A method for estimating joint range and spatial time delay of ultra-massive MIMO near field

Abstract

This invention discloses a near-field spatial-time delay joint domain distance estimation method for ultra-large-scale MIMO, relating to the fields of wireless sensing and array signal processing technology. This method utilizes the propagation characteristics of near-field spherical waves to establish a spatial steering vector, and combines multi-subcarrier time delay phase information to construct a spatial-time delay joint domain model. A joint distance search dictionary is generated based on the distance resolutions in the spatial and time delay domains, and the target distance is estimated using an orthogonal matching pursuit algorithm. This invention defines resolution thresholds to distinguish the dominant regions of spatial and time delay domain resolutions and constructs a segmented joint distance search dictionary. This invention can simultaneously leverage the spatial resolution capability of near-field spherical waves and the time delay resolution capability of multi-subcarriers, avoiding the bandwidth limitation problem caused by relying solely on the time delay domain and the array size problem caused by relying solely on the spatial domain, thereby improving multi-target range resolution and target distance estimation accuracy.

Description

Technical Field

[0001] This invention relates to the field of wireless sensing and array signal processing technology, and in particular to a method for near-field spatial delay joint domain distance estimation for ultra-large-scale MIMO. Background Technology

[0002] With the large-scale deployment of fifth-generation (5G) mobile communication networks and the development of 6G communication technology research, the integration of communication systems and wireless sensing functions has become an important development direction for future wireless networks. Utilizing Orthogonal Frequency Division Multiplexing (OFDM) signals to simultaneously achieve data transmission and target sensing can improve the utilization efficiency of spectrum and hardware resources, and provide fundamental support for applications such as intelligent transportation, low-altitude sensing, environmental sensing, and high-precision positioning. In OFDM sensing systems, target distance is typically obtained by estimating the propagation delay of the echo signal; its distance resolution is mainly determined by the system bandwidth, and can be expressed as:

[0003]

[0004] in, For signal propagation speed, For the number of subcarriers, Let be the subcarrier spacing. Therefore, when the available bandwidth of the system is limited or the number of subcarriers is small, the traditional time-delay domain distance estimation method has low distance resolution, making it difficult to distinguish multiple targets that are close together. This can easily lead to multiple targets falling into the same distance cell and failing to be effectively distinguished.

[0005] On the other hand, with the development of higher frequency communication technologies such as millimeter waves and terahertz, and the adoption of large-scale and ultra-large-scale antenna array technologies to compensate for high-frequency path loss, the array aperture is constantly increasing, making it easier for targets to be located in the near-field region of the array. According to the near-field range formula... The increased antenna aperture brought about by ultra-large-scale arrays and the shorter wavelengths resulting from millimeter waves both increase the near-field range of the array. In the near-field range, the traditional uniform plane wave assumption is no longer applicable, and channel modeling needs to consider more realistic uniform or non-uniform spherical wave models, which introduces new near-field characteristics to the channel.

[0006] In the far-field model, the array steering vector can be written as:

[0007]

[0008] in For array spacing, For the number of array elements, From the perspective of the target, The signal wavelength is denoted by . Since the array's steering vector depends only on the target's angle, it is impossible to simultaneously estimate the target's angle and range using array signal processing. However, in the near field, under the assumption of a uniform spherical wave, the distance difference between the target and different array elements depends on both the target's angle and range. The spatial steering vector contains not only angle information but also range-related phase information. At this point, the distance difference between the target and the [number] array element is denoted by . The propagation distance of each array element can be expressed as:

[0009]

[0010] The expression for the steering vector becomes: The corresponding near-field phase term can be expressed as

[0011]

[0012] The resulting near-field spatial steering vector is simultaneously related to both the target distance and the target angle. Therefore, the near-field spherical wave propagation characteristics of VMI arrays provide a new possibility for target distance estimation using spatial domain information.

[0013] In addition to time-delay domain range resolution, the spatial domain can also provide range resolution under near-field VMI (Very Large Scale) antenna array conditions. Unlike the far-field plane wave model, in the near-field spherical wave model, the propagation distance difference from the target to different array elements is related to both the target angle and the target distance. Therefore, the spatial steering vector contains not only angular information but also range-related phase information. Thus, whether two targets at similar distances can be distinguished in the spatial domain can be measured by the correlation between their near-field spatial steering vectors.

[0014] Specifically, for those located at the same target angle Direction and distance are respectively and The two objectives can be represented by the magnitude of the inner product of their spatial steering vectors, which can be used as a measure of spatial domain distance discriminability.

[0015]

[0016] in, and They represent distances of 1 and 2 respectively. and The near-field spatial steering vector at that time. When the correlation is less than a preset correlation threshold, the two distance sampling points are considered to be distinguishable in the spatial domain. Based on the near-field spherical wave phase term, the above correlation can be approximately expressed as a correlation with the parameter... The relevant functions, where:

[0017]

[0018] By setting a resolution threshold This allows us to determine the distinguishability conditions between adjacent distance sampling points in the spatial domain, and to construct spatial domain distance sampling points accordingly:

[0019]

[0020] in:

[0021]

[0022] Therefore, the interval between two adjacent spatial domains and sampling points can be expressed as:

[0023]

[0024] Therefore, the near-field spatial domain range resolution differs from the time-delay domain range resolution. It is not a fixed quantity determined by system bandwidth, but rather related to array size, element spacing, signal wavelength, target angle, and target distance. Generally, the closer the target, the more pronounced the spatial phase nonlinearity caused by near-field spherical waves, allowing the spatial domain to provide finer range sampling intervals. Conversely, the farther the target, the larger the spatial domain range sampling interval, leading to a decrease in spatial domain range resolution. Thus, spatial domain range resolution and time-delay domain range resolution are complementary in different range regions, providing a basis for constructing a piecewise joint range search dictionary based on resolution thresholds.

[0025] Existing range estimation methods typically process data in either the time-delay domain or the spatial domain. Time-delay domain methods rely heavily on the bandwidth of the orthogonal frequency division multiplexing (OFDM) signal, resulting in insufficient range resolution under limited bandwidth conditions. While spatial domain methods can utilize range-related information from the near-field spatial steering vector, their estimation performance is affected by factors such as array size, target distance, and target angle. When array size is limited or the target distance is large, the spatial domain range resolution degrades. Therefore, relying solely on information from a single domain makes it difficult to achieve good range estimation performance across different target distance regions and under varying system resource conditions.

[0026] Based on the above analysis, existing positioning methods struggle to simultaneously address the limitations of traditional time-delay domain methods in terms of insufficient range resolution under bandwidth constraints, and the limitations of spatial domain methods in terms of estimation performance when array size is limited or target distance varies. To resolve these issues, it is necessary to propose a joint spatial-temporal domain range estimation method to improve the range resolution and estimation accuracy for multiple targets in the near field and at different ranges. Summary of the Invention

[0027] The technical problem this invention aims to solve is to overcome the shortcomings of existing technologies and provide a joint spatial-temporal domain range estimation method for ultra-large-scale MIMO. This method addresses the limitations of traditional delay-domain range estimation methods in terms of range resolution under bandwidth constraints, and the performance limitations of standalone spatial-domain range estimation methods when array size is limited or target distance varies. This method constructs a joint spatial-temporal domain model by jointly utilizing the near-field spatial resolution of an ultra-large-scale antenna array and the delay-domain resolution of orthogonal frequency division multiplexing (OFDM) signals. Based on the spatial and delay-domain range resolutions, a joint range search dictionary is generated, thereby improving the multi-target range resolution and estimation accuracy under limited bandwidth and array size conditions.

[0028] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0029] A method for joint domain distance estimation of near-field spatial and temporal delay in ultra-large-scale MIMO proposed according to the present invention includes:

[0030] Step 1: Construct a space-time delay joint domain distance estimation model using the steering vector of the ultra-large-scale array with near-field characteristics and the multi-subcarrier delay phase information;

[0031] Step 2: Based on the spatial-temporal joint domain distance estimation model and according to the distance resolution characteristics of the near-field spatial domain and temporal domain, generate a piecewise joint distance search dictionary;

[0032] Step 3: Based on the segmented joint distance search dictionary and the spatial delay joint domain received signal obtained from the echo signal, the orthogonal matching pursuit algorithm is applied to the spatial delay joint domain received signal to obtain the target distance estimation result.

[0033] As a further optimization of the near-field spatial-temporal joint domain distance estimation method for ultra-large-scale MIMO described in this invention, the ultra-large-scale array steering vector is within the near-field Rayleigh distance range. It also includes angular and range phase information for estimating the location of environmental scatterers. The distance between the midpoint in space and the array. For array size, The wavelength is the signal wavelength.

[0034] As a further optimization of the near-field spatial delay joint domain distance estimation method for ultra-large-scale MIMO described in this invention, near-field characteristics and multi-subcarrier delay phase information are used to estimate the distance to the target.

[0035] As a further optimization of the near-field spatial-time delay joint domain distance estimation method of ultra-large-scale MIMO described in this invention, the spatial-time delay joint domain distance estimation model is jointly constructed by the spatial steering vector and the time delay domain steering vector. It is used to simultaneously describe the scatterer distance phase change information of the received signal in the spatial domain and the time delay domain, and to realize the target distance estimation by utilizing the near-field spatial domain and time delay domain distance resolution.

[0036] As a further optimization of the near-field spatial-temporal joint domain distance estimation method for ultra-large-scale MIMO described in this invention, the piecewise joint distance search dictionary includes: determining a resolution threshold based on the size between the near-field spatial domain distance resolution and the temporal domain distance resolution, and determining the switching distance between spatial domain distance sampling and temporal domain distance sampling based on the resolution threshold; generating non-uniform distance sampling points based on the near-field spatial domain distance resolution in the distance region within the switching distance, and generating uniform distance sampling points based on the temporal domain distance resolution in the distance region outside the switching distance, and constructing the piecewise joint distance search dictionary from all the non-uniform and uniform distance sampling points.

[0037] As a further optimization of the joint spatial delay domain distance estimation method for ultra-large-scale MIMO near-field spatial delay described in this invention, the orthogonal matching pursuit algorithm constructs candidate joint spatial delay domain guiding atoms according to the joint distance search dictionary, and uses the ultra-large-scale MIMO channel matrix as the initial residual to calculate the correlation between the candidate joint spatial delay domain guiding atoms and the current residual, and selects the candidate distance sampling point with the highest correlation to include in the support set as a preliminary estimate of the target distance.

[0038] The support set is used to estimate the ultra-large-scale MIMO channel, and the estimated ultra-large-scale MIMO channel is subtracted from the initial residual to update the current residual;

[0039] The residuals are iteratively updated until all target distances are estimated.

[0040] As a further optimization scheme of the near-field spatial-temporal joint domain distance estimation method for ultra-large-scale MIMO described in this invention, after obtaining the preliminary estimated target distance, a small-step distance search dictionary is generated within the range of adjacent distance sampling points for each preliminary estimated target distance. Then, the distance with the highest correlation to the ultra-large-scale MIMO channel matrix is ​​obtained using the matched filtering method, which is used as the distance estimation result.

[0041] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0042] (1) This invention combines the near-field spatial resolution capability of ultra-large scale arrays and the time-delay domain resolution capability of orthogonal frequency division multiplexing signals. Compared with the time-delay domain distance estimation method alone, it can alleviate the problem of insufficient distance resolution under bandwidth-limited conditions.

[0043] (2) This invention utilizes the propagation characteristics of near-field spherical waves to establish a spatial steering vector related to the target angle and target distance. Compared with the traditional far-field plane wave model, it can extract spatial phase information related to the target distance under near-field conditions.

[0044] (3) The invention generates a joint distance search dictionary based on the distance resolution of the spatial domain and the time delay domain, so that the near distance area can make full use of the non-uniform distance sampling in the spatial domain and the far distance area can make full use of the uniform distance sampling in the time delay domain, thereby adapting to the resolution changes of different target distance areas.

[0045] (4) The present invention uses the orthogonal matching pursuit algorithm to perform target distance estimation in the joint spatial and temporal domain, which can improve the multi-target distance resolution capability and reduce the impact of target distance estimation performance degradation under limited bandwidth or limited array size conditions.

[0046] (5) The present invention is applicable to target distance estimation in near-field ultra-large-scale antenna array orthogonal frequency division multiplexing system, and can improve the target distance estimation accuracy and system sensing performance without significantly increasing the system bandwidth or array size. Attached Figure Description

[0047] Figure 1 This is a flowchart of a joint spatial delay domain distance estimation method for near-field ultra-large-scale antenna array orthogonal frequency division multiplexing systems, according to an embodiment of the present invention.

[0048] Figure 2 This is a schematic diagram of a near-field ultra-large-scale antenna array orthogonal frequency division multiplexing system scenario and near-field steering vector modeling provided in an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram illustrating the construction of the joint spatial delay domain model provided in an embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram illustrating the generation of the joint distance search dictionary provided in an embodiment of the present invention;

[0051] Figure 5 This is a flowchart of orthogonal matching tracking distance estimation provided in an embodiment of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] Figure 1 This is a flowchart illustrating a spatial delay joint domain distance estimation method for near-field ultra-large-scale antenna array orthogonal frequency division multiplexing systems, according to an embodiment of the present invention.

[0054] like Figure 1 As shown, the spatial delay joint domain distance estimation method for near-field ultra-large-scale antenna array orthogonal frequency division multiplexing systems includes the following steps:

[0055] S101, a space-time delay joint domain distance estimation model is constructed using the steering vector of a large-scale array with near-field characteristics and the time delay phase information of multiple subcarriers;

[0056] S102, Based on the spatial-temporal joint domain distance estimation model and according to the distance resolution characteristics of the near-field spatial domain and temporal domain, a piecewise joint distance search dictionary is generated;

[0057] S103, based on the segmented joint distance search dictionary and the spatial delay joint domain received signal obtained from the echo signal, the orthogonal matching pursuit algorithm is applied to the spatial delay joint domain received signal to obtain the target distance estimation result.

[0058] In step S101, the base station uses a very large-scale antenna array (MSA) to receive the target echo signal. Because the MSA has a large aperture, the target is more likely to be located in the near-field region of the array.

[0059] Figure 2 This is a schematic diagram illustrating the near-field ultra-large-scale antenna array orthogonal frequency division multiplexing system scenario and near-field steering vector modeling provided in an embodiment of the present invention. Figure 2 As shown, the base station is equipped with a very large-scale antenna array (VMA), which is used to transmit and receive orthogonal frequency division multiplexing (OFDM) signals. If one or more targets exist in the area to be sensed, the OFDM signals transmitted by the base station are reflected or scattered by the targets and then received by the base station's receiving array.

[0060] In this embodiment, the base station receiving array is assumed to be a uniform linear array with the number of array elements being... The spacing between array elements is The signal wavelength is Let the first... The location of the target is The distance from the target to the array reference point is The target angle is ,in , The target quantity.

[0061] In step S101, a spatial steering vector is first established using the propagation characteristics of near-field spherical waves.

[0062] like Figure 2 As shown, the first The goal is to reach the first The propagation distance of each array element is denoted as Because the target is located in the near field region, Not only with the From the perspective of each goal Related to, and also related to the Distance to each target Relevant. Let the first... The position index of each array element relative to the array reference point is: Then the first The goal is to reach the first The propagation distance of each array element can be expressed as:

[0063]

[0064] Based on the difference in propagation distance from the target to different array elements, the first... Near-field spatial steering vectors corresponding to each target:

[0065]

[0066] in, The natural base, For located The The corresponding ultra-large-scale array steering vector for each target This indicates the transpose. As shown in the above expression, the near-field spatial steering vector contains both target angle and target range information. Therefore, the near-field characteristics of a very large-scale antenna array can be used to extract target range-related phase information from the spatial domain.

[0067] Secondly, a time-delay domain steering vector is established using the multi-subcarrier time-delay phase information of the orthogonal frequency division multiplexing signal.

[0068] Suppose that the orthogonal frequency division multiplexed signal includes Subcarriers and orthogonal frequency division multiplexing symbols, subcarrier spacing is . The system bandwidth is For single-station sensing scenarios, the first... The echo propagation delay of a target can be expressed as: , Let be the waveform propagation speed. Since different subcarriers have different frequencies, the propagation delay of the same target will produce different phase changes on different subcarriers. Therefore, the th... The time-delay domain steering vector corresponding to each target can be expressed as:

[0069]

[0070] in, This indicates the phase change of multi-subcarrier delay caused by the target distance.

[0071] Figure 3This is a schematic diagram illustrating the construction of a joint spatial-temporal domain model provided in an embodiment of the present invention. Figure 3 As shown, after receiving the target echo signal, the base station establishes a spatial steering vector based on the near-field spherical wave propagation characteristics and a time-delay domain steering vector based on the multi-subcarrier delay phase information. Then, the spatial steering vector and the time-delay domain steering vector are combined to obtain the spatial-time-delay joint domain steering vector.

[0072]

[0073] in, The spatial-temporal joint domain guiding vector, It represents the Kronecker product.

[0074] The base station processes the received signal, combining the array element dimension and the subcarrier dimension to obtain the received signal in the joint spatial-temporal domain. For signals containing... In a scenario with multiple targets, the joint spatial-temporal domain reception model can be represented as:

[0075]

[0076] in, For the spatial-temporal joint domain received signal matrix, For the spatial-temporal joint domain steering matrix, The target equivalent signal matrix, This is the noise matrix.

[0077] The joint domain steering matrix of spatial delay can be expressed as:

[0078]

[0079] Through the above processing, the target distance information is simultaneously represented in the spatial domain and the temporal domain. The target distance estimation problem is transformed into the problem of searching for the target distance parameter in the joint spatial-temporal domain steering matrix.

[0080] In step S102, a segmented joint distance search dictionary is generated based on the spatial-temporal joint domain distance estimation model and according to the distance resolution characteristics of the near-field spatial domain and temporal domain.

[0081] Figure 4 This is a schematic diagram illustrating the generation of the joint distance search dictionary provided in an embodiment of the present invention. Figure 4 As shown, this invention does not employ spatial domain distance search alone, nor does it employ temporal delay domain distance search alone. Instead, it generates a joint distance search dictionary based on the relationship between the magnitudes of the two distance resolutions.

[0082] First, calculate the time-delay domain distance resolution. For an orthogonal frequency division multiplexing (OFDM) system, the time-delay domain distance resolution... Primarily determined by system bandwidth, it can be expressed as:

[0083]

[0084] As can be seen from this formula, when the system bandwidth is limited or the number of subcarriers is small, the time delay domain distance resolution is low, and it is difficult to effectively distinguish multiple targets that are close to each other.

[0085] Secondly, the spatial domain range sampling points are determined. Under near-field conditions, changes in target distance cause phase changes in the spatial steering vector, thus the spatial domain can provide range resolution. Since the phase change caused by spherical waves varies non-linearly with target distance, a non-uniform sampling method is used for the spatial domain range sampling points.

[0086] In one specific embodiment, spatial domain candidate distance sampling points can be generated as follows:

[0087]

[0088] in, For candidate distance sampling points in the spatial domain, For sampling sequence number, The target direction or the direction to be searched. Distance sampling parameters are related to array size, element spacing, signal wavelength, and preset correlation threshold.

[0089] Then, the distance interval between adjacent spatial domain candidate distance sampling points is calculated, and this distance interval is compared with the time delay domain distance resolution. Compare them.

[0090] When the interval between adjacent candidate distance sampling points in the spatial domain is less than the distance resolution in the time delay domain, it indicates that the spatial domain can provide higher distance resolution within this distance region. Therefore, non-uniform distance sampling in the spatial domain is adopted.

[0091] When the interval between adjacent candidate distance sampling points in the spatial domain is greater than or equal to the distance resolution in the time delay domain, it indicates that the time delay domain can provide a distance resolution capability no less than that in the spatial domain within this distance region. Therefore, uniform distance sampling in the time delay domain is adopted.

[0092] This allows us to determine the switching distance between spatial domain sampling and time-delay domain sampling. At or near the switching distance In the near-field region, spatial domain non-uniform distance sampling is used; in areas greater than the switching distance... For long-distance regions, time-delay domain uniform distance sampling is adopted, with a time-delay domain uniform sampling interval of . .

[0093] Finally, by combining the non-uniform distance sampling points in the spatial domain and the uniform distance sampling points in the time delay domain, a joint distance search set is obtained. :

[0094]

[0095] in, The number of distance sampling points in the joint distance search set. For the first The value of each distance sampling point, ;

[0096] Based on the aforementioned joint distance search set, a joint distance search dictionary can be constructed:

[0097]

[0098] in, For joint distance search dictionary, For the first distance sampling point and the target direction or the direction to be searched The ultra-large-scale array steering vector. If the target direction is unknown, a joint search of angle and distance can be performed; if the target direction is known or has been obtained through angle estimation methods, a joint distance search dictionary can be constructed for the corresponding target direction.

[0099] Step S102 allows the near-field resolution capability of the spatial domain to be fully utilized in the near-range region, and the stable resolution capability of the time-delay domain to be fully utilized in the far-range region, thereby improving the adaptability of the joint search dictionary at different target distances.

[0100] In step S103, the spatial-temporal delay joint domain received signal is obtained based on the segmented joint distance search dictionary and the echo signal, and the target distance estimation result is obtained by performing an orthogonal matching pursuit algorithm on the spatial-temporal delay joint domain received signal.

[0101] Figure 5 This is a flowchart illustrating the orthogonal matching tracking distance estimation process provided in an embodiment of the present invention. Figure 5 As shown, the input to the orthogonal matching pursuit algorithm includes the received signal matrix in the joint spatial-temporal domain. and joint distance search dictionary .

[0102] First, initialize the residual matrix. Set as:

[0103]

[0104] Initialize the support set to an empty set and initialize the number of iterations. .

[0105] Then, in the... In each iteration, the candidate spatial delay joint domain-guided atoms in the joint distance search dictionary and the current residual are calculated sequentially. The correlation between them. For the first candidate distances Its corresponding correlation It can be represented as:

[0106]

[0107] in, This indicates the conjugate transpose. This represents the L2 norm.

[0108] Next, select the candidate distance with the highest relevance. As a coarse estimate of the distance for the current iteration:

[0109]

[0110] To further improve distance estimation accuracy, a fine search is performed within the neighborhood of the coarse estimated distance after obtaining the coarse estimated distance. The fine search can be performed with a smaller search step size between the coarse estimated distance and its neighboring sampling points, or it can be performed within a preset distance range centered on the coarse estimated distance. The correlation of candidate distances within the fine search range is recalculated, and the fine search point with the highest correlation is selected as the corrected distance estimate. .

[0111] Subsequently, the spatial-temporal delay joint domain-guided atoms corresponding to the corrected distance estimates are added to the support set. Let the first... The guidance matrix corresponding to the support set after the next iteration is: Then, the least squares method is used to estimate the target equivalent signal. :

[0112]

[0113] Then, update the residuals based on the estimated equivalent signal. .

[0114] Repeatedly perform correlation calculation, coarse distance estimation, fine neighborhood search, support set update, equivalent signal estimation, and residual update until a preset termination condition is met. The preset termination condition may be reaching a preset target number, residual energy falling below a preset threshold, or the number of iterations reaching a preset maximum value.

[0115] After the iteration is complete, output the target distance estimation result:

[0116]

[0117] in, Indicates the first Distance estimates for each target.

[0118] The complete execution process of this embodiment will be further explained below.

[0119] First, a joint spatial-temporal domain distance estimation model is constructed using the near-field characteristics of the ultra-large-scale array steering vector and multi-subcarrier delay phase information. Specifically, the ultra-large-scale array steering vector simultaneously contains both angle and range phase information within the near-field Rayleigh distance range, and the near-field spatial phase information and multi-subcarrier delay phase information are used to estimate the target distance.

[0120] Secondly, the spatial-temporal joint domain target distance estimation model is constructed by the spatial steering vector and the temporal-delay domain steering vector. It is used to simultaneously describe the target distance phase change information of the received signal in the spatial domain and the temporal-delay domain, and to realize target distance estimation by utilizing the near-field spatial domain and temporal-delay domain distance resolution.

[0121] Next, based on the aforementioned spatial-temporal joint domain target distance estimation model, and according to the distance resolution characteristics of the near-field spatial domain and the temporal delay domain, a piecewise joint distance search dictionary is generated. The piecewise joint distance search dictionary includes: determining a resolution threshold based on the magnitude between the near-field spatial domain distance resolution and the temporal delay domain distance resolution, and determining the switching distance between spatial domain distance sampling and temporal delay domain distance sampling based on the resolution threshold; generating non-uniform distance sampling points based on the near-field spatial domain distance resolution within the distance region within the switching distance; generating uniform distance sampling points based on the temporal delay domain distance resolution within the distance region outside the switching distance; and constructing the piecewise joint distance search dictionary from all the non-uniform and uniform distance sampling points.

[0122] Then, based on the segmented joint distance search dictionary, and the spatial delay joint domain received signal is obtained from the echo signal; the target distance is estimated by the orthogonal matching pursuit algorithm on the spatial delay joint domain received signal obtained from the echo signal, and the target distance estimation result is obtained.

[0123] Specifically, the orthogonal matching pursuit algorithm constructs candidate spatial-delay joint domain guiding atoms based on a piecewise joint distance search dictionary, and uses the ultra-large-scale MIMO channel matrix as the initial residual. It calculates the correlation between the candidate spatial-delay joint domain guiding atoms and the current residual, selects the candidate distance sampling point with the highest correlation and includes it in the support set as a preliminary estimate of the target distance. It uses the support set to estimate the ultra-large-scale MIMO channel, and subtracts the estimated ultra-large-scale MIMO channel from the initial residual to update the current residual. It iteratively updates the residual until the target distance is estimated.

[0124] Finally, after obtaining the preliminary estimated target distance, a small-step distance search dictionary is generated within the range of adjacent distance sampling points for each preliminary estimated target distance. Then, the matched filtering method is used to obtain the distance with the highest correlation to the ultra-large-scale MIMO channel matrix, which is used as the target distance estimation result.

[0125] The spatial-temporal joint domain range estimation method for near-field ultra-large-scale antenna array orthogonal frequency division multiplexing (OFDM) systems provided by embodiments of the present invention constructs a spatial-temporal joint domain model by jointly utilizing spatial domain range-related phase information generated by near-field spherical wave propagation and multi-subcarrier delay phase information generated by OFDM signals; generates a joint range search dictionary by comparing spatial domain and time-delay domain range resolutions; and achieves target range estimation through an orthogonal matched pursuit algorithm. This invention addresses the problems of insufficient range resolution in traditional time-delay domain range estimation methods under bandwidth constraints, and the limited estimation performance of standalone spatial domain range estimation methods when array size is limited or target distance varies, thereby improving the multi-target range resolution and estimation accuracy under limited bandwidth and limited array size conditions.

[0126] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0127] Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance, or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0128] Any process or method description in the flowchart or otherwise herein can be understood to represent one or more processing steps for implementing a particular logical function or process step, and the scope of embodiments of the invention includes other implementations in which functions may not be performed in the order shown or discussed, including performing functions substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0129] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for joint spatial-temporal domain distance estimation in near-field MIMO, characterized in that, include: Step 1: Construct a space-time delay joint domain distance estimation model using the steering vector of the ultra-large-scale array with near-field characteristics and the multi-subcarrier delay phase information; Step 2: Based on the spatial-temporal joint domain distance estimation model and according to the distance resolution characteristics of the near-field spatial domain and temporal domain, generate a piecewise joint distance search dictionary; Step 3: Based on the segmented joint distance search dictionary and the spatial delay joint domain received signal obtained from the echo signal, the orthogonal matching pursuit algorithm is applied to the spatial delay joint domain received signal to obtain the target distance estimation result.

2. The method for joint spatial-temporal domain distance estimation of ultra-large-scale MIMO near-field as described in claim 1, characterized in that, The steering vector of a very large-scale array within the near-field Rayleigh distance range It also includes angular and range phase information for estimating the location of environmental scatterers. The distance between the midpoint in space and the array. For array size, The wavelength is the signal wavelength.

3. The method for joint spatial-temporal distance estimation in the near-field domain of ultra-large-scale MIMO according to claim 1, characterized in that, Near-field characteristics and multi-subcarrier delay phase information are used to estimate the target's range.

4. The method for joint spatial-temporal distance estimation in the near-field domain of ultra-large-scale MIMO according to claim 1, characterized in that, The spatial-temporal-delay joint domain range estimation model is constructed by combining the spatial steering vector and the temporal-delay domain steering vector. It is used to simultaneously describe the scatterer range phase change information of the received signal in both the spatial and temporal domains, and to achieve target range estimation by utilizing the near-field spatial domain and temporal-delay domain range resolution.

5. The method for joint spatial-temporal distance estimation in the near-field domain of ultra-large-scale MIMO according to claim 1, characterized in that, The segmented joint distance search dictionary includes: determining a resolution threshold based on the size between the near-field spatial domain distance resolution and the time-delay domain distance resolution, and determining the switching distance between spatial domain distance sampling and time-delay domain distance sampling based on the resolution threshold; generating non-uniform distance sampling points based on the near-field spatial domain distance resolution in the distance region within the switching distance, and generating uniform distance sampling points based on the time-delay domain distance resolution in the distance region outside the switching distance, and constructing the segmented joint distance search dictionary from all the non-uniform and uniform distance sampling points.

6. The method for joint spatial-temporal domain distance estimation of ultra-large-scale MIMO near-field as described in claim 1, characterized in that, The orthogonal matching pursuit algorithm constructs candidate spatial-time delay joint domain guiding atoms based on the joint distance search dictionary, and uses the ultra-large-scale MIMO channel matrix as the initial residual to calculate the correlation between the candidate spatial-time delay joint domain guiding atoms and the current residual. The candidate distance sampling point with the highest correlation is selected and included in the support set as a preliminary estimate of the target distance. The support set is used to estimate the ultra-large-scale MIMO channel, and the estimated ultra-large-scale MIMO channel is subtracted from the initial residual to update the current residual; The residuals are iteratively updated until all target distances are estimated.

7. The method for joint spatial-temporal distance estimation in the near-field domain of ultra-large-scale MIMO according to claim 6, characterized in that, After obtaining the initial estimated target distance, a small-step distance search dictionary is generated within the range of adjacent distance sampling points for each initial estimated target distance. Then, the matched filtering method is used to obtain the distance with the highest correlation to the ultra-large-scale MIMO channel matrix, which is used as the distance estimation result.

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