Non-line-of-sight link sensing integrated positioning method based on channel knowledge map

Abstract

The invention discloses a non-line-of-sight link sensing integrated positioning method based on a channel knowledge map (CKM), and the method comprises the steps: an offline stage: constructing the CKM in a target region, storing the multipath angle-time delay characteristics of each reference position and the equivalent scatterer position mapped by the multipath angle-time delay characteristics, and forming an environmental geometry priori database; in the online stage, a base station extracts a multipath arrival angle and arrival time delay of an uplink signal of a to-be-positioned user, power spectrum similarity matching with CKM is carried out to obtain a coarse positioning result, and then high-confidence scatterer prior is obtained through path-level fine matching; and finally, constructing and solving a weighted nonlinear least square optimization model fusing geometric consistency constraint and prior constraint, and jointly outputting the accurate position of the user terminal and the position of the main scatterer in the environment. According to the method, the prior information provided by the CKM is utilized, the problems of equation underdetermination and fingerprint fuzziness in single-base-station non-line-of-sight positioning are effectively solved, and the positioning precision and the environment perception capability are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of communication technology, and specifically relates to a non-line-of-sight link sensing integrated positioning method based on channel knowledge map. Background Technology

[0002] The sixth-generation (6G) mobile communication network is entrusted with the important mission of realizing Integrated Sensing and Communication (ISAC). Relying on ultra-high bandwidth and ultra-large-scale antenna arrays, it enables wireless signals to have higher resolution in the time and angle domains, allowing existing communication infrastructure to be expanded into a high-precision environmental sensing and positioning platform. High-precision integrated sensing and communication wireless positioning services have significant application prospects in fields such as intelligent navigation, emergency response, rescue, and low-altitude economy. Against this backdrop, the research objective of this invention is to achieve high-precision positioning in complex urban non-line-of-sight environments, reliably sensing the user's location and environmental geometry using only a single base station's uplink communication.

[0003] Localization and environmental awareness are core capabilities of integrated communication and sensing systems. However, in non-line-of-sight scenarios, traditional single-base station localization methods relying on geometric constraints are often difficult to apply directly. Taking the uplink single-transmit multiple-receive (SIMO) scenario as an example, although the base station can estimate the angle of arrival (AoA) and time of arrival (ToA) of each multipath through array processing, the lack of array antennas on the user terminal side and the unobservable transmission angle and other information lead to more geometric unknowns than available equations, resulting in a typical underdetermined problem. On the other hand, fingerprint localization methods perform similarity matching based on features such as channel state information (CSI) or received signal strength (RSS). However, in complex urban environments with strong multipath propagation and highly symmetrical scene layouts, the phenomenon of "multi-point co-image" with similar fingerprints at multiple locations easily occurs, leading to localization ambiguity and drift, thus limiting robustness. Therefore, whether relying solely on instantaneous angle-delay observations or fingerprint matching alone, achieving stable, interpretable, and high-precision localization under single-base station non-line-of-sight conditions still faces significant challenges.

[0004] To address these issues, the key lies in transforming complex multipath propagation from a traditionally perceived interference factor into a useful information carrier for improving positioning and sensing performance. This means transforming a "positioning problem relying solely on instantaneous observations" into a "geometric solution constrained by prior environmental knowledge." Following this line of thought, the concept of a Channel Knowledge Map (CKM) has been proposed in recent years. CKM pre-organizes and uniformly stores the multipath structure and channel characteristics of each location within a region under typical conditions into a spatial knowledge base. On one hand, it explicitly preserves the correspondence between "location and channel characteristics"; on the other hand, it implicitly characterizes the distribution and geometry of major scatterers in the scene through these multipath features. During the positioning phase, CKM can implicitly provide the locations of candidate environmental scatterers, while the base station estimates the angle of arrival and arrival delay of the multipath based on real-time observations. Matching these observations with the geometric relationships between "base station—scatterer—user" fills in the missing geometric quantities in traditional single-base station geometric positioning, transforming an underdetermined problem with more unknowns than equations into a geometric positioning problem solvable under prior environmental constraints and verifiable through consistency.

[0005] Based on the above analysis of the integrated communication and sensing wireless positioning problem in non-line-of-sight (NLS) scenarios, there is an urgent need for a technical approach that can simultaneously achieve high-precision user positioning and environmental geometric cognition in NLS scenarios. This approach should fully utilize CKM (Content-Based Mapping) to transform multipath interference, which is considered uncontrollable, into usable geometric constraints and "physical anchors." This would compensate for the underdeterminacy caused by single base stations and the lack of emission angle information, thereby improving the accuracy of position estimation while enhancing the perception capability of dominant scatterers and the overall scene structure. Summary of the Invention

[0006] The purpose of this invention is to provide a non-line-of-sight link sensing integrated positioning method based on channel knowledge map. This method uses a communication sensing integrated base station to sense the non-line-of-sight propagation path parameters between the base station and the user, and combines the channel and environmental prior information provided by CKM to solve the problem of difficulty in achieving user positioning when there is no line-of-sight path between the base station and the user.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A non-line-of-sight link sensing-integrated positioning method based on channel knowledge maps includes an offline stage and an online positioning stage, wherein:

[0009] The offline phase includes the following steps:

[0010] (1) Within the target area, user terminals send uplink signals at different locations. The base station receives the signals and performs sensing to estimate the multipath angle-delay characteristics of the corresponding user locations.

[0011] (2) Store the angle-delay features of all reference points obtained in step (1) as a channel knowledge map, which serves as a database representing channel priors and environmental geometric priors;

[0012] (3) Based on the angle-delay parameters in the channel knowledge map, the equivalent scatterer coordinates corresponding to each path are calculated using the single scattering geometric model to form a mapping relationship from angle-delay to scatterer position;

[0013] The online location phase includes the following steps:

[0014] (4) The base station receives the uplink signal from the user terminal at an unknown location and extracts the angle of arrival and arrival delay parameters of the multipath signal at the current time;

[0015] (5) Based on angle-delay power spectrum analysis, calculate the similarity between the power spectrum of the real-time observed signal and the power spectrum of each candidate position in the channel knowledge map;

[0016] (6) Select the top K candidate location points from high to low similarity, and calculate the user's initial location centroid based on similarity weighting to achieve coarse positioning;

[0017] (7) Within the selected candidate location range, perform path-level matching between the real-time observation path and the channel knowledge map path, calculate the angle-delay difference between each observation path and the channel knowledge map path, and assign weights to each path to reflect the matching credibility.

[0018] (8) Based on the path-level matching results and geometric consistency constraints, a weighted nonlinear least squares optimization model with scatterer priors is constructed. The coarse positioning results are used as initial values. The nonlinear least squares model is solved iteratively to jointly estimate the user terminal position and the scatterer position.

[0019] Furthermore, in the integrated communication and sensing system, the base station is equipped with a multi-antenna linear array to improve angular resolution; the user terminal sends a broadband uplink signal to provide latency resolution; the base station extracts multipath angle and latency information from the uplink signal, and combines it with prior information from the channel knowledge map to achieve positioning and environmental perception.

[0020] Furthermore, the channel knowledge map traverses reference locations in the target area and saves the set of angle-delay paths for each candidate location.

[0021] Furthermore, the reference position covers the target area in a pre-defined grid pattern.

[0022] Furthermore, in step (3), the calculation of the equivalent scatterer coordinates using the single scattering geometric model is specifically as follows:

[0023] For reference position Its position vector relative to the base station is Then the corresponding angle and latency Equivalent scatterer coordinates Calculated using the following formula:

[0024]

[0025] in, At the speed of light, For angle The unit direction vector.

[0026] Furthermore, in step (5), calculating the similarity specifically includes: mapping the observations and channel knowledge map candidates to the same angle-delay grid to form a unified and comparable power map, and calculating the similarity between the power maps.

[0027] Furthermore, in step (6), the user's initial position centroid is calculated based on similarity weighting, specifically using the following formula:

[0028]

[0029] in, Let K represent the set of the first K candidate locations. Indicates candidate position The similarity.

[0030] Furthermore, in step (7), path-level matching involves performing a one-to-one allocation matching between each observation path and each candidate path in the channel knowledge map; matching each observation path l to the globally optimal candidate position. and its corresponding channel knowledge map paths; and the weights assigned to each path. Calculate according to the following formula:

[0031]

[0032] in, For the observation path l and its position The angle-delay difference between the matched channel knowledge map paths is calculated, and low-confidence matches are eliminated by thresholding.

[0033] Furthermore, in step (8), the objective function of the weighted nonlinear least squares optimization model takes into account the geometric consistency constraints based on the observed angle of arrival and arrival delay, and suppresses the deviation relative to the prior of the channel knowledge map. Its expression is:

[0034]

[0035] in, For user location, The location of the scatterer for the l-th path. For the location of the base station, For path weights, For observation delay, At the speed of light, The scatterer position is a priori obtained from the channel knowledge map based on the matching results. These are the prior weight coefficients, and Located at the angle of arrival of the observation On a defined ray.

[0036] Furthermore, the iterative solution employs the Levenberg-Marquardt algorithm; the user location is initialized with the coarse positioning result, and the scatterer location is initialized with the prior channel knowledge map.

[0037] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0038] (1) Engineering feasibility of adapting to a single base station: Under the conditions of only uplink SIMO, base station configuration with multiple antenna arrays and no direct path, it is possible to obtain multipath angle of arrival and arrival delay observation based on conventional receiving processing, which meets the actual constraints of hardware and synchronization on the single-station system and has the conditions for implementation.

[0039] (2) Resolving the underdeterminacy of a single station and overcoming fingerprint ambiguity: Compared with fingerprint methods that rely solely on instantaneous CSI / RSS similarity and are prone to problems such as "multiple points of the same image" and cross-time period drift, this invention introduces prior channel information and environmental information provided by CKM, and uses scene geometric consistency as a constraint to effectively supplement the missing geometric quantities, thereby transforming the underdetermined problem into a discriminable and interpretable solution, significantly improving the stability and reliability of positioning.

[0040] (3) Generate "physical anchor points" from environmental priors to achieve synergy between positioning and environmental cognition: CKM stores typical multipath features and scene structures in a spatial form and provides spatial priors of equivalent dominant scatterers, transforming the originally uncontrollable multipaths into usable geometric references; thereby, in the absence of a direct path, it can obtain high-precision user location and simultaneously improve the perception of the main scatterers and environmental geometry. Attached Figure Description

[0041] Figure 1 This is a schematic diagram comparing the non-line-of-sight sensory integrated positioning framework based on CKM and the ambiguity of fingerprint positioning provided in this embodiment of the invention;

[0042] Figure 2 This is a flowchart of the non-line-of-sight integrated sensing positioning method based on CKM provided in the embodiments of the present invention;

[0043] Figure 3This is a simulation comparison diagram of the cumulative error distribution function of different positioning methods provided in the embodiments of the present invention;

[0044] Figure 4 This is the cumulative distribution function of positioning error under different numbers of receiving antennas M provided in the embodiments of the present invention;

[0045] Figure 5 It is the cumulative distribution function of positioning error under different non-prior scatterers provided in the embodiments of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. The specific embodiments described in this section are only for explaining this invention and are not intended to limit the scope of protection of this invention.

[0047] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0048] The present invention discloses a non-line-of-sight link sensing integrated positioning method based on channel knowledge map. It utilizes the communication sensing integrated base station to receive uplink signals from user terminals and channel priors and environmental geometric priors provided by CKM. By analyzing the angle of arrival and arrival delay information of multipath signals reflected by environmental scatterers, the joint estimation of user location and environmental scatterers is achieved.

[0049] like Figure 1 The diagram shown is a comparison of the ambiguity between a non-line-of-sight sensing integrated positioning framework based on CKM and fingerprint positioning, according to an embodiment of the present invention. The base station is located at... Configuration Uniform linear array of array elements; user equipment is located in Equipped with a single antenna, the direct path between the two points is blocked, and the signal reaches the base station after being reflected by several scattering objects in the environment. The base station processes the uplink reference signal to obtain the angle of arrival and arrival delay observations for each resolvable path, denoted as . Its geometric relations satisfy

[0050]

[0051]

[0052] in For the first The location of the scatterer corresponding to each path, and These are the angle of arrival and time delay of the path, respectively. It's an angle. The unit direction vector is denoted as... , The speed is the speed of light. Since there is no array on the user side, the emission angle is unavailable, and it cannot be determined simultaneously based solely on instantaneous observation. and Therefore, CKM is introduced as the environmental prior, and the candidate grid points of this environment are... In spatial candidate grid points At this location, CKM stores the set of angle-delay paths corresponding to that position. The online positioning phase will and Matching is performed to obtain several Top-K candidate locations. Some of these candidates may not match the true geometry due to fingerprint ambiguity. CKM can transform multipath uncertainties into verifiable geometric constraints, providing environmental prior time-varying scattering anchor points for subsequent localization and environmental perception, thereby achieving localization correction and refinement.

[0053] Based on the above definitions, the specific implementation steps of the exemplary embodiment of the proposed method are as follows:

[0054] (1) Offline in candidate location grid CKM is built on top of each Storage perspective - latency set

[0055]

[0056] When evaluating a grid point season Each angle-delay pair It can be mapped to an equivalent scatterer prior based on the single-hop geometric relationship.

[0057] (2) To robustly compare real-time observations with CKM channel priors, a two-dimensional angle-delay "snapshot" is constructed and correlated with the dictionary. First, the array steering vector is defined. and delay steering vector The base station uniform linear array has the following number of elements: The spacing between array elements is The carrier wavelength is Subcarrier spacing is The number of effective subcarriers is ,but

[0058] .

[0059] Therefore, it is possible to construct a "virtual snapshot" of observations and prior knowledge.

[0060] .

[0061] Angular domain Discrete Fourier Transform (DFT) dictionary With delay domain DFT dictionary ,in Define the angle / delay sampling number.

[0062] ,

[0063] Among them, subscript , representing observation and prior knowledge, respectively. Symbols For the accumulation of Hadamah. We command Construct a similarity metric for the peak-normalized power.

[0064] .

[0065] Based on this, a full-domain scan is performed to select Top-K candidates. Based on the weighted k-nearest neighbor (WKNN) paradigm, we can first obtain coarse localization by weighting the centroids.

[0066] .

[0067] This result, when used as initialization for subsequent nonlinear optimization, can significantly suppress local extrema and spurious matching.

[0068] (3) After obtaining the coarse location candidate set, in order to achieve path-level pairing, the continuous parameters are mapped to the two-dimensional spectral grid index: For each Define the observation path With CKM path The cost

[0069] .

[0070] A one-to-one match is made for each observation path, that is, in the "observation" Injective assignment in the "prior" direction

[0071] .

[0072] And give the path credibility weight For each observation path In all Select the global optimal pairing

[0073] ,

[0074] Each observation path was obtained sequentially. Weight of the globally optimal path

[0075]

[0076] And match the Mapped to equivalent scatterer prior Obtain the weighted a priori set

[0077]

[0078] To suppress false matches, further thresholding is applied. Eliminate low-reliability paths.

[0079] (4) After completing path pairing and prior screening, jointly estimate the user location and the dominant scatterer location. The core idea is to use the angle of arrival to restrict each scatterer to a ray originating from the base station, and then use geometric consistency and prior consistency to jointly constrain the objective function. For the first... A path, defined by the observed angle of arrival of the ray.

[0080] ,

[0081] The objective function constrained by both geometric consistency and prior consistency is expressed as follows:

[0082]

[0083] The confidence strength of the CKM prior is controlled, and the objective function is solved using the Levenberg-Marquardt (LM) algorithm, with the user's initial value obtained in step (2). The initial value of the scatterer is given by the environmental prior in step (3).

[0084] The simulation scenario of this invention is: evaluation in a two-dimensional non-line-of-sight environment, with the base station placed... Fixed scatterers are randomly distributed in Within the rectangular area, the user's test location is randomly distributed. The system has a carrier frequency of 6 GHz, a bandwidth of 100 MHz, 1024 subcarriers, a signal-to-noise ratio of 30 dB, a CKM (Carrier Detector) spacing of 1 m with the fingerprint reference point, and a candidate CKM number of... Angular domain DFT dimension Delay-domain DFT dimension All simulations use the same set of parameter configurations.

[0085] Figure 3This is a simulation comparison of the AoA–RSS fingerprint, CKM coarse matching, and the error cumulative distribution function of the proposed method according to an embodiment of the present invention. To test robustness, four additional scatterers not included in the CKM prior were added to the environment as interference. The results show that the error cumulative distribution function (CDF) curve of the proposed method is basically located to the upper left of the two baseline curves. The reliability rate at the error point has reached about 80%, indicating that introducing environmental geometric priors can significantly alleviate the ambiguity of coarse matching and improve accuracy.

[0086] Figure 4 Different numbers of receiving antennas are shown in the embodiments of the present invention. The cumulative distribution function of positioning error under the following conditions is compared. Three configurations. With As the angle increases, positioning accuracy continues to improve, reflecting higher angular resolution and better multipath separability. When the error is less than... hour, and The curves almost overlap, indicating that in this scenario... The main multipaths can be identified; when the error is greater than hour, It gains an advantage due to its superior resolution.

[0087] Figure 5 This describes the effect of additional non-prior scatterers on the cumulative distribution function as shown in the embodiments of the present invention, by adding non-prior scatterers. A scatterer not within CKM is used to simulate a time-varying scattering environment. When At that time, the reliability rate within 1m was approximately 99%; with... As the size increases, the performance of this method decreases, but even so... The reliability rate within 2m is still close to 65%, demonstrating robustness to environmental disturbances.

[0088] The above method introduces CKM under the integrated communication and sensing framework to provide channel and environmental priors for user positioning. The base station performs angle-delay analysis on the uplink signal and matches it with the equivalent scatterer prior mapped by CKM to perceive the location and path geometry of the environmental scatterer, providing additional and interpretable channel information for subsequent user positioning (step (1)). On this basis, the measured angle of arrival / delay of arrival is coarsely matched with the CKM prior (step (2)) and registered one-to-one at the path level (step (3)). A weighted nonlinear least squares objective is constructed under the "base station-scatterer" ray constraint, and high-precision user position estimation and environmental perception are obtained by combining "geometry + timing consistency" and "prior attraction" (step (4)).

[0089] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A non-line-of-sight link sensing-integrated positioning method based on channel knowledge maps, characterized in that: It includes an offline phase and an online positioning phase, in which: The offline phase includes the following steps: (1) Within the target area, user terminals send uplink signals at different locations. The base station receives the signals and performs sensing to estimate the multipath angle-delay characteristics of the corresponding user locations. (2) Store the angle-delay features of all reference points obtained in step (1) as a channel knowledge map, which serves as a database representing channel priors and environmental geometric priors; (3) Based on the angle-delay parameters in the channel knowledge map, the equivalent scatterer coordinates corresponding to each path are calculated using the single scattering geometric model to form a mapping relationship from angle-delay to scatterer position; The online location phase includes the following steps: (4) The base station receives the uplink signal from the user terminal at an unknown location and extracts the angle of arrival and arrival delay parameters of the multipath signal at the current time; (5) Based on angle-delay power spectrum analysis, calculate the similarity between the power spectrum of the real-time observed signal and the power spectrum of each candidate position in the channel knowledge map; (6) Select the top K candidate location points from high to low similarity, and calculate the user's initial location centroid based on similarity weighting to achieve coarse positioning; (7) Within the selected candidate location range, perform path-level matching between the real-time observation path and the channel knowledge map path, calculate the angle-delay difference between each observation path and the channel knowledge map path, and assign weights to each path to reflect the matching credibility. (8) Based on the path-level matching results and geometric consistency constraints, a weighted nonlinear least squares optimization model with scatterer priors is constructed. The coarse positioning results are used as initial values. The nonlinear least squares model is solved iteratively to jointly estimate the user terminal position and the scatterer position.

2. The method according to claim 1, characterized in that: In an integrated communication and sensing system, the base station is equipped with a multi-antenna linear array to improve angular resolution; the user terminal sends a broadband uplink signal to provide latency resolution. The base station uses multipath angle and time delay information extracted from uplink signals, combined with prior information from the channel knowledge map, to achieve positioning and environmental awareness.

3. The method according to claim 1, characterized in that: The channel knowledge map traverses reference locations in the target area and saves the set of angle-delay paths for each candidate location.

4. The method according to claim 3, characterized in that: The reference position covers the target area in a pre-defined grid pattern.

5. The method according to claim 1, characterized in that: In step (3), the calculation of the equivalent scatterer coordinates using the single scattering geometric model is specifically as follows: For reference position Its position vector relative to the base station is Then the corresponding angle and latency Equivalent scatterer coordinates Calculated using the following formula: in, At the speed of light, For angle The unit direction vector.

6. The method according to claim 1, characterized in that: In step (5), the calculation of similarity specifically includes: mapping the observation and channel knowledge map candidates to the same angle-delay grid to form a unified and comparable power map, and calculating the similarity between the power maps.

7. The method according to claim 6, characterized in that: In step (6), the initial centroid of the user's position is calculated based on similarity weighting, specifically using the following formula: in, Let K represent the set of the first K candidate locations. Indicates candidate position The similarity.

8. The method according to claim 1, characterized in that: In step (7), path-level matching involves performing a one-to-one allocation matching between each observation path and each candidate path in the channel knowledge map; and matching each observation path l with the globally optimal candidate position. and its corresponding channel knowledge map path; And assign weights to each path. Calculate according to the following formula: in, For the observation path l and its position The angle-delay difference between the matched channel knowledge map paths is calculated, and low-confidence matches are eliminated by thresholding.

9. The method according to claim 1 or 8, characterized in that: In step (8), the objective function of the weighted nonlinear least squares optimization model takes into account the geometric consistency constraints based on the observed angle of arrival and arrival delay, and suppresses the deviation relative to the prior of the channel knowledge map. Its expression is: in, For user location, The location of the scatterer for the l-th path. For the location of the base station, For path weights, For observation delay, At the speed of light, The scatterer position is a priori obtained from the channel knowledge map based on the matching results. These are the prior weight coefficients, and Located at the angle of arrival of the observation On a defined ray.

10. The method according to claim 9, characterized in that: The iterative solution uses the Levenberg-Marquardt algorithm; the user location is initialized with the coarse positioning result, and the scatterer location is initialized with the prior of the channel knowledge map.

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