A local information calculation module is used to calculate local information based on the probability model for each of the receiving APs, wherein the local information includes the probability that the receiving AP determines that there is a scatterer at different sampling points;

[0027] A global information calculation module is used to receive local information sent by each receiving AP through the CPU and calculate global information based on the local information, wherein the global information includes the probability that the CPU determines the presence of a scatterer at different sampling points;

[0028] The scatterer position determination module is used to determine the position of the sensed scatterer based on the global information.

[0029] Thirdly, this application discloses a computer program product that, when executed, implements the aforementioned non-cellular system sensing method.

[0030] Fourthly, this application discloses an electronic device, including a memory and a processor, wherein:

[0031] The memory is used to store computer programs;

[0032] The processor is used to execute the computer program to implement the aforementioned non-cellular system sensing method.

[0033] Fifthly, this application discloses a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned non-cellular system sensing method.

[0034] As can be seen from the above scheme, this application provides a sensing method for a cellular system, including: establishing a probabilistic model of system parameters in the cellular system, wherein the cellular system deploys multiple distributed access points (APs) and a CPU connected to the multiple distributed APs, the multiple distributed APs include transmitting APs and multiple receiving APs, the system parameters include the received signals of any receiving AP, and the probabilistic model of the received signals of the receiving APs is determined based on the angle and time delay corresponding to the path passing through the sampling points; calculating local information based on the probabilistic model through each receiving AP, wherein the local information includes the probability that the receiving AP determines that there is a scatterer at different sampling points; receiving the local information sent by each receiving AP through the CPU and calculating global information based on the local information, wherein the global information includes the probability that the CPU determines that there is a scatterer at different sampling points; and determining the location of the sensed scatterer based on the global information.

[0035] Therefore, the beneficial effects of this application are as follows: It utilizes a non-cellular system composed of multiple distributed APs and CPUs for sensing, thereby improving sensing performance. The probability model for received signals is determined based on the angle and time delay corresponding to the path passing through the sampling points. In the non-cellular system, joint multi-AP cooperative sensing is performed based on angle and time delay, avoiding misjudgments by a single AP and improving estimation accuracy. The angle and time delay have a geometric relationship with the position of the scattering object and can be used for localization. Furthermore, the estimation accuracy of the time delay is independent of the number of antennas, reducing the requirement for the number of antennas on the AP, thus improving sensing flexibility (applicable to both non-cellular systems with a small number of antennas and those with a large number of antennas), reducing sensing costs, and improving sensing performance by utilizing angle information based on time delay estimation.

[0036] Correspondingly, the non-cellular system sensing device, product, equipment, and readable storage medium provided in this application also have the above-mentioned technical effects. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0038] Figure 1 A flowchart of a non-cellular system sensing method provided in this application embodiment;

[0039] Figure 2 A diagram of a non-cellular system architecture for a sensing scenario provided in this application embodiment;

[0040] Figure 3 System factor diagram of the non-cellular system sensing method provided in the embodiments of this application;

[0041] Figure 4 The graph shows the change in detection rate of the sensing method with different numbers of subcarriers when the successful detection range is 1 meter, as provided in the embodiments of this application.

[0042] Figure 5 A flowchart of a non-cellular system sensing method based on Turbo-VBI provided in this application embodiment;

[0043] Figure 6 This is a schematic diagram of a non-cellular system sensing device provided in an embodiment of this application;

[0044] Figure 7 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] With the continuous development of communication technology and the increasing resolution across various signal domains, the design of integrated communication and sensing technologies that can share frequency bands and hardware has become a research hotspot, aiming to improve bandwidth utilization and reduce hardware requirements. Currently, integrated communication and sensing has become one of the key technologies of 6G mobile communication technology. Based on the communication capabilities and waveforms of mobile communication networks, it can simultaneously sense surrounding objects such as drones and vehicles while communicating, and can be applied to a wide range of scenarios including low-altitude economy, the Internet of Things, and telemedicine.

[0047] Currently, performance evaluation standards for inductive signals include resolution, ambiguity function, Doppler sensitivity, Doppler tolerance, peak-to-average power ratio (PAPR), mutual information, and data rate. Available waveforms include orthogonal frequency division multiplexing (OFDM), linear frequency modulation (LFM), and orthogonal time-frequency space (OTFS). Signal optimization includes PAPR optimization, interference management, and adaptive signal optimization.

[0048] For fundamental limits of synsensory systems, performance standards include theoretical metrics for sensing estimation (mean squared error (MSE), Cramero bound (CRB), Wiesweinstein bound (WWB), Ziff-Zakhe bound (ZZB), equivalent Fisher information matrix, normalized cross-ambiguity function, multidimensional ambiguity function, detection probability and false alarm probability, radar capacity, etc.), information theory metrics for communication, methods for estimating the information rate of synsensory systems, equivalent MSE methods, capacity distortion function methods, etc. Existing performance analyses of sensing typically focus on balancing the performance of communication and sensing, and usually involve a small number of sensing targets.

[0049] In traditional radar detection and sensing systems, the number of cooperating nodes is typically small. In such scenarios, for time-delay-based sensing methods, the same time delay between the transmitting and receiving ends may correspond to multiple locations, leading to ambiguity in the sensing results. Furthermore, for time-delay-based or angle-based sensing methods, false detections may occur along the LOS path. In non-cellular systems, by setting up multiple APs in different geographical locations and controlling their cooperation through a CPU (Central Processing Unit), macro-diversity gain can be achieved, providing more uniform coverage and enabling cooperative sensing with a larger number of nodes. However, considering that methods for angle-domain cooperative sensing, which mainly rely on high angular resolution provided by a large number of antennas, may require configuring a fully digital beamforming array for each AP, resulting in excessive costs. Taking all the above into consideration, this application chooses a multi-AP cooperative sensing method that prioritizes time delay and is also applicable with a low number of antennas.

[0050] See Figure 1 As shown in the figure, this application discloses a non-cellular system sensing method, including:

[0051] Step S11: Establish a probabilistic model of system parameters in a cellular-free system, wherein the cellular-free system deploys multiple distributed APs and CPUs connected to the multiple distributed APs, the multiple distributed APs include transmitting APs and multiple receiving APs, the system parameters include the received signals of any receiving AP, and the probabilistic model of the received signals of the receiving APs is determined based on the angle and time delay corresponding to the path passing through the sampling points.

[0052] In this system, "distributed" can be understood as each access point (AP) being placed in a different location. System parameters can include local variables for each AP, which include directly observable received signals and local latent variables. These latent variables include a vector composed of the LOS path and the gain of the propagation path formed through different sampling points, corresponding to the accuracy of the LOS path and the gain of the propagation path formed through different sampling points, and local support variables reflecting a single AP's determination of whether a scatterer exists at each sampling point. System parameters can also include global support variables reflecting the CPU's determination of whether a scatterer exists at different sampling points. By setting the prior distribution of system parameters, a probabilistic model of the system parameters is obtained. Sampling points can be understood as hypothetical scatterers.

[0053] In this embodiment of the application, in a cellular-free system, multiple access points (APs) with several antennas can be deployed to provide coverage and services to users. All APs are connected to a CPU, which can control different APs to cooperate. One AP is selected as a transmitting AP, and the others act as receiving APs. The transmitting AP transmits a sensing signal on one antenna. Resource scheduling ensures the orthogonality of the sensing signal's resource usage with signals used for other functions in the system, eliminating interference. The receiving APs use the received sensing signal to estimate the location of scatterers. The sensing signal can be a downlink data signal. Samples are taken at different locations within the coverage area to form several sampling points.

[0054] In this embodiment, the probability model for the received signal of the receiving AP is determined based on the time delay and angle of arrival corresponding to the path passing through the sampling volume, as well as the time delay and angle of arrival corresponding to the LOS path. That is, in this embodiment, multi-AP cooperative sensing is performed based on time delay information and angle information in a non-cellular system architecture. Through multi-AP cooperation, the energy leakage of the LOS path in single-AP sensing can be overcome, the accuracy of location sensing can be improved by using multi-AP cooperation, and the flexibility under different configurations can be improved.

[0055] This embodiment can establish the received signal of the receiving AP on each subcarrier based on the time delay and angle of arrival corresponding to the path passing through the scatterer and the time delay and angle of arrival corresponding to the LOS path; and determine the received signal of the receiving AP based on the received signal of the receiving AP on all subcarriers. The time delay and angle correspond to the changes in received signals between subcarriers and between antennas, respectively. The change information between subcarriers can be used based on the time delay, and the change information between antennas can be used based on the angle. Combining the two can provide more samples for estimation and further improve the estimation accuracy.

[0056] In this embodiment, two receiving access points (APs) can be arbitrarily selected, and the received signals from the two APs are used to estimate the time delay. Based on the estimated time delay, the initial positions of the sampling points are determined. This method, by calculating the initial set of sampling point positions, reduces the number of sampling points compared to the gridded method, especially in cases with a large area and a small number of scatterers, thus reducing the computational load of subsequent algorithms. Specifically, the time delay estimation can use the Global Least Squares-Signal Parameter Estimation with Rotation Invariant Techniques (TLS-ESPRIT) method.

[0057] Furthermore, a probabilistic model of system parameters in a cellular-free system can be established based on the initial location of the sampling points.

[0058] In one optional implementation, the non-cellular system is equipped with multiple distributed access points (APs) and CPUs connected to the APs. The APs include one transmitting AP and multiple receiving APs. Furthermore, two receiving APs can be arbitrarily selected to estimate the time delay using the received signals and collaboratively solve for the location that satisfies the time delay to obtain a set of sampling point locations (i.e., the initial set of sampling point locations). Based on the sampling point locations, a probabilistic model of the system parameters in the non-cellular system is established.

[0059] Step S12: Calculate local information based on the probability model for each receiving AP, wherein the local information includes the probability that the receiving AP determines that there is a scatterer at different sampling points.

[0060] In this embodiment, local information can be calculated based on the estimation result of the accuracy of the target gain for each receiving AP.

[0061] Step S13: The CPU receives local information sent by each receiving AP and calculates global information based on the local information, wherein the global information includes the probability that the CPU determines the presence of a scatterer at different sampling points.

[0062] This application embodiment can: execute a first iterative logic, wherein the first iterative logic includes: each receiving AP estimating the probability distribution information of local latent variables according to the variational Bayesian principle, the local latent variables including: target gain and the accuracy of target gain, the target gain being a vector composed of the gains of the LOS path (i.e., line-of-sight path) and the propagation paths formed through different sampling points, the accuracy of target gain being the accuracy of the gains of the corresponding LOS path and the propagation paths formed through different sampling points; the CPU estimating the probability distribution information of common noise accuracy; the common noise accuracy can be understood as the noise accuracy determined by the CPU. In this embodiment, the noise accuracy estimation is calculated by the CPU by integrating the results of each AP, which can improve the estimation accuracy.

[0063] If the first iterative logic satisfies the first preset convergence condition, the second iterative logic is executed. If the second iterative logic satisfies the second preset convergence condition, the step of determining the position of the sensed scatterer based on the global information is triggered. If the first iterative logic does not satisfy the first preset convergence condition, the step of estimating the probability distribution information of local latent variables by each receiving AP according to the variational Bayesian principle is entered, and the first iterative logic is executed again. If the second iterative logic does not satisfy the second preset convergence condition, the step of estimating the probability distribution information of local latent variables by each receiving AP according to the variational Bayesian principle is entered, and the first iterative logic is executed again.

[0064] The second iterative logic includes: calculating local information based on the accuracy estimation result of the target gain for each receiving AP; receiving the local information sent by each receiving AP and calculating global information and a first probability based on the local information, wherein the first probability is the probability that a scatterer exists at the sampling point location as determined by the CPU; calculating a second probability based on the first probability and the local information for each receiving AP, wherein the second probability represents the probability that a scatterer exists at the sampling point location as determined by the receiving AP, and the second probability is used as the probability distribution information for estimating the accuracy of the target gain when executing the first iterative logic in the next round; and updating the location of the sampling point based on the target gain estimation result and the common noise accuracy estimation result.

[0065] In a specific implementation, a surrogate function can be constructed based on the estimation results of the target gain and the estimation results of the common noise accuracy; the partial derivatives of the surrogate function with respect to the position of the sampling point in different dimensions can be calculated, and the position of the sampling point can be updated based on the partial derivatives.

[0066] In this embodiment, the first preset convergence condition can be reaching a first preset number of iterations or the change in the estimation result between two adjacent iterations being less than a first preset threshold, and the second preset convergence condition can be reaching a second preset number of iterations or the estimation result between two adjacent iterations being less than a second preset threshold.

[0067] Step S14: Determine the location of the sensed scatterer based on the global information.

[0068] This application embodiment can extract target sampling points based on the global information, wherein the target sampling point is a sampling point where the probability of the CPU determining that there is a scatterer on the sampling point is greater than a preset threshold; the position of the target sampling point is determined as the position of the sensed scatterer.

[0069] As can be seen, in this embodiment, the probability model for receiving signals from the AP is determined based on the angle and time delay corresponding to the path passing through the sampling point. In a non-cellular system, joint multi-AP cooperative sensing is performed based on time delay to avoid misjudgment by a single AP. The time delay has a geometric relationship with the position of the scatterer and can be used for positioning. Furthermore, the estimation accuracy of the time delay is independent of the number of antennas, which reduces the requirement for the number of antennas on the AP and can reduce sensing costs.

[0070] See Figure 2 As shown, Figure 2 This application provides a schematic diagram of a cellular-free system. The cellular-free system sensing method provided in this application includes the following steps:

[0071] Step 1: As Figure 2 As shown, multiple configurations N are deployed in a cellular-free system. E Each AP with one antenna provides coverage and service to users. All APs are connected to the CPU, which can control different APs to cooperate. One AP is selected as the transmitting AP, and N... A Each access point (AP) acts as a receiving AP, while the transmitting AP sends a sensing signal on a single antenna. Resource scheduling ensures the orthogonality of the sensing signal's resource usage with signals used for other functions in the system, eliminating interference. The receiving AP uses the received sensing signal to estimate the location of scattering objects. The sensing signal can be a downlink data signal. Samples are taken at different locations within the coverage area to form N... G The nth sampling point is established. A Approximate received signal of a receiving AP on the k-th subcarrier:

[0072]

[0073] Where X(k) is the transmitted signal of the transmitting antenna on the k-th subcarrier. For the nth A The noise vector of all antennas of a receiving AP on the k-th subcarrier, wherein the elements follow a mean of 0 and a variance of σ. 2 Complex Gaussian noise. Time delay and angle correspond to the changes in received signals between subcarriers and between antennas, respectively. Information about changes between subcarriers can be obtained from the time delay, and information about changes between antennas can be obtained from the angle. Combining these two methods can provide more samples for estimation, further improving estimation accuracy. Specifically, the channel vector:

[0074]

[0075] in, These represent the gain, angle of arrival (AOA), and delay of the LOS path, respectively. sc For subcarrier spacing, For the nth A The receiving AP regarding the angle The array vector, j is the imaginary sign, and the transmitting antenna on the k-th subcarrier passes through the n-th... G The scatterer at the nth sampling point reaches the nth sampling point. A Channels of each AP:

[0076]

[0077] in The transmitting antenna passes through the nth... G The scatterer at the nth sampling point reaches the nth sampling point. A The gain, AOA, and delay of the propagation path of each AP, specifically:

[0078]

[0079] Where c represents the speed of light. The location of the transmitting antenna of the transmitting AP, where and The coordinates of the transmitting antenna of the transmitting AP are the first and second dimensions, respectively. In this embodiment, the position is represented by two-dimensional coordinates, referred to as the first dimension and the second dimension. For the nth A The center position of all antennas of a receiving AP can be understood as the average position of all antennas. and They are respectively the nth A The center positions of all antennas of a receiving AP are located in the first and second dimensions. For the nth G The location of each sampling point, among which and They are respectively the nth G The location of each sampling point is determined by the coordinates in the first and second dimensions. In this embodiment, the location of the sampling point can be obtained by any two receiving APs estimating the received signal delay and cooperating accordingly. Specifically, each AP coarsely estimates the delay component in the received signal based on the received signal from its own antenna, and extracts the location that simultaneously matches the delay estimation results of the two APs as the sampling point location for subsequent processing. Array vector The nth E One element:

[0080]

[0081] in, For the nth A The nth receiving AP E The root antenna relative to the nth A The relative positions of the centers of all antennas of a receiving AP, where and They are respectively the nth A The nth receiving AP E The root antenna relative to the nth A The offsets of the first and second dimensions of the centers of all antennas of a receiving AP, where λ is the system wavelength. Let... For the set of subcarriers used, the nth A All subcarriers used for sensing by each AP The mathematical model for the received signal, i.e., the received signal of the receiving AP, is as follows:

[0082]

[0083] in, k i Let be the subcarrier number used for sensing, where For including the nth A The matrix of sensed signals received by all antennas of a receiving AP on the k-th subcarrier, taking into account the angle and time delay effects of the paths passing through all sampling points. For including the nth A The matrix of sensed signals received by all antennas of a receiving AP on all subcarriers, taking into account the angle and time delay effects of the path corresponding to all sampling points. Specifically,

[0084]

[0085] Based on the SBL principle and the aforementioned expression for the received signal, combined with the Turbo-VBI framework, a probabilistic model of the system parameters is established, and each parameter is initialized. The corresponding factor graph is shown below. Figure 3 As shown, except for the received signal In addition, circles represent latent variables, and black squares represent the set prior distributions. Specifically, the variables (i.e., the system parameters in the proposed cellular-free system sensing method) are set as follows:

[0086] Among them, the variables include: the nth A The received signal vector of each receiving AP Send AP and nth A A vector consisting of the LOS path between receiving APs and the gain of the propagation path formed by scatterers at different sampling points. Send AP and nth A The accuracy vector corresponding to the gain vector of the LOS path between receiving APs and the propagation path formed by scatterers at different sampling points. n A =0,…,N A The nth A The AP determines the nth... GAre there local supporting variables of the scatterer at each sampling point location? Common noise accuracy γ. And common support vector.

[0087] This embodiment can establish a distributed probability distribution model based on the set of sampling point locations, angle, and time delay information. The proposed mapping matrix... The structure further affects the distribution of the received signal.

[0088] Step 11: Set the prior distribution of local variables for each AP in the system, where the local variables of each AP include the directly observable nth... A The received signal of each AP The local latent variables of each AP include a vector composed of the LOS path and the gain of the propagation path formed through different sampling points. Accuracy of gain corresponding to LOS path and propagation path formed through different sampling points n G =0,…,N G and reflecting the nth A Each AP determines whether there are local support variables of scatterers at each sampling point location.

[0089] The probability model for the received signal of the receiving AP is determined based on the angle and time delay corresponding to the path passing through the sampling points; specifically, for the nth... A The received signal of each AP Distribution in Let x represent a random vector that follows a complex Gaussian distribution with mean μ and covariance matrix Σ. Representative dimensions are The identity matrix, where γ is the common noise precision. The vector consists of the LOS path and the gains of the propagation paths formed through different sampling points. nth G sampling points (n G =0 represents the gain corresponding to the LOS path. Distribution For the accuracy of gain For the LOS path, distribution in Let x represent a random variable that follows a Gamma distribution with parameters a and b. For the NLOS path, the distribution is:

[0090]

[0091] Where δ(·) represents the impulse function. For reflecting the nth... A The AP determines the nth... GAre there local supporting variables of the scatterer at each sampling point location? distributed:

[0092]

[0093] in For the CPU to the nth G Supporting variables for the joint determination of whether a scatterer exists at each sampling point location. These are the conditional probabilities of local supporting variables. Local supporting variables reflect the conditional probabilities of the nth local supporting variable. A The AP determines the nth... G The local support variable for the presence of a scatterer at each sampling point location takes a value of 1 or -1, representing the presence or absence of the scatterer. Local support variables are obtained by each receiving AP, while common support variables are obtained by the CPU and reflect the presence or absence of the scatterer at the nth sampling point. A The AP determines the nth... G The variable indicating whether a scatterer exists at each sampling point is 1 or -1, representing whether it exists or not.

[0094] Step 12: Set the distribution of global hidden variables in the system, including the CPU's handling of the nth variable. G Supporting variables for the joint determination of whether scatterers exist at each sampling point location Common support vector distributed:

[0095]

[0096] Where Z(ζ) is the partition function that makes the probability distribution have an integral of 1. For the common noise precision γ, the distribution... For common support vectors The parameters of the distribution. The elements in the common support vector are the common support variables; calculating the common support variables is equivalent to calculating the common support vector.

[0097] Step 13: Initialize parameters, including the position of each sampling point. Common support vector Distribution parameters Conditional probability of local supporting variables and the nth A The AP determines the nth... G The probability of a scatterer existing at each sampling point location. The parameters a and b of the two Gamma distributions Distribution parameters of common noise accuracy γ d, parameters related to gain accuracy Common noise accuracy related parameters <γ q(γ) .

[0098] Step 2: Define V is the set of received signals from all antennas of all receiving APs on all subcarriers. Figure 3 The set of all variables except the received signal. To include all sampling point location parameters The collection of perception problems can be summarized in the form of the EM framework.

[0099]

[0100] Where t is the iteration number. In the E-step, the distributions of all variables are updated, and the objective function is updated based on the updated distributions. In step M, optimize according to the new objective function. To maximize the objective function. In this application, for distributed processing, the nth... A The set of local latent variables corresponding to each AP in Gain vector The corresponding precision vector, based on the Turbo principle, adds an internal iteration for each receiving AP on top of the EM framework. In one iteration, each receiving AP performs an internal iteration. The distribution of variables γ within the CPU is iterated until convergence, and then the decision of each receiving AP is calculated and passed to the CPU at the nth iteration. G The CPU calculates the global probability by combining the probabilities of each AP and then calculates the decision value for the nth sampling point. G The probability of a scatterer at each sampling point is calculated. Each receiving AP obtains the probability from the CPU and then calculates its own probability of determining the presence of a scatterer at each sampling point, thus completing one E-step iteration. First, each AP performs an E-step iteration based on the variational Bayesian (VB) principle. The specific steps for updating the approximate marginal probability distribution of the implicit variables are as follows:

[0101] Step 21: Update the corresponding value reaching the nth position. A Gain of all LOS and NLOS paths of an AP The distribution is estimated based on the initialization or obtained from steps 22 and 23. and γ obtained from step 24 q(γ) ,definition in diag(x) is a diagonal matrix with x as its diagonal. The estimated results of the distribution are as follows:

[0102]

[0103] in,<f(x)>q(x) =∫f(x)q(x)dx, Re{·} represents the real part of the complex number, ∝ denotes proportionality, exp{·} is the exponential function, and from the estimation results, Follow the mean covariance is The complex Gaussian distribution.

[0104] Step 22: Update for the nth... A Accuracy of LOS path gain corresponding to each AP The distribution estimate is obtained from step 21. and The estimated results of the distribution are as follows:

[0105]

[0106] in, for a+1-1 times, for The first element, Based on the estimation results, Obeying a+1 and For a Gamma distribution with parameter denoted as , based on the properties of the Gamma distribution, we have:

[0107]

[0108] Where Ψ(·) is the digamma function.

[0109] Step 23: Update for the nth... A The AP corresponds to the nth AP. G Precision of path gain at each sampling point n G The estimate of the distribution of >0 is obtained from the initialization or step 44. and the result obtained in step 21 and Estimation results:

[0110]

[0111] Among them, the nth A The AP determines the nth... G The probability of a scatterer existing at each sampling point location. for The nth G +1 element, Based on the estimation results, Obey and For a Gamma distribution with parameter denoted as , based on the properties of the Gamma distribution, we have:

[0112]

[0113] Step 24: Update the estimate of the distribution of the common noise precision γ, obtained from step 21. and Distribution estimation results for γ:

[0114]

[0115] Based on the estimation results, γ follows the principle of and For a Gamma distribution with parameter denoted as , based on the properties of the Gamma distribution, we have:

[0116]

[0117] Step 3: Determine if the iteration in Step 2 has reached the convergence requirement. If not, proceed to Step 2; otherwise, proceed to Step 4. Specifically, the convergence requirement is reaching the required number of iterations or... The estimation results change sufficiently small between two adjacent iterations.

[0118] Step 4: Perform external iteration. Based on the principles of VB and message passing (MP), each receiving AP calculates a message about the local support vector (each receiving AP's determination of whether there is a scatterer at different sampling points) based on the results of the local iteration and passes it to the CPU. The CPU jointly estimates the statistical information of the common support vector (the CPU's determination of whether there is a scatterer at different sampling points) based on the messages passed by all APs and passes the message to each AP. Each AP estimates the distribution of the local support vector based on the received message. The specific steps are as follows:

[0119] Step 41: Update the nth... A Each AP passes the common support vector to the CPU. The message, from the initialization and the initialization or obtained in step 23 and nth A The AP transmits information about the nth bit to the CPU. G The probability distribution of whether a scatterer exists at each sampling point:

[0120]

[0121] in,

[0122]

[0123] Among them, intermediate variables:

[0124]

[0125] Message refers to It can be done Complete description. Observation shows that the nth... A The decision that AP passes to the CPU for the nth time... G The probability of a scatterer existing at each sampling point It can fully describe the probability distribution, therefore the transmission It can transmit messages completely.

[0126] Step 42: Update CPU's common support vector The distribution estimate is obtained from the initialization. and the result obtained in step 41 Estimation results:

[0127]

[0128] in,

[0129]

[0130] Among them, intermediate variables

[0131]

[0132] Observation shows that the CPU determines the nth... G The probability of a scatterer existing at each sampling point It can fully describe the probability distribution.

[0133] Step 43: Update CPU propagation to the nth node A Local support variables of an AP The message, from the initialization and the result obtained in step 42 Estimation results:

[0134]

[0135] in,

[0136]

[0137] Observation shows that the CPU passes the data to the nth... A The determination of the nth AP G The probability of a scatterer existing at each sampling point (That is, the aforementioned first probability) can fully describe the probability distribution.

[0138] Step 44: Update the nth... A The AP determines the nth... G Are there local supporting variables of the scatterer at each sampling point location? The distribution estimate is based on the result obtained in step 41. and obtained in step 43 Estimation results:

[0139]

[0140] in,

[0141]

[0142] Observation shows that the nth A The AP determines the nth... G The probability of a scatterer existing at each sampling point location. (That is, the aforementioned second probability) can fully describe the probability distribution.

[0143] Step 5: Update the position of each sampling point based on the result obtained in Step 21. and Construct proxy function As the objective function for M-step optimization

[0144]

[0145] Where C is a constant, Tr(·) is the trace of the matrix, and the partial derivative of the surrogate function with respect to the coordinates of each sampling point is calculated and the position of each sampling point is updated. The specific steps are as follows:

[0146] Step 51: Calculate the proxy function pair partial derivatives

[0147]

[0148] when hour,

[0149]

[0150] in, for The element in the i-th row and j-th column, when hour,

[0151]

[0152]

[0153] when hour,

[0154]

[0155] Step 52: Calculate the surrogate function pair partial derivatives

[0156] Among them when hour,

[0157]

[0158] when hour,

[0159]

[0160]

[0161] when hour,

[0162]

[0163] Step 53: Update location

[0164]

[0165] Where Δζ is the step size.

[0166] Step 6: Determine whether the iterations in Steps 4 and 5 have reached the convergence requirement. If not, proceed to Step 2; otherwise, proceed to Step 7. Specifically, the convergence requirement is that the required number of iterations has been reached or the difference between the results of two adjacent iterations is sufficiently small.

[0167] Step 7: Extract π snG For sampling points greater than 0.5, the location corresponding to the sampling point is the perceived location of the scatterer. In the case of a single scatterer, four receiving APs, and four receiving antennas per AP, successful detection is defined as the detected scatterer location being within 1 meter of the actual scatterer. Detection rates under different transmit powers and subcarrier numbers are as follows: Figure 4 As shown.

[0168] As can be seen, the beneficial effects of the embodiments of this application are: using a non-cellular system composed of multiple distributed APs and CPUs for sensing improves sensing performance. By using a non-cellular system for joint multi-AP cooperative sensing, it is possible to prevent energy leakage from the LOS path to nearby sampling point locations when a single AP is sensing, which could cause sampling point locations near the LOS path that are actually free of scatterers to be misjudged as having scatterers, leading to false alarms.

[0169] This approach primarily relies on time delay for collaborative multi-AP positioning. Time delay is the propagation time of a signal between the transmitter and receiver, multiplied by the speed of light to equal the distance the signal travels. This application assumes the channel mainly consists of single-hop paths. For a single-hop NLOS path, the corresponding distance is the sum of the distance from the transmitter to the scatterer and the distance from the scatterer to the receiver, which has a geometric relationship with the scatterer's position and can therefore be used for positioning. The accuracy of time delay estimation can be adjusted by the OFDM modulation parameters, independent of the number of antennas; even a single antenna can achieve this. In contrast, the resolvable angular interval for angular domain signals is essentially inversely proportional to the number of antennas, thus requiring multiple antennas to achieve high angular resolution. Therefore, this application has a lower requirement for the number of antennas on the APs, helping to reduce the deployment cost of non-cellular systems. Considering the case of APs with multiple antennas, the increased number of received signal samples can be utilized to improve sensing performance based on time delay estimation using angular information. Therefore, the proposed scheme has greater flexibility and adaptability to different AP configurations. The estimation of complex local latent variables is performed by each receiving AP in a distributed manner, helping to ensure the scalability of the non-cellular system.

[0170] It is understood that the sensing method provided in this application is a sensing method for a non-cellular system based on Turbo-VBI. Figure 5 This is a flowchart of a Turbo-VBI-based non-cellular system sensing method according to an embodiment of this application. A probabilistic model of system parameters is established, and the system parameters are initialized. The sensing process includes two iterations; when the inner iteration meets the convergence condition, it proceeds to the outer iteration. If the outer iteration converges, the sensed scatterers and their corresponding locations are extracted based on the estimated marginal probability distribution of the common support vector. A probabilistic model of system parameters is established and solved using the expectation-maximization (EM) framework. Two arbitrarily selected receiving APs estimate the delay using the received signals and collaboratively obtain an initial set of sampling point locations. Based on the sensing signals received by the receiving APs and combined with the sampling point locations, a system probabilistic model is established, and system parameters are initialized. Each receiving AP individually estimates the probability distribution information of its corresponding local latent variables. The estimated channel gain distribution information is passed to the CPU to estimate the statistical information of the common noise accuracy. Convergence is determined. If convergence is achieved, each receiving AP individually calculates the distribution information of the local support vector and passes it to the CPU to jointly estimate the distribution information of the common support vector. The CPU adjusts the sampling point locations and determines whether convergence is achieved. If convergence is achieved, the sensed scatterer locations are output. Specifically, the following steps may be included:

[0171] Step 1: Based on the deployed N A Configuration N E The receiving AP of the root antenna senses the signal received, using a set of subcarriers. The transmitted signal on the k-th subcarrier is X(k), which is sampled at different locations in the coverage area to form N.G Based on the principle of sparse Bayesian learning (SBL), a probabilistic model of system parameters is established using a sampling point, and each parameter is initialized.

[0172] Step 2: Define the nth... A The set of local latent variables corresponding to each AP Each AP is based on the variational Bayesian (VB) principle. The approximate marginal probability distribution of the implicit variables is updated and passed to the central processing unit (CPU) for updating the approximate marginal probability distribution with common noise accuracy.

[0173] Step 3: Determine if the iteration in Step 2 has reached the convergence requirement. If not, proceed to Step 2; otherwise, proceed to Step 4. Specifically, the convergence requirement is that the required number of iterations has been reached or the difference between the results of two adjacent iterations is sufficiently small.

[0174] Step 4: Perform external iteration. Based on the principles of VB and message passing (MP), each receiving AP calculates a message about the local support vector based on the results of the local iteration and passes it to the CPU. The CPU jointly estimates the statistical information of the common support vector based on the messages passed by all APs and passes the message to each AP. Each AP estimates the distribution of the local support vector based on the received message.

[0175] Step 5: Update the position of each sampling point, construct a surrogate function, calculate the partial derivative of the surrogate function with respect to the coordinates of each sampling point, and update the position of each sampling point; this application provides a precise expression for position adjustment.

[0176] Step 6: Determine whether the iterations in Steps 4 and 5 have reached the convergence requirement. If not, proceed to Step 2; otherwise, proceed to Step 7. Specifically, the convergence requirement is that the required number of iterations has been reached or the difference between the results of two adjacent iterations is sufficiently small.

[0177] Step 7: Extract π snG For sampling points greater than 0.5, the location of the sampling point corresponds to the location of the sensed scatterer.

[0178] This application's embodiments enable multi-AP cooperative sensing in a cellular system architecture, where latency information is primary and angle information is secondary, even with a limited number of antennas per access point (AP). Through multi-AP cooperation, false positives caused by line-of-sight (LOS) energy leakage between transceivers during single-AP sensing can be corrected, reducing the false positive rate and improving sensing performance. This application's embodiment provides a Turbo-VBI-based cellular system sensing method that primarily relies on latency for multi-AP cooperative sensing in a cellular system. It does not depend on the high spatial resolution provided by a large number of antennas and can be used with any number of antennas, thus improving sensing performance.

[0179] As can be seen, this application embodiment utilizes a non-cellular system composed of multiple distributed APs and CPUs for sensing, improving sensing performance. By using a non-cellular system for joint multi-AP cooperative sensing, it can prevent energy leakage from the LOS path to adjacent grids during single-AP sensing, which could lead to false alarms caused by grids near the LOS path without scatterers being misjudged as having scatterers. The initial sampling point location set is formed using the time delay estimation results of two APs. Compared to gridded location sampling, this effectively reduces the number of sampling points and subsequent computational complexity in cases with large areas and a small number of scatterers. The CPU jointly determines the target gain estimation results of each AP, improving the estimation accuracy of common noise. Position adjustment is performed using multi-AP cooperation, calculating local information based on a probability model. The CPU receives local information sent by each receiving AP and calculates global information based on this local information. The location of the sensed scatterer is then determined based on the global information, resulting in a gain in the scatterer location estimation accuracy. The received signal in the receiving AP is determined based on the angle and time delay corresponding to the path passing through the scatterer. The time delay estimation accuracy is independent of the number of antennas, and there are no requirements on the number of antennas on the AP, reducing the hardware cost of the sensing device. Considering the case of AP configuration with multiple antennas, the proposed scheme can utilize the increased received signal samples provided by multiple antennas to improve sensing performance based on time delay estimation and angle information. Therefore, the proposed scheme has more flexible adaptability to different AP configurations.

[0180] Further, see Figure 6 As shown, this application discloses a non-cellular system sensing device, comprising:

[0181] The probability model establishment module 11 is used to establish a probability model of system parameters in a non-cellular system. In the non-cellular system, multiple distributed APs and CPUs connected to the multiple distributed APs are deployed. The multiple distributed APs include transmitting APs and multiple receiving APs. The system parameters include the received signals of any of the receiving APs. The probability model of the received signals of the receiving APs is determined based on the angle and time delay corresponding to the path passing through the sampling points.

[0182] The local information calculation module 12 is used to calculate local information based on the probability model for each of the receiving APs, wherein the local information includes the probability that the receiving AP determines that there is a scatterer at different sampling points;

[0183] The global information calculation module 13 is used to receive local information sent by each of the receiving APs through the CPU and calculate global information based on the local information, wherein the global information includes the probability that the CPU determines that there is a scatterer at different sampling points;

[0184] The scatterer position determination module 14 is used to determine the position of the sensed scatterer based on the global information.

[0185] The device further includes:

[0186] The first iteration logic execution module is used to execute the first iteration logic, wherein the first iteration logic includes: each receiving AP estimating the probability distribution information of local latent variables according to the variational Bayesian principle, the local latent variables including: target gain and the accuracy of target gain, the target gain being a vector composed of the LOS path and the gain of the propagation path formed through different sampling points, and the accuracy of target gain being the accuracy of the gain of the corresponding LOS path and the propagation path formed through different sampling points; and the CPU estimating the probability distribution information of the accuracy of common noise.

[0187] The second iteration logic execution trigger module is used to trigger the execution of the second iteration logic when the first iteration logic satisfies a first preset convergence condition. The second iteration logic includes calculating local information based on the estimation result of the target gain accuracy for each receiving AP; receiving local information sent by each receiving AP through the CPU and calculating global information and a first probability based on the local information, where the first probability is the probability that a scatterer exists at the sampling point location as determined by the CPU; calculating a second probability based on the first probability and the local information through each receiving AP, where the second probability represents the probability that a scatterer exists at the sampling point location determined by the receiving AP, and the second probability is used as the probability distribution information for estimating the target gain accuracy in the next round of executing the first iteration logic; and updating the sampling point location based on the target gain estimation result and the common noise accuracy estimation result. The device may further include a second iteration logic execution module, which executes the second iteration logic. The second iteration logic execution module may include a local information calculation module 12, a global information calculation module 13, a second probability calculation module, and a sampling point location update module. Specifically, the local information calculation module 12 is used to calculate local information based on the estimation result of the target gain accuracy for each receiving AP. The global information calculation module 13 is specifically used to receive local information sent by each receiving AP through the CPU and calculate global information and a first probability based on the local information. The second probability calculation module is used to calculate a second probability based on the first probability and the local information through each receiving AP. The sampling point position update module is used to update the position of the sampling point based on the estimation result of the target gain and the estimation result of the common noise accuracy.

[0188] The scatterer position determination trigger module is used to trigger the scatterer position determination module to execute the step of determining the position of the sensed scatterer based on the global information when the second iterative logic satisfies the second preset convergence condition;

[0189] The sampling point location update module may specifically include:

[0190] A surrogate function construction module is used to construct a surrogate function based on the estimation results of the target gain and the estimation results of the common noise accuracy;

[0191] The sampling point update module is used to calculate the partial derivatives of the surrogate function with respect to the position of the sampling point in different dimensions, and update the position of the sampling point based on the partial derivatives.

[0192] The scatterer location determination module 14 is specifically used to extract target sampling points based on the global information, wherein the target sampling point is a sampling point where the probability of a scatterer on the sampling point determined by the CPU is greater than a preset threshold; and the location of the target sampling point is determined as the location of the sensed scatterer.

[0193] The first preset convergence condition is to reach a first preset number of iterations or the change in the estimation result between two adjacent iterations is less than a first preset threshold. The second preset convergence condition is to reach a second preset number of iterations or the estimation result between two adjacent iterations is less than a second preset threshold.

[0194] The probability model for the received signal of the receiving AP is determined based on the time delay and angle of arrival of the path passing through the sampling point, as well as the time delay and angle of arrival of the LOS path.

[0195] The device further includes a sampling point initial position determination module, which is used to arbitrarily select two receiving APs, estimate the time delay using the received signals of the two receiving APs, and obtain the estimated time delay; and determine the initial position of the sampling point based on the estimated time delay.

[0196] As can be seen, the embodiments of this application utilize a non-cellular system composed of multiple distributed APs and CPUs for sensing, thereby improving sensing performance. The probability model for receiving signals is determined based on the angle and time delay corresponding to the path passing through the sampling points. In the non-cellular system, joint multi-AP cooperative sensing is performed based on angle and time delay, avoiding misjudgment by a single AP and improving estimation accuracy. The angle and time delay have a geometric relationship with the position of the scattering object and can be used for localization. Furthermore, the estimation accuracy of the time delay is independent of the number of antennas, which reduces the requirement for the number of antennas on the AP, thereby reducing sensing costs. Based on the estimation of time delay, angle information is used to improve sensing performance.

[0197] Furthermore, embodiments of this application also disclose a computer program product, which, when executed, implements the aforementioned non-cellular system sensing method.

[0198] For details regarding the specific process of the above-mentioned non-cellular system sensing method, please refer to the relevant content disclosed in the foregoing embodiments, which will not be repeated here.

[0199] See Figure 7 As shown in the figure, this application discloses an electronic device 20, including a processor 21 and a memory 22; wherein, the memory 22 is used to store a computer program; the processor 21 is used to execute the computer program, the non-cellular system sensing method disclosed in the foregoing embodiment.

[0200] For details regarding the specific process of the above-mentioned non-cellular system sensing method, please refer to the relevant content disclosed in the foregoing embodiments, which will not be repeated here.

[0201] Furthermore, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk, or optical disk, and the storage method can be temporary storage or permanent storage.

[0202] In addition, the electronic device 20 also includes a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26; wherein, the power supply 23 is used to provide operating voltage for the various hardware devices on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0203] Furthermore, embodiments of this application also disclose a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the non-cellular system sensing method disclosed in the foregoing embodiments.

[0204] For details regarding the specific process of the above-mentioned non-cellular system sensing method, please refer to the relevant content disclosed in the foregoing embodiments, which will not be repeated here.

[0205] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0206] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0207] The foregoing has provided a detailed description of a non-cellular system sensing method, apparatus, product, equipment, and medium. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A sensing method for a cellular-free system, characterized in that, include: A probabilistic model of system parameters is established in a non-cellular system, wherein multiple distributed access points (APs) and CPUs connected to the multiple distributed APs are deployed in the non-cellular system, the multiple distributed APs include transmitting APs and multiple receiving APs, the system parameters include the received signals of any of the receiving APs, and the probabilistic model of the received signals of the receiving APs is determined based on the angle and time delay corresponding to the path passing through the sampling points. For each of the receiving APs, local information is calculated based on the probability model, wherein the local information includes the probability that the receiving AP determines that a scatterer exists at different sampling points; The CPU receives local information sent by each receiving AP and calculates global information based on the local information. The global information includes the probability that the CPU determines the presence of a scatterer at different sampling points. The location of the sensed scatterer is determined based on the global information; The method further includes: The first iteration logic is executed, wherein the first iteration logic includes: each receiving AP estimating the probability distribution information of local latent variables according to the variational Bayes principle, the local latent variables including: target gain and the accuracy of target gain, the target gain being a vector composed of the LOS path and the gain of the propagation path formed through different sampling points, and the accuracy of target gain being the accuracy of the gain of the corresponding LOS path and the propagation path formed through different sampling points; the CPU estimating the probability distribution information of the accuracy of common noise; When the first iterative logic satisfies the first preset convergence condition, the second iterative logic is executed. When the second iterative logic satisfies the second preset convergence condition, the step of determining the position of the perceived scatterer based on the global information is triggered. The second iterative logic includes: calculating local information based on the accuracy estimation result of the target gain for each receiving AP; receiving the local information sent by each receiving AP and calculating global information and a first probability based on the local information, wherein the first probability is the probability that a scatterer exists at the sampling point location as determined by the CPU; calculating a second probability based on the first probability and the local information for each receiving AP, wherein the second probability represents the probability that a scatterer exists at the sampling point location as determined by the receiving AP, and the second probability is used as the probability distribution information for estimating the accuracy of the target gain when executing the first iterative logic in the next round; and updating the location of the sampling point based on the target gain estimation result and the common noise accuracy estimation result.

2. The non-cellular system sensing method according to claim 1, characterized in that, The updating of the sampling point position based on the estimation results of the target gain and the estimation results of the common noise accuracy includes: A surrogate function is constructed based on the estimation results of the target gain and the estimation results of the common noise accuracy. Calculate the partial derivatives of the surrogate function with respect to the position of the sampling point in different dimensions, and update the position of the sampling point based on the partial derivatives.

3. The non-cellular system sensing method according to claim 2, characterized in that, Determining the location of the sensed scatterer based on the global information includes: Target sampling points are extracted based on the global information, wherein the target sampling points are sampling points on which the CPU determines that the probability of a scatterer is greater than a preset threshold. The location of the target sampling point is determined as the location of the sensed scatterer.

4. The non-cellular system sensing method according to claim 1, characterized in that, The first preset convergence condition is to reach a first preset number of iterations or the change in the estimation result between two adjacent iterations is less than a first preset threshold. The second preset convergence condition is to reach a second preset number of iterations or the estimation result between two adjacent iterations is less than a second preset threshold.

5. The non-cellular system sensing method according to claim 1, characterized in that, The probability model for the received signal of the receiving AP is determined based on the time delay and angle of arrival of the path passing through the sampling point, as well as the time delay and angle of arrival of the LOS path.

6. The non-cellular system sensing method according to any one of claims 1 to 5, characterized in that, Also includes: Arbitrarily select two receiving APs, and estimate the time delay using the received signals of the two receiving APs to obtain the estimated time delay; The initial position of the sampling point is determined based on the estimated time delay.

7. A non-cellular system sensing device, characterized in that, include: The probability model building module is used to build a probability model of system parameters in a non-cellular system. The non-cellular system is equipped with multiple distributed APs and CPUs connected to the multiple distributed APs. The multiple distributed APs include transmitting APs and multiple receiving APs. The system parameters include the received signals of any of the receiving APs. The probability model of the received signals of the receiving APs is determined based on the angle and time delay corresponding to the path passing through the sampling points. A local information calculation module is used to calculate local information based on the probability model for each of the receiving APs, wherein the local information includes the probability that the receiving AP determines that there is a scatterer at different sampling points; A global information calculation module is used to receive local information sent by each receiving AP through the CPU and calculate global information based on the local information, wherein the global information includes the probability that the CPU determines the presence of a scatterer at different sampling points; A scatterer position determination module is used to determine the position of the sensed scatterer based on the global information; The device is further configured to execute a first iterative logic, wherein the first iterative logic includes: each receiving AP estimating the probability distribution information of local latent variables according to the variational Bayesian principle, the local latent variables including: target gain and the accuracy of target gain, the target gain being a vector composed of the LOS path and the gain of the propagation path formed through different sampling points, the accuracy of target gain being the accuracy of the gain of the corresponding LOS path and the propagation path formed through different sampling points; and the CPU estimating the probability distribution information of the accuracy of common noise. When the first iterative logic satisfies the first preset convergence condition, the second iterative logic is executed. When the second iterative logic satisfies the second preset convergence condition, the step of determining the position of the perceived scatterer based on the global information is triggered. The second iterative logic includes: calculating local information based on the accuracy estimation result of the target gain for each receiving AP; receiving the local information sent by each receiving AP and calculating global information and a first probability based on the local information, wherein the first probability is the probability that a scatterer exists at the sampling point location as determined by the CPU; calculating a second probability based on the first probability and the local information for each receiving AP, wherein the second probability represents the probability that a scatterer exists at the sampling point location as determined by the receiving AP, and the second probability is used as the probability distribution information for estimating the accuracy of the target gain when executing the first iterative logic in the next round; and updating the location of the sampling point based on the target gain estimation result and the common noise accuracy estimation result.

8. A computer program product, characterized in that, When the computer program product is executed, it implements the non-cellular system sensing method as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, Includes memory and processor, wherein: The memory is used to store computer programs; The processor is configured to execute the computer program to implement the non-cellular system sensing method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements the non-cellular system sensing method as described in any one of claims 1 to 6.

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