Location sensing method, sensing device, network device, medium and program product

Abstract

The invention provides a position sensing method, a sensing device, network equipment, a medium and a program product, and relates to the technical field of wireless communication. The position sensing method comprises the following steps: acquiring corresponding sensing data and auxiliary sensing information sent by a plurality of sensing nodes, the sensing data being signal data of a target node based on sensing operation received by the sensing nodes on the target node based on a polarization sensitive array, the auxiliary sensing information is characteristic information of the polarization sensitive array; for each group of corresponding sensing data and auxiliary sensing information, converting the sensing data into a spatial spectrum intermediate matrix related to the target position of the target node based on the auxiliary sensing information; constructing a global spatial spectrum function based on maximum likelihood estimation by combining the spatial spectrum intermediate matrixes of the plurality of sensing nodes; and determining a target position based on the maximum likelihood solution of the global spatial spectrum function. According to the technical scheme, the sensing precision of the target position is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of wireless communication technologies, and in particular to a location sensing method, a location sensing apparatus, a network device, a computer-readable storage medium, and a computer program product. Background Art

[0002] In the process of evolving towards synaesthesia integration, improving positioning capabilities has become a key goal. However, current multi-station collaborative positioning mainly relies on each base station to estimate position parameters independently, which has obvious limitations in information processing and fusion, restricting the improvement of positioning performance. As a result, the positioning performance of the synaesthesia integration network needs to be improved.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0004] The purpose of the present disclosure is to provide a location perception method, configuration device, network device, storage medium and computer program product, which at least to some extent overcome the problem that the positioning performance of the synaesthesia integrated network in the related art needs to be improved.

[0005] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by practice of the present disclosure.

[0006] According to one aspect of the present disclosure, a position perception method is provided, comprising: obtaining corresponding perception data and auxiliary perception information respectively sent by a plurality of perception nodes, the perception data being signal data of the target node received based on a polarization-sensitive array as a result of the perception operation performed by the perception node on the target node, and the auxiliary perception information being characteristic information of the polarization-sensitive array; for each corresponding set of the perception data and the auxiliary perception information, converting the perception data into a spatial spectrum intermediate matrix associated with a target position of the target node based on the auxiliary perception information; constructing a global spatial spectrum function based on maximum likelihood estimation by combining the spatial spectrum intermediate matrices of the plurality of perception nodes; and determining the target position based on a maximum likelihood solution of the global spatial spectrum function.

[0007] In one embodiment of the present disclosure, for each corresponding set of the perception data and the auxiliary perception information, the perception data is converted into a spatial spectrum intermediate matrix related to the target location based on the auxiliary perception information, including: the auxiliary perception information includes spatial position information of multiple antennas included in the polarization-sensitive array and polarization information of the multiple antennas; a steering matrix for the target location is constructed based on the spatial position information; a transmit signal of the target node is modulated based on the polarization information to obtain a polarization modulated signal; and the spatial spectrum intermediate matrix of the perception data related to the target location is constructed based on the steering matrix, the polarization modulated signal, and the additive noise signal of the polarization-sensitive array.

[0008] In one embodiment of the present disclosure, a spatial spectrum intermediate matrix of the perception data related to the target position is obtained based on the steering matrix, the polarization modulation signal, and the additive noise signal of the polarization-sensitive array, including: constructing a mathematical expression of the perception data based on the steering matrix, the polarization modulation signal, and the additive noise signal; calculating a sample covariance matrix of the perception data based on the mathematical expression; and constructing the spatial spectrum intermediate matrix based on the steering matrix, the conjugate transpose of the steering matrix, and the sample covariance matrix.

[0009] In one embodiment of the present disclosure, the perception data is snapshot samples at multiple moments, and the sample covariance matrix is ​​determined by accumulating the mathematical expressions at the multiple moments and the conjugate transpose of the mathematical expressions.

[0010] In one embodiment of the present disclosure, the perception data is a snapshot sampled at multiple moments, and a mathematical expression of the perception data is constructed based on the steering matrix, the polarization modulation signal, and the additive noise signal, including: constructing the mathematical expression based on a first calculation formula, wherein the first calculation formula is Q n (x,y,z) is the steering matrix, is the polar modulation signal, n n (t k ) is the additive noise signal, (x, y, z) represents the target position, n represents the sequence number of the sensing node, t k Characterizes any of the aforementioned moments.

[0011] In one embodiment of the present disclosure, a steering matrix of the target position is constructed based on the spatial position information, including: constructing an array manifold matrix U and a direction matrix T of the target position based on the spatial position information, the array manifold matrix is ​​used to characterize the phase delay difference of the target position reaching the multiple antennas respectively, and the direction matrix is ​​used to characterize the projection relationship of the target position relative to the multiple antennas; based on the gain matrix W and the polarization sensitive matrix B corresponding to the polarization sensitive array, as well as the array manifold matrix U and the direction matrix T, the steering matrix is ​​constructed.

[0012] In one embodiment of the present disclosure, an array manifold matrix U of the target position is constructed based on the spatial position information, including: calculating the relative distance between the target position and the array center of the polarization-sensitive array; for each of the antennas in the polarization-sensitive array, calculating the phase delay factor of the antenna based on the relative distance; and constructing a diagonal matrix based on the phase delay factor to serve as the array manifold matrix.

[0013] In one embodiment of the present disclosure, the transmission signal of the target node is modulated based on the polarization information to obtain a polarization modulated signal, including: determining a polarization auxiliary angle and a polarization phase difference based on the polarization information; constructing a polarization modulation vector based on the polarization auxiliary angle and the polarization phase difference; and modulating the transmission signal based on the polarization modulation vector to obtain the polarization modulated signal.

[0014] In one embodiment of the present disclosure, constructing a polarization modulation vector based on the polarization auxiliary angle and the polarization phase difference includes: constructing the polarization modulation vector based on a second calculation formula, wherein the second calculation formula is γ n is the polarization auxiliary angle, η n is the polarization phase difference, and j is an imaginary unit.

[0015] In one embodiment of the present disclosure, the spatial spectrum intermediate matrices of the plurality of sensing nodes are combined to construct a global spatial spectrum function based on maximum likelihood estimation, including: performing a trace operation on the spatial spectrum intermediate matrix of each sensing node to obtain a corresponding single-node spatial spectrum expression; and combining the single-node spatial spectrum expressions of the plurality of sensing nodes to obtain a global spatial spectrum expression to construct the global spatial spectrum function based on maximum likelihood estimation.

[0016] In one embodiment of the present disclosure, the target position is determined based on the maximum likelihood solution of the global spatial spectrum function, including: dividing the perception target area where the target node is located into multiple grids; traversing the multiple grids to find the maximum likelihood solution of the global spatial spectrum function; and determining the coordinates of the grid with the maximum likelihood solution as the target position.

[0017] According to another aspect of the present disclosure, a position sensing device is provided, comprising: an acquisition module for acquiring corresponding sensing data and auxiliary sensing information respectively sent by a plurality of sensing nodes, the sensing data being signal data of the target node based on the sensing operation performed by the sensing node on the target node and received based on a polarization-sensitive array, and the auxiliary sensing information being feature information of the polarization-sensitive array; a conversion module for converting, for each set of corresponding sensing data and the auxiliary sensing information, the sensing data into a spatial spectrum intermediate matrix related to the target position of the target node based on the auxiliary sensing information; a construction module for jointly constructing a global spatial spectrum function based on maximum likelihood estimation with the spatial spectrum intermediate matrices of the plurality of sensing nodes; and a determination module for determining the target position based on the maximum likelihood solution of the global spatial spectrum function.

[0018] According to another aspect of the present disclosure, a network device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; the processor is configured to execute the location awareness method of the first aspect above by executing the executable instructions.

[0019] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned location awareness method is implemented.

[0020] According to another aspect of the present disclosure, a computer program product is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned location awareness method is implemented.

[0021] The position sensing solution provided by the embodiments of the present disclosure obtains sensing data and auxiliary sensing information from multiple sensing nodes, first converts each set of data into a spatial spectrum intermediate matrix related to the target position, then jointly constructs a global spatial spectrum function and determines the target position based on the extreme value of the maximum likelihood estimate. By fusing information from multiple sensing nodes, the limitations of single-node information processing are overcome, the impact of local observation errors or interference on positioning results is reduced, and the synergy between the polarization characteristics of the polarization-sensitive array and spatial position information is utilized to improve the perception accuracy of the target position.

[0022] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0024] Figure 1 A flow chart of a location awareness method according to an embodiment of the present disclosure is shown;

[0025] Figure 2 A flow chart of another location awareness method according to an embodiment of the present disclosure is shown;

[0026] Figure 3 A flow chart of another location awareness method according to an embodiment of the present disclosure is shown;

[0027] Figure 4 A flow chart of another location awareness method according to an embodiment of the present disclosure is shown;

[0028] Figure 5 A schematic diagram illustrating a location awareness solution in an embodiment of the present disclosure is shown;

[0029] Figure 6 A schematic diagram illustrating another location awareness solution in an embodiment of the present disclosure is shown;

[0030] Figure 7 A schematic diagram of a position sensing device according to an embodiment of the present disclosure is shown;

[0031] Figure 8 A structural block diagram of a computer device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0032] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0033] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0034] In the evolution toward integrated interawareness, optimizing air interfaces and architecture design has become a key focus. Multi-station collaborative sensing, as a key means of improving positioning accuracy, has attracted considerable attention. Currently, multi-station collaborative positioning generally employs an indirect positioning approach: each base station independently estimates intermediate position parameters such as distance, velocity, and angle, then combines its own coordinates to solve for the target location through geometric relationships. This result-level fusion approach has significant limitations: each base station retains only the most probable estimate from its own inference, discarding other, less probable, but still containing target location information. This results in a significant amount of valid information being wasted before fusion. Due to the lack of sufficient information exchange and collaboration between stations, this hard fusion approach is not only inefficient but also fails to fully tap the potential of multi-station collaboration, hindering further improvements in positioning performance. The resulting technical challenge is how to achieve data-level fusion of multi-station sensing information to prevent information loss caused by intermediate variable estimation in indirect positioning, thereby improving the positioning accuracy and efficiency of multi-station collaborative sensing.

[0035] Below, each step of the location sensing method in this example implementation will be described in more detail with reference to the accompanying drawings and embodiments.

[0036] Figure 1 A flow chart of a location awareness method in an embodiment of the present disclosure is shown.

[0037] like Figure 1 As shown, a location awareness method according to an embodiment of the present disclosure includes:

[0038] Step S102: Acquire corresponding perception data and auxiliary perception information sent by each of the plurality of perception nodes. The perception data is signal data of the target node based on the perception operation received by the perception node based on the polarization-sensitive array when the perception node performs a perception operation on the target node. The auxiliary perception information is characteristic information of the polarization-sensitive array.

[0039] In some embodiments, the auxiliary sensing information refers to the inherent parameters of the polarization-sensitive array, which are used to describe the physical characteristics of the array, including but not limited to: the spatial position information of each antenna in the array, which is used to calculate the phase difference of the signal arriving at different antennas; the polarization information of each antenna, such as the polarization direction angle and the polarization response matrix, which is used to characterize the antenna's reception characteristics for electromagnetic waves in different polarization states; and hardware-related parameters, which are used to compensate for the non-ideality of the antenna or circuit.

[0040] In some embodiments, a polarization-sensitive array refers to a sensor array composed of multiple antennas with polarization resolution capabilities, each of which can sense the polarization state of electromagnetic waves, thereby improving positioning accuracy by simultaneously utilizing the spatial position information and polarization characteristics of the signal.

[0041] Step S104 : for each set of corresponding perception data and auxiliary perception information, convert the perception data into a spatial spectrum intermediate matrix related to the target position of the target node based on the auxiliary perception information.

[0042] In some embodiments, the spatial spectrum intermediate matrix is ​​an intermediate transition matrix model used to associate target position, signal polarization characteristics, array spatial characteristics and noise information during multi-node sensing and target positioning.

[0043] In some embodiments, the perception data is converted into a spatial spectrum intermediate matrix based on auxiliary perception information. The auxiliary perception information provides inherent characteristics of the polarization-sensitive array. Based on these characteristics, the propagation patterns of signals reaching the array from different spatial locations can be determined. Combined with information such as signal strength and phase contained in the perception data itself, the original perception data can be converted into an intermediate matrix that can reflect the characteristics of the target location.

[0044] Step S106: Combining the spatial spectrum intermediate matrices of multiple sensing nodes to construct a global spatial spectrum function based on maximum likelihood estimation.

[0045] In some embodiments, a spatial spectrum function that reflects global observation patterns is constructed by integrating the spatial spectrum intermediate matrices output by multiple perception nodes and the association information of each node with the target position. Each spatial spectrum intermediate matrix includes the association characteristics between the corresponding node perception data and the hypothetical target position. These matrices are combined to fuse the local association information of multiple nodes into a global metric, thereby achieving quantization processing of the spatial spectrum intermediate matrix of each node, and then integrating the quantization results into a function related to the target position.

[0046] In some embodiments, maximum likelihood estimation refers to finding the target position that best matches all observed data. The global spatial spectrum function integrates information from multiple nodes so that the size of the function value can reflect the overall possibility of a certain position as the target position. The extreme point finally determined corresponds to the optimal estimated position under the maximum likelihood sense.

[0047] Step S108 : determining the target position based on the maximum likelihood solution of the global spatial spectrum function.

[0048] In some embodiments, by traversing the spatial area where the target may exist, calculating the global spectrum function value corresponding to each candidate position, and taking the position of the function extreme value as the final estimated position of the target, mapping from spatial spectrum to physical position is achieved.

[0049] In this embodiment, by acquiring perception data and auxiliary perception information from multiple perception nodes, each set of data is first converted into a spatial spectrum intermediate matrix related to the target position. A global spatial spectrum function is then jointly constructed, and the target position is determined based on the extreme value of the maximum likelihood estimate. By fusing information from multiple perception nodes, the limitations of single-node information processing are overcome, the impact of local observation errors or interference on positioning results is reduced, and the synergy between the polarization characteristics of the polarization-sensitive array and the spatial position information is utilized to improve the perception accuracy of the target position.

[0050] like Figure 2 As shown, in one embodiment of the present disclosure, for each set of corresponding perception data and auxiliary perception information, the perception data is converted into a spatial spectrum intermediate matrix related to the target position based on the auxiliary perception information, including:

[0051] Step S202: The auxiliary sensing information includes spatial position information of multiple antennas included in the polarization sensitive array and polarization information of the multiple antennas, and a steering matrix of the target position is constructed based on the spatial position information.

[0052] In some embodiments, the spatial position information of multiple antennas in a polarization-sensitive array is utilized, combined with the relative orientation and distance between the target position and each antenna, to construct a steering matrix that can reflect the spatial propagation characteristics when propagating from the target position to different antennas, so as to convert the spatial position information of the target into a quantifiable mathematical form.

[0053] Step S204: modulate the transmission signal of the target node based on the polarization information to obtain a polarization modulated signal.

[0054] In some embodiments, the polarization state of the signal transmitted by the target node is characterized using the polarization information of multiple antennas to achieve mathematical modulation processing of the transmitted signal so that the modulated signal can reflect the polarization characteristics of the original signal, thereby integrating the polarization information into the subsequent spatial spectrum function.

[0055] Step S206 : constructing a spatial spectrum intermediate matrix of the perception data related to the target position based on the steering matrix, the polarization modulation signal, and the additive noise signal of the polarization sensitive array.

[0056] In some embodiments, the steering matrix depicts the phase difference of the signal propagating from the target position to each antenna. The polarization modulation signal is generated based on the polarization information of the antenna and describes how the polarization state of the signal is received by the array. The additive noise signal represents random interference during the reception process, and its statistical characteristics affect the overall distribution of the received signal. The three are combined through mathematical operations to generate a spatial spectrum intermediate matrix. The element values ​​of this matrix change with the target position, so as to further construct a global spatial spectrum function based on the spatial spectrum intermediate matrix.

[0057] In this embodiment, the spatial position information of the polarization-sensitive array is used to construct a steering matrix to capture spatial features. The signal is modulated with polarization information to incorporate polarization features. Then, based on these elements, an intermediate spatial spectrum matrix related to the target position is constructed. This achieves the fusion of target position information, polarization information, and signal statistical characteristics. This overcomes the limitation of multi-station collaborative positioning that relies on each base station to estimate position parameters independently, and can more comprehensively utilize the effective information in the perception data, thereby improving the positioning performance of the integrated synesthesia network.

[0058] like Figure 3 As shown, in one embodiment of the present disclosure, based on the steering matrix, the polarization modulation signal, and the additive noise signal of the polarization sensitive array, the spatial spectrum intermediate matrix of the target position-related perception data includes:

[0059] Step S302: construct a mathematical expression of the perception data based on the steering matrix, the polarization modulation signal, and the additive noise signal.

[0060] In one embodiment of the present disclosure, the perception data is snapshot samples at multiple moments, and a mathematical expression of the perception data is constructed based on a steering matrix, a polarization modulation signal, and an additive noise signal, including:

[0061] A mathematical expression is constructed based on the first calculation formula, wherein the first calculation formula is shown in formula (1).

[0062]

[0063] Among them, Q n (x,y,z) is the orientation matrix, is the polarization modulation signal, n n (t k ) is the additive noise signal, (x, y, z) represents the target position, n represents the sequence number of the sensing node, t k Represents any moment.

[0064] Mathematical expression is used to decompose the perception data into the superposition of spatial features, polarization features and noise features, and establish a mathematical association between the perception data and the target location.

[0065] Step S304: Calculate the sample covariance matrix of the perception data based on the mathematical expression.

[0066] Among them, the sample covariance matrix describes the second-order statistical characteristics of the perception data, including the comprehensive information of perception data, noise and interference.

[0067] In one embodiment of the present disclosure, the perception data is snapshot samples at multiple moments, and the sample covariance matrix is ​​determined by the mathematical expressions at multiple moments and the conjugate transpose accumulation of the mathematical expressions, as shown in formula (2).

[0068]

[0069] Step S306 : constructing a spatial spectrum intermediate matrix based on the steering matrix, the conjugate transpose of the steering matrix, and the sample covariance matrix.

[0070] In one embodiment of the present disclosure, a spatial spectrum intermediate matrix is ​​constructed based on a steering matrix, a conjugate transpose of the steering matrix, and a sample covariance matrix, as shown in formula (3).

[0071]

[0072] In this embodiment, a mathematical model of the perception data is established using a steering matrix, a polarization modulation signal, and an additive noise signal to describe the composition of the perception data. Based on this model, a sample covariance matrix of the perception data is calculated to statistically analyze the second-order characteristics of the perception data. Finally, the product of the steering matrix and its conjugate transpose is used to reflect the spatial projection characteristics of the target position. This product is multiplied by the covariance matrix. The resulting spatial spectrum intermediate matrix changes with changes in the target position. This allows the target position, polarization characteristics, and signal statistical characteristics to be associated with the same matrix. This allows for more comprehensive mining of effective positioning information in the perception data, thereby improving the accuracy and reliability of target position estimation.

[0073] In one embodiment of the present disclosure, a steering matrix Q of the target position is constructed based on the spatial position information. n ,include:

[0074] The array manifold matrix U and direction matrix T of the target position are constructed based on the spatial position information. The array manifold matrix is ​​used to represent the phase delay difference of the target position reaching multiple antennas respectively, and the direction matrix is ​​used to represent the projection relationship of the target position relative to multiple antennas.

[0075] In some embodiments, based on the relative relationship between the spatial position information of multiple antennas and the target position, the distance difference of the signal from the target position to each antenna is calculated, and the distance difference is converted into a phase delay. The array manifold matrix U n (x,y,z).

[0076] In some embodiments, the direction matrix T is constructed based on the geometric parameters such as the azimuth and elevation of the target position in the array coordinate system. n (x, y, z) to describe the projection relationship of the target position in different antenna directions, thereby converting the spatial position information of the target into a quantifiable matrix form. The direction matrix T n (x, y, z) is as shown in formula (4).

[0077]

[0078] Based on the gain matrix W corresponding to the polarization sensitive array n and polarization sensitivity matrix B n , and the array manifold matrix U n (x,y,z) and direction matrix T n (x,y,z), construct the steering matrix Q n .

[0079] in, is the gain matrix corresponding to the receiving array of the nth node, where the diagonal elements are used to compensate for the channel mismatch error of the corresponding antenna, g and respectively characterize the amplitude and phase offset; when there is no channel mismatch error, W is the M-order unit matrix.

[0080] B n ∈R M×2 is the polarization sensitivity matrix, where the mth row vector is [cosα m ,sinα m ].

[0081] In some embodiments, the gain matrix W n Used to compensate for the amplitude and phase inconsistency of each channel of the array, the polarization sensitivity matrix B n Used to describe the response characteristics of the antenna to signals in different polarization states, these two matrices are combined with the constructed array manifold matrix U n (x,y,z) and direction matrix T n (x, y, z) performs cascade multiplication operations, as shown in formula (5), so that the steering matrix Q n It can reflect the spatial propagation characteristics of the signal, as well as the array's ability to perceive the signal's polarization state and the hardware channel characteristics, thereby constructing a matrix that can characterize the target position, spatial propagation, polarization characteristics, and hardware response.

[0082] Q n =W n U n (x,y,z)B n T n (x,y,z) (5)

[0083] In this embodiment, the spatial geometric information of the target position is decomposed into an array manifold matrix and a direction matrix, and a steering matrix is ​​constructed by combining the gain matrix and the polarization sensitivity matrix. This achieves a multi-dimensional integration of target position information, signal spatial propagation characteristics, polarization characteristics, and hardware characteristics. This enables the steering matrix to more comprehensively describe the characteristics of the perception data, thereby improving the correlation accuracy between the perception data and the target position, and enhancing the sensitivity of the subsequent spatial spectrum intermediate matrix and the global spatial spectrum function to the target position.

[0084] In one embodiment of the present disclosure, constructing an array manifold matrix U of the target position based on spatial position information includes:

[0085] The relative distance between the target position and the array center of the polarization-sensitive array is calculated; for each antenna in the polarization-sensitive array, a phase delay factor of the antenna is calculated based on the relative distance; and a diagonal matrix is ​​constructed based on the phase delay factor to serve as an array manifold matrix.

[0086] In some embodiments, the relative distance between the perceived target position and the center of the polarization-sensitive array is calculated. This distance determines the difference in the propagation path of the signal reaching the array. Because the path lengths of the signal reaching different antennas are different, a phase difference will be generated. For each antenna in the array, the phase delay factor is calculated based on the relative distance, and the phase delay factor of each antenna is used as a diagonal element to construct a diagonal matrix, namely, the array manifold matrix U. This matrix is ​​essentially a mathematical representation of the spatial phase characteristics of the target position in the polarization-sensitive array, providing a basis for subsequent target positioning through array signal processing.

[0087] In some embodiments, the array manifold matrix describes the phase difference of the signals received by each antenna when the signal at the target position (x, y, z) in space is incident on the nth polarization-sensitive array (comprising M antennas). The array manifold matrix is ​​a main diagonal matrix with dimension M×M, that is, only the diagonal elements are non-zero, and each diagonal element corresponds to the phase response of an antenna. The non-diagonal elements are 0, and the mth diagonal element is the phase delay factor when the mth antenna receives the target signal, as shown in the following formula (6).

[0088]

[0089] Among them, j represents the imaginary unit, (x, y, z) is the coordinate of the target position, (x n ,y n ,z n ) is the spatial coordinate of the receiving array of node n, vector [xx n ,yy n ,zz n ] represents the reference position vector from the array reference point to the target position, r m is the element position vector of the mth antenna relative to the array reference point, [xxn ,yy n ,zz n ]r m Then it means r m The projection length on the reference position vector, λ is the signal wavelength.

[0090] In this embodiment, the phase delay factor is calculated by relative distance and an array manifold matrix in the form of a diagonal matrix is ​​constructed to convert the spatial information of the target position into a quantifiable phase characteristic matrix. This realizes the mapping between the spatial position and the phase characteristics of the array signal, so that the matrix can reflect the spatial orientation characteristics of the target in the polarization-sensitive array, thereby enhancing the polarization-sensitive array's ability to resolve the target's spatial position.

[0091] In one embodiment of the present disclosure, modulating a transmit signal of a target node based on polarization information to obtain a polarization modulated signal includes:

[0092] A polarization auxiliary angle and a polarization phase difference are determined based on polarization information; a polarization modulation vector is constructed based on the polarization auxiliary angle and the polarization phase difference; and a transmission signal is modulated based on the polarization modulation vector to obtain a polarization modulated signal.

[0093] In some embodiments, the polarization assist angle γ n Used to describe the inclination angle of the electromagnetic wave polarization ellipse, polarization phase difference η n Used to describe the phase offset between two orthogonal polarization components.

[0094] In one embodiment of the present disclosure, constructing a polarization modulation vector based on the polarization auxiliary angle and the polarization phase difference includes: constructing a polarization modulation vector based on a second calculation formula, wherein the second calculation formula is shown in formula (7).

[0095]

[0096] γ n is the polarization auxiliary angle, η n is the polarization phase difference, j is the imaginary unit, T represents the transpose of the matrix, that is, the row vector is converted into a column vector. In polarization signal processing, the polarization auxiliary angle γ n and polarization phase difference η n Represented as a column vector, it facilitates subsequent operations such as matrix multiplication with array manifold matrices.

[0097] In one embodiment of the present disclosure, it is assumed that the target node's transmitted signal is a scalar s n (t k ), the polar modulation signal can be expressed as shown in formula (8).

[0098]

[0099] This is equivalent to converting the original signal s n (t k ) is decomposed into the superposition of two orthogonal polarization components, namely s n (t k )cosγ n and

[0100] In this embodiment, when the signal reaches the polarization-sensitive array, each antenna in the array selectively receives the two components based on its own polarization response characteristics, carrying the polarization information of the target, thereby enabling the receiving array to simultaneously utilize the spatial and polarization characteristics of the signal to improve positioning accuracy and anti-interference capabilities.

[0101] In one embodiment of the present disclosure, a global spatial spectrum function based on maximum likelihood estimation is constructed by combining spatial spectrum intermediate matrices of multiple sensing nodes, including:

[0102] The spatial spectrum intermediate matrix of each sensing node is traced to obtain the corresponding single-node spatial spectrum expression.

[0103] In some embodiments, a trace operation is performed on the intermediate spatial spectrum matrix of each node to convert the matrix into a scalar to obtain the spatial spectrum value of a single node, that is, tr{P n (x,y,z)R n}.

[0104] The single-node spatial spectrum expressions of multiple sensing nodes are combined to obtain the global spatial spectrum expression to construct the global spatial spectrum function based on maximum likelihood estimation, as shown in Equation (9).

[0105]

[0106] In some embodiments, spatial spectrum estimation is used to locate the orientation (such as angle, distance or three-dimensional coordinates) of a signal source in space. In a distributed sensing array scenario, the observation range of a single node is limited or susceptible to interference, so it is necessary to combine the information of multiple nodes.

[0107] In some embodiments, maximum likelihood estimation (MLE) refers to finding parameter values, ie, spatial parameters, that maximize the probability of the observed data occurring.

[0108] In this embodiment, the single-node spatial spectrum values ​​of all nodes are summed to obtain the global spatial spectrum function. Since the core of maximum likelihood estimation is to find the target position that is most likely to generate all observation data, the global spectrum function integrates the perception information of different positions by accumulating the matching degree of multiple nodes, combining the observation diversity of distributed nodes and the statistical optimality of maximum likelihood estimation, thereby achieving high-precision positioning in complex environments.

[0109] In one embodiment of the present disclosure, determining the target position based on the maximum likelihood solution of the global spatial spectrum function includes:

[0110] The perception target area where the target node is located is divided into multiple grids; the multiple grids are traversed to find the maximum likelihood solution of the global spatial spectrum function; and the coordinates of the grid with the maximum likelihood solution are determined as the target position.

[0111] In this embodiment, according to the range of the perception target area and the positioning accuracy requirements, the area is divided into several discrete grids, each grid represents a possible target position candidate point, and the size of the grid determines the positioning resolution. For each grid point, the global spatial spectrum function is substituted into the corresponding function value to calculate the corresponding function value, which reflects the overall matching degree between the target at the grid point and the observation data of all perception nodes. By traversing all grid points and comparing the function values, the grid point that makes the global spatial spectrum function reach the maximum value, that is, the position corresponding to the maximum likelihood solution, is found. Finally, the coordinates of the grid point are used as the estimated result of the target position. Under the premise of ensuring the positioning resolution, the target position that meets the global observation data can be found, thereby improving the efficiency and reliability of target positioning, and providing a feasible implementation path for high-precision positioning of the synaesthesia integrated network.

[0112] like Figure 4 As shown, a location awareness method according to another embodiment of the present disclosure includes:

[0113] In step S402, the plurality of sensing nodes respectively perform sensing operations, and each sensing node uses a polarization array composed of a plurality of polarization-sensitive antennas to receive a sensing signal and obtain sensing data.

[0114] For each sensing node, the sensing data includes the received data of multiple antennas on the polarization array. The received data of each antenna is a snapshot sample at multiple moments, which can be expressed as Among them, x n (t k )∈C M×1 is an M-dimensional column vector, M is the number of array antennas, and K is the number of samples.

[0115] In step S404, the plurality of sensing nodes transmit their respective received data to the control plane network element, and at the same time transmit their respective auxiliary sensing information to the control plane network element.

[0116] Among them, for each sensing node, auxiliary sensing information is used to describe the spatial position of each antenna in the array that receives sensing data, as well as the polarization information of each antenna. Through the auxiliary sensing information, the relative position, orientation and polarization properties of each antenna in the array can be obtained.

[0117] Step S406: The control plane network element obtains a spatial spectrum based on a signal processing method, and then obtains a perception result.

[0118] In some embodiments, the array of sensing nodes n is arranged at t k Received data at time x n (t k ) is expressed as formula (10).

[0119] x n (t k )=W n U n (x,y,z)B n T n (x,y,z)h n (γ n ,η n )s n (t k )+n n (t k ) (10)

[0120] Among them, x n (t k )∈C M×1 is an M-dimensional column vector, where M is the number of array antennas, x, y, z represent the coordinate position components of the perceived target, and γ n , η n represent the polarization auxiliary angle and polarization phase difference of the receiving array relative to node n, s n (t)∈C M×1 and n n (t)∈C M×1 They represent the transmitted signal and additive noise respectively.

[0121] U n (x,y,z)∈C M×M is an array manifold matrix, which is a main diagonal matrix, where the mth main diagonal element is:

[0122]

[0123] Among them, (x n ,y n ,z n ) is the spatial coordinate of the receiving array of node n.

[0124] B n ∈R M×2 is the polarization sensitivity matrix, where the mth row vector is [cosα m ,sinα m ].

[0125]

[0126] Among them, the array position is fixed during perception, so T n Determined by the target location.

[0127] It reflects the polarization auxiliary angle and polarization phase difference information.

[0128] The array of sensor node n is set at t k Received data at time x n (t k ) is reformulated as formula (1).

[0129]

[0130] Among them, Q n =W n U n (x,y,z)B n T n (x,y,z) is determined only by the perceived target space position, is the signal part that contains polarization information.

[0131] remember At the same time The array receiving sample covariance matrix of node n is combined with the sample data of all nodes to construct the following maximum likelihood spatial spectrum estimation expression, namely, equation (9).

[0132]

[0133] Furthermore, the perception target area of ​​interest is gridded, and the grid points in the area are traversed. The spatial spectrum is obtained according to the maximum likelihood spatial spectrum estimation expression, and then the maximum likelihood solution of the perception target position coordinates is obtained, where: is the estimated target position.

[0134] In addition, if the height of the perceived target is fixed, z does not need to be estimated, and the solution disclosed in the present invention is converted into a direct positioning method in a two-dimensional plane.

[0135] like Figure 5 As shown in the figure, the circular logo represents the sensing nodes participating in the collaboration, and the trajectory curve is the target node, such as the actual motion path of a mobile terminal, drone, etc., which is the object that needs to be located.

[0136] The latitude and longitude coordinates of the nodes (31°12′30″N~31°14′30″N, 121°27′E~121°29′E) determine their spatial geometric relationship.

[0137] like Figure 6 As shown, the horizontal axis (x) and vertical axis (y) are plane coordinates in meters, covering Figure 5 The vertical column on the right represents the amplitude value of the spatial spectrum function, which increases gradually from bottom to top. The larger the value, the higher the probability that the target is located at that point.

[0138] Figure 6 It is the visualization of the global spatial spectrum function. The specific implementation process includes:

[0139] Each node calculates the single-node spatial spectrum based on the received signal.

[0140] The spatial spectra of the three nodes are combined to obtain the global spectrum.

[0141] The global spectrum is mapped to plane coordinates to form a color heat map.

[0142] The brightest area in the spectrum corresponds to the maximum likelihood solution of the global spatial spectrum function. Figure 5 From the actual trajectory in the figure, we can see that the spectrum peak and the trajectory curve are highly consistent, indicating that the three-node collaboration has successfully captured the target's motion path.

[0143] In this embodiment, the visual positioning process based on the spatial spectrum heat map transforms abstract mathematical operations (maximum likelihood estimation, spatial spectrum combination) into intuitive peak matching, realizing perceptual positioning. That is, target coordinates are directly estimated by jointly processing sample data measured by each perception node. This is applicable to polarization-sensitive arrays and can fully utilize polarization information to obtain multi-station joint angle-only maximum likelihood spatial spectrum estimation. The multi-node data-level fusion direct positioning solution can significantly improve positioning performance and prevent the unnecessary loss of information caused by indirect positioning methods.

[0144] It should be noted that the above figures are merely illustrative of the processes included in the methods according to exemplary embodiments of the present disclosure and are not intended to be limiting. It is readily understood that the processes illustrated in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0145] Refer to the following Figure 7 1 and 2 to describe the position sensing device 700 according to an embodiment of the present invention. Figure 7 The position sensing device 700 shown is merely an example and should not limit the functionality and scope of use of the embodiments of the present invention.

[0146] The location sensing device 700 is implemented as a hardware module. The components of the location sensing device 700 may include, but are not limited to: an acquisition module 702 for acquiring corresponding sensing data and auxiliary sensing information sent by multiple sensing nodes, wherein the sensing data is signal data of the target node based on the sensing operation performed by the sensing node on the target node and received based on the polarization-sensitive array; and the auxiliary sensing information is characteristic information of the polarization-sensitive array; a conversion module 704 for converting each set of corresponding sensing data and auxiliary sensing information into a spatial spectrum intermediate matrix associated with the target location of the target node based on the auxiliary sensing information; a construction module 706 for combining the spatial spectrum intermediate matrices of multiple sensing nodes to construct a global spatial spectrum function based on maximum likelihood estimation; and a determination module 708 for determining the target location based on the maximum likelihood solution of the global spatial spectrum function.

[0147] Those skilled in the art will appreciate that various aspects of the present invention may be implemented as systems, methods, or program products. Therefore, various aspects of the present invention may be implemented in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits," "modules," or "systems."

[0148] Refer to the following Figure 8 The network device 800 according to this embodiment of the present invention is described. Figure 8 The network device 800 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0149] like Figure 8 As shown, the network device 800 is implemented as a general-purpose computing device. Components of the network device 800 may include, but are not limited to, the at least one processing unit 810, the at least one storage unit 820, and a bus 830 connecting different system components (including the storage unit 820 and the processing unit 810).

[0150] The storage unit stores program codes that can be executed by the processing unit 810, so that the processing unit 810 performs the steps according to various exemplary embodiments of the present invention described in the "Exemplary Method" section above. For example, the processing unit 810 may perform the following steps: Figure 1 The described scheme.

[0151] The storage unit 820 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 8201 and / or a cache memory unit 8202 , and may further include a read-only memory unit (ROM) 8203 .

[0152] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0153] Bus 830 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0154] The network device 800 may also communicate with one or more external devices 870 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the network device 800, and / or any device that enables the network device 800 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication may occur via an input / output (I / O) interface 850. Furthermore, the network device 800 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 860. As shown, the network adapter 860 communicates with the other modules of the network device 800 via a bus 830. It should be understood that, although not shown, other hardware and / or software modules may be used in conjunction with the network device 800, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0155] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0156] In exemplary embodiments of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above-described methods of this specification is stored. In some possible implementations, various aspects of the present invention may also be implemented in the form of a program product, which includes program code. When the program product is executed on an electronic device, the program code is used to cause the electronic device to perform the steps according to various exemplary embodiments of the present invention described in the "Exemplary Methods" section of this specification.

[0157] According to an embodiment of the present invention, a program product for implementing the above-mentioned method can be a portable compact disc read-only memory (CD-ROM) and include program code, and can be run on an electronic device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0158] The program product may be implemented in any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0159] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0160] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0161] The program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0162] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0163] Furthermore, although the steps of the method of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in this particular order, or that all steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0164] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0165] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the appended claims.

Claims

1. A location awareness method, characterized in that: Applicable to control plane network elements, including: Acquiring corresponding sensing data and auxiliary sensing information sent by each of the plurality of sensing nodes, wherein the sensing data is signal data of the target node received based on a polarization-sensitive array after the sensing node performs a sensing operation on the target node, and the auxiliary sensing information is characteristic information of the polarization-sensitive array; For each set of corresponding perception data and auxiliary perception information, converting the perception data into a spatial spectrum intermediate matrix related to a target position of the target node based on the auxiliary perception information; Combining the spatial spectrum intermediate matrices of the plurality of sensing nodes to construct a global spatial spectrum function based on maximum likelihood estimation; The target position is determined based on a maximum likelihood solution of the global spatial spectral function.

2. The location sensing method according to claim 1, wherein: For each set of corresponding perception data and auxiliary perception information, converting the perception data into a spatial spectrum intermediate matrix related to the target position based on the auxiliary perception information includes: The auxiliary sensing information includes spatial position information of multiple antennas included in the polarization sensitive array and polarization information of the multiple antennas, and a steering matrix of the target position is constructed based on the spatial position information; Modulating a transmission signal of the target node based on the polarization information to obtain a polarization modulated signal; An intermediate spatial spectrum matrix of the sensing data associated with the target position is constructed based on the steering matrix, the polarization modulation signal, and the additive noise signal of the polarization sensitive array.

3. The location sensing method according to claim 2, wherein: The spatial spectrum intermediate matrix of the sensing data related to the target position based on the steering matrix, the polarization modulation signal and the additive noise signal of the polarization sensitive array includes: constructing a mathematical representation of the perception data based on the steering matrix, the polarization modulation signal, and the additive noise signal; Calculating a sample covariance matrix of the perception data based on the mathematical expression; The spatial spectrum intermediate matrix is ​​constructed based on the steering matrix, the conjugate transpose of the steering matrix, and the sample covariance matrix.

4. The location sensing method according to claim 3, wherein: The perception data is snapshot samples at multiple moments, and the sample covariance matrix is ​​determined by accumulating the mathematical expressions at the multiple moments and the conjugate transpose of the mathematical expressions.

5. The location sensing method according to claim 2, wherein: The perception data is snapshot samples at multiple moments, and a mathematical expression of the perception data is constructed based on the steering matrix, the polarization modulation signal, and the additive noise signal, including: The mathematical expression is constructed based on the first calculation formula, wherein the first calculation formula is x n (t k )= x n (t k ) is the mathematical expression, Q n (x,y,z) is the steering matrix, is the polar modulation signal, n n (t k ) is the additive noise signal, (x, y, z) represents the target position, n represents the sequence number of the sensing node, t k Characterizes any of the aforementioned moments.

6. The location sensing method according to claim 2, characterized in that: Constructing a guidance matrix for the target position based on the spatial position information includes: Constructing an array manifold matrix U and a direction matrix T of the target position based on the spatial position information, wherein the array manifold matrix is ​​used to represent the phase delay difference of the target position reaching the multiple antennas respectively, and the direction matrix is ​​used to represent the projection relationship of the target position relative to the multiple antennas; The steering matrix is ​​constructed based on the gain matrix W and the polarization sensitive matrix B corresponding to the polarization sensitive array, the array manifold matrix U and the direction matrix T.

7. The location sensing method according to claim 6, characterized in that: Constructing an array manifold matrix U of the target position based on the spatial position information, including: Calculating the relative distance between the target position and the array center of the polarization sensitive array; For each of the antennas in the polarization-sensitive array, calculating a phase delay factor of the antenna based on the relative distance; A diagonal matrix is ​​constructed based on the phase delay factors to serve as the array manifold matrix.

8. The location sensing method according to claim 2, characterized in that: Modulating the transmit signal of the target node based on the polarization information to obtain a polarization modulated signal includes: Determining a polarization auxiliary angle and a polarization phase difference based on the polarization information; Constructing a polarization modulation vector based on the polarization auxiliary angle and the polarization phase difference; The transmit signal is modulated based on the polarization modulation vector to obtain the polarization modulation signal.

9. The location sensing method according to claim 8, characterized in that: Constructing a polarization modulation vector based on the polarization auxiliary angle and the polarization phase difference, comprising: The polarization modulation vector is constructed based on the second calculation formula, wherein the second calculation formula is γ n is the polarization auxiliary angle, η n is the polarization phase difference, and j is an imaginary unit.

10. The location sensing method according to claim 1, wherein: Combining the spatial spectrum intermediate matrices of the plurality of sensing nodes to construct a global spatial spectrum function based on maximum likelihood estimation includes: Performing a trace operation on the spatial spectrum intermediate matrix of each sensing node to obtain a corresponding single-node spatial spectrum expression; The single-node spatial spectrum expressions of the plurality of sensing nodes are combined to obtain a global spatial spectrum expression, so as to construct the global spatial spectrum function based on maximum likelihood estimation.

11. The location sensing method according to claim 1, characterized in that: Determining the target position based on a maximum likelihood solution of the global spatial spectrum function includes: Dividing the sensing target area where the target node is located into multiple grids; traversing the plurality of grids to find the maximum likelihood solution of the global spatial spectrum function; The coordinates of the grid having the maximum likelihood solution are determined as the target position.

12. A position sensing device, characterized in that: Applicable to control plane network elements, including: an acquisition module, configured to acquire corresponding perception data and auxiliary perception information sent by each of the plurality of perception nodes, wherein the perception data is signal data of the target node received based on the perception operation performed by the perception node on the target node based on the polarization-sensitive array, and the auxiliary perception information is characteristic information of the polarization-sensitive array; a conversion module, configured to convert, for each corresponding set of the perception data and the auxiliary perception information, the perception data into a spatial spectrum intermediate matrix associated with a target position of the target node based on the auxiliary perception information; A construction module, configured to combine the spatial spectrum intermediate matrices of the plurality of sensing nodes to construct a global spatial spectrum function based on maximum likelihood estimation; A determination module is used to determine the target position based on a maximum likelihood solution of the global spatial spectrum function.

13. A network device, characterized in that: include: processor; as well as a memory for storing executable instructions of the processor; The processor is configured to execute the location awareness method according to any one of claims 1 to 11 by executing the executable instructions.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the position sensing method according to any one of claims 1 to 11 is implemented.

15. A computer program product having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the position sensing method according to any one of claims 1 to 11 is implemented.

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