A user equipment positioning and tracking method, system, terminal and medium based on external millimeter wave phased array neighborhood beam adjustment

Abstract

The application discloses a user equipment positioning and tracking method and system based on an external millimeter wave phased array neighborhood beam adjustment, a terminal and a medium, and the method comprises the following steps: completing beam alignment with the user equipment by means of a base station external millimeter wave phased array, and determining a main alignment beam used for communication between the two; generating a plurality of neighborhood adjustment beams in the angle neighborhood with the beam direction as the center; the base station controls the phased array to switch each neighborhood adjustment beam, respectively receives the user signal, and obtains the equivalent channel state information corresponding to each beam; extracting the channel information to obtain the multi-beam spatial difference feature, and estimating the user equipment position state according to the feature; relying on the position state to build a device state tracking model and carrying out filtering estimation to obtain the user equipment state. The application takes advantage of the high gain, narrow beam and controllable switching of the external millimeter wave phased array, and completes the positioning and continuous tracking of the user equipment with low cost and hardware complexity on the premise of obtaining single-port equivalent channel state information in the baseband.

Description

Technical Field

[0001] This invention relates to the fields of wireless communication, millimeter-wave phased array, integrated communication and sensing, and wireless positioning technology, and in particular to a user equipment positioning and tracking method, system, terminal, and medium based on neighboring beam adjustment of an external millimeter-wave phased array. Background Technology

[0002] With the large-scale commercialization of 5G mobile communication technology and the in-depth research and development of 6G mobile communication technology, high-speed, high-bandwidth, and high-precision wireless communication and sensing services have become the core development requirements of the next generation of mobile communication systems. Millimeter-wave communication, with its outstanding advantages of high operating frequency and ample available bandwidth, has become a core candidate technology for the large-scale application of 5G and the iterative upgrade of 6G technology. Unlike the traditional Sub-6GHz communication band, the millimeter-wave band has extremely strong propagation directionality and also exhibits high path loss. Therefore, millimeter-wave communication systems need to rely on phased array antennas to construct high-gain narrow beams, compensating for propagation loss through antenna array gain to ensure the stability and reliability of the communication link between the base station and user equipment. In conventional millimeter-wave communication mechanisms, the base station and user equipment need to select the optimal transmit and receive beams through beam search, beam training, and codebook matching processes. Existing beam alignment schemes mostly focus on maximizing received power, improving signal-to-noise ratio, reducing bit error rate, or meeting communication quality thresholds. Their core function is to establish and maintain a stable wireless communication link, focusing only on communication performance optimization and lacking the ability to accurately obtain the spatial location information of user equipment. Communication-sensing integration, as a core research direction of next-generation wireless communication, can simultaneously achieve wireless data transmission and target environment perception based on a single communication signal, effectively overcoming the technical limitations of the separation of traditional communication and sensing functions. Millimeter-wave signals, due to their narrow beamwidth and excellent spatial resolution, possess natural technical advantages in sensing scenarios such as user equipment positioning and mobile trajectory tracking. Therefore, the industry has successively proposed various millimeter-wave positioning technologies, with mainstream solutions covering technologies such as full-space beam scanning, multi-antenna array signal processing, angle of arrival estimation, time of arrival, and time difference of arrival estimation. However, existing millimeter-wave communication and positioning technologies have many inherent defects, failing to meet the high-efficiency, low-overhead, and high-precision application requirements of communication-sensing integration, severely restricting the practical application of millimeter-wave communication-sensing integration technology. Specifically, traditional millimeter-wave positioning technology based on full-space beam scanning requires periodically traversing multiple candidate beams within a large angle range to collect spatial information. While this method can obtain basic spatial angle data, the beam training process incurs high resource consumption and long scanning time, easily interfering with currently operating communication links. For high-speed mobile user equipment, frequent full-space scanning leads to insufficient positioning update frequency, making it impossible to track user location in real time. It also significantly increases the risk of communication link interruption, making it difficult to balance real-time positioning with communication stability. Traditional positioning methods based on array signal processing rely on the base station acquiring the independently received signals of each array element or the complete array channel state information (CSI). High-precision angle estimation is achieved using classic algorithms such as MUSIC and ESPRIT. However, in actual engineering prototypes and experimental systems, external millimeter-wave phased arrays only output the equivalent signal after simulated beamforming through a single RF port. The baseband equipment cannot acquire the complete array channel matrix, rendering traditional multi-element CSI positioning algorithms unusable and exhibiting extremely poor technical adaptability.Meanwhile, existing general-purpose software radio platforms, baseband processing platforms, and Sub-6GHz communication platforms lack native millimeter-wave beam transceiver capabilities. Conducting millimeter-wave integrated sensing experiments requires an external millimeter-wave phased array as a radio frequency front-end to expand hardware capabilities. However, external millimeter-wave phased arrays only support fixing beam states through control interfaces, ultimately outputting only a single synthesized radio frequency signal. Their hardware architecture differs fundamentally from all-digital arrays and multi-RF chain arrays, making traditional communication and positioning algorithms adapted to multi-RF architectures unusable. There is a severe disconnect between existing technologies and actual hardware platforms. Furthermore, existing millimeter-wave beam management mechanisms suffer from resource waste. Communication beam alignment results are only used to maintain communication link quality, failing to extract the spatial location information inherent in the beam state. In real-world scenarios, when a user equipment experiences a slight offset relative to the main alignment beam center, the equivalent CSI amplitude, phase, power, and frequency domain characteristics of the main beam and neighboring beams exhibit regular changes. Existing technologies do not utilize this characteristic for local beam fine-tuning and multi-beam feature extraction, making it impossible to achieve user positioning and tracking with low overhead, resulting in extremely low beam resource utilization. Furthermore, existing technologies separate millimeter-wave beam management and user positioning and tracking into two independent functional modules. Beam management focuses on communication link quality optimization, while positioning and tracking focuses on spatial location calculation. These two core processes lack effective coordination and linkage. The millimeter-wave main beam can already lock the approximate spatial direction of user equipment and can serve as accurate prior information for positioning and tracking. However, existing technologies have not achieved the integration and reuse of the two. The positioning process still requires a large-scale spatial search, which not only increases the system's computational overhead but also fails to transform the routine communication link maintenance process into a data source for positioning and tracking. This makes it impossible to achieve integrated coordination of communication performance optimization and high-precision sensing, and it is difficult to meet the comprehensive application requirements of current integrated millimeter-wave communication and sensing systems for low overhead, high precision, and high real-time performance.

[0003] Therefore, existing technologies still need to be improved and enhanced. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a user equipment positioning and tracking method, system, terminal and medium based on the neighboring beam adjustment of an external millimeter-wave phased array, in order to address the above-mentioned defects of the prior art. The aim is to solve the problems of high overhead of full-space beam scanning, reliance on independent CSI of multiple array elements, difficulty in adapting to external single-port phased array systems and insufficient utilization of communication beam alignment information in existing millimeter-wave positioning methods.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a user equipment positioning and tracking method based on neighboring beam adjustment of an external millimeter-wave phased array, wherein the method includes: The base station performs beam alignment with the user equipment by using an external millimeter-wave phased array configured on the base station side to determine the main alignment beam used for communication between the base station and the user equipment, and generates multiple neighborhood adjustment beams in the angular neighborhood of the beam direction corresponding to the main alignment beam as the center. The external millimeter-wave phased array is controlled by the base station to switch sequentially to the multiple neighborhood adjustment beams, and signals sent by user equipment are received under each neighborhood adjustment beam to obtain the equivalent channel state information corresponding to each neighborhood adjustment beam. Feature extraction is performed on the equivalent channel state information corresponding to each neighborhood adjustment beam to obtain multi-beam spatial differential features, and the location state of the user equipment is estimated based on the multi-beam spatial differential features. Based on the location state, a user equipment state tracking model is constructed and filtered state estimation is performed to obtain the user equipment state.

[0006] In one implementation, the external millimeter-wave phased array is connected to the base station-side baseband device via a single radio frequency port.

[0007] In one implementation, determining the primary alignment beam for communication between the base station and the user equipment includes: The base station controls an external millimeter-wave phased array to form multiple candidate beams according to a preset codebook, and measures the communication quality indicators under different candidate beams. Based on the communication quality indicators, the main alignment beam used for communication between the base station and user equipment is determined.

[0008] In one implementation, estimating the location state of the user equipment based on the multi-beam spatial differential features includes: Based on the normalized power of the neighborhood adjustment beam and the beam center direction of the neighborhood adjustment beam in the multi-beam spatial differential characteristics, the angle state of the user equipment relative to the base station is obtained. Based on the angle state and the estimated distance between the user equipment and the base station, the location state of the user equipment is obtained.

[0009] In one implementation, the step of constructing a user equipment state tracking model and performing filtered state estimation based on the location state to obtain the user equipment state includes: Based on the location state, the motion speed is calculated by combining the historical location, and the current state vector of the user equipment is constructed based on the location state and the motion speed. Extract the position component from the current state vector of the user equipment to obtain the current position observation value; The optimal state of the user equipment output from the previous time step is input into the preset state transition model, and the predicted value of the prior state at the current time step and the corresponding prediction covariance are calculated. The prior state prediction value, prediction covariance and position observation value are input into the filtering update process to complete the iterative update and obtain the optimized user equipment state at the current time.

[0010] In one implementation, the optimized user equipment state at the current moment, followed by: The optimized user equipment state at the current moment is used as the input to the state transition model at the next moment. The user equipment is continuously tracked in a loop to obtain the user equipment's motion trajectory.

[0011] In one implementation, obtaining the user equipment motion trajectory then includes: The current user equipment orientation is obtained. If the current user equipment orientation deviates from the center of the current main alignment beam by more than a preset threshold, the main alignment beam orientation is updated through the base station, and a neighboring beam set is regenerated around the updated main alignment beam.

[0012] Secondly, embodiments of the present invention also provide a user equipment positioning and tracking system based on external millimeter-wave phased array neighborhood beam adjustment, wherein the system includes: The neighborhood adjustment beam generation module is used to perform beam alignment with the user equipment through an external millimeter-wave phased array configured on the base station side, determine the main alignment beam used for communication between the base station and the user equipment, and generate multiple neighborhood adjustment beams in its angular neighborhood with the beam direction corresponding to the main alignment beam as the center. The equivalent channel state information acquisition module is used to control the external millimeter-wave phased array to switch sequentially to the multiple neighboring adjustment beams through the base station, and to receive the signal sent by the user equipment under each neighboring adjustment beam to obtain the equivalent channel state information corresponding to each neighboring adjustment beam. The location status acquisition module is used to extract features from the equivalent channel status information corresponding to each neighborhood adjustment beam to obtain multi-beam spatial differential features, and estimate the location status of the user equipment based on the multi-beam spatial differential features. The user equipment status acquisition module is used to construct a user equipment status tracking model and perform filtered status estimation based on the location status to obtain the user equipment status.

[0013] Thirdly, embodiments of the present invention also provide a terminal, wherein the terminal includes a memory, a processor, and a user equipment positioning and tracking program based on external millimeter-wave phased array neighborhood beam adjustment stored in the memory and executable on the processor. When the processor executes the user equipment positioning and tracking program based on external millimeter-wave phased array neighborhood beam adjustment, it implements the steps of the user equipment positioning and tracking method based on external millimeter-wave phased array neighborhood beam adjustment as described in any of the above schemes.

[0014] Fourthly, embodiments of the present invention also provide a computer-readable storage medium, wherein the computer-readable storage medium stores a user equipment positioning and tracking program based on external millimeter-wave phased array neighborhood beam adjustment, and when the user equipment positioning and tracking program based on external millimeter-wave phased array neighborhood beam adjustment is executed by a processor, it implements the steps of the user equipment positioning and tracking method based on external millimeter-wave phased array neighborhood beam adjustment as described in any of the above schemes.

[0015] Beneficial Effects: This invention provides a user equipment (UE) positioning and tracking method based on neighboring beam adjustment of an external millimeter-wave phased array. Compared with existing technologies, this invention first performs beam alignment between the external millimeter-wave phased array configured on the base station side and the UE, determining the main alignment beam used for communication between the base station and the UE. Then, with the beam direction corresponding to the main alignment beam as the center, multiple neighboring adjustment beams are generated within its angular neighborhood. This expands the beam detection range based on the precise main beam direction, breaking through the limitation of a single main beam detection angle. This lays the foundation for subsequent comprehensive and refined acquisition of channel data from different angles, effectively avoiding the problems of partial information acquisition and insufficient angle coverage caused by single beam detection. Next, the base station controls the external millimeter-wave phased array to sequentially switch to the multiple neighboring adjustment beams, and receives signals sent by the UE under each neighboring adjustment beam to obtain the equivalent channel state information corresponding to each neighboring adjustment beam. Through multi-beam sequential detection, the method can... Comprehensive collection of channel transmission characteristic data from different spatial angles enriches the dimensions of channel information and compensates for the deficiency of single-beam channel data in reflecting spatial channel differences, providing sufficient and detailed data support for subsequent feature extraction and location estimation. Then, feature extraction is performed on the equivalent channel state information corresponding to each neighborhood adjustment beam to obtain multi-beam spatial differential features. The location state of user equipment is estimated based on these multi-beam spatial differential features, overcoming the shortcomings of insufficient positioning accuracy in traditional single-channel feature-based positioning. Accurate preliminary estimation of user equipment location state is achieved based on multi-beam spatial differential features. Then, based on the location state, a user equipment state tracking model is constructed and filtered state estimation is implemented to obtain the user equipment state. This effectively filters out noise interference and errors caused by channel fluctuations during signal transmission, eliminates abnormal deviations in positioning data, and achieves accurate tracking and stable output of real-time motion state and location information of user equipment, significantly improving the smoothness and accuracy of equipment state detection. Overall, this invention, through a complete closed-loop technical process of main beam alignment, neighborhood multi-beam channel acquisition, spatial differential feature extraction, and filter state tracking, fully leverages the advantages of millimeter-wave phased arrays, such as flexible angle adjustment and high spatial resolution. It progressively completes the location perception and state tracking of user equipment, effectively solving the problems of single beam coverage, limited channel data dimensions, large positioning errors, and unstable state tracking in traditional millimeter-wave positioning methods. Furthermore, it significantly improves the accuracy, robustness, and real-time performance of user equipment location detection in complex communication scenarios, adapting to dynamically changing wireless communication environments and providing reliable technical support for accurate positioning, real-time user status monitoring, and efficient communication scheduling in millimeter-wave communication systems. Attached Figure Description

[0016] Figure 1 A flowchart illustrating a specific implementation of the user equipment positioning and tracking method based on neighboring beam adjustment of an external millimeter-wave phased array, as provided in this embodiment of the invention.

[0017] Figure 2 This is a schematic diagram of the user equipment positioning and tracking system structure in the user equipment positioning and tracking method based on the neighboring beam adjustment of an external millimeter-wave phased array provided in an embodiment of the present invention.

[0018] Figure 3 This is a schematic diagram of the system structure of the external millimeter-wave phased array single-port access baseband device in the user equipment positioning and tracking method based on the neighboring beam adjustment of the external millimeter-wave phased array provided in the embodiments of the present invention.

[0019] Figure 4 This is a flowchart of the user equipment positioning and tracking method in the user equipment positioning and tracking method based on the neighboring beam adjustment of an external millimeter-wave phased array provided in an embodiment of the present invention.

[0020] Figure 5 This is a schematic diagram of user equipment trajectory tracking in the user equipment positioning and tracking method based on neighboring beam adjustment of an external millimeter-wave phased array provided in an embodiment of the present invention.

[0021] Figure 6 This is a schematic diagram of the main alignment beam and the neighborhood adjustment beam in the user equipment positioning and tracking method based on the neighboring beam adjustment of an external millimeter-wave phased array provided in an embodiment of the present invention.

[0022] Figure 7 This is a schematic diagram of the multi-beam spatial differential CSI feature construction process in the user equipment positioning and tracking method based on the neighboring beam adjustment of an external millimeter-wave phased array provided in an embodiment of the present invention.

[0023] Figure 8 This is a schematic diagram of the user equipment positioning and tracking system based on the neighboring beam adjustment of an external millimeter-wave phased array provided in an embodiment of the present invention.

[0024] Figure 9 This is a block diagram illustrating the internal structure of a terminal provided in an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0026] With the large-scale commercialization of 5G mobile communication technology and the in-depth research and development of 6G mobile communication technology, high-speed, high-bandwidth, and high-precision wireless communication and sensing services have become the core development requirements of the next generation of mobile communication systems. Millimeter-wave communication, with its outstanding advantages of high operating frequency and ample available bandwidth, has become a core candidate technology for the large-scale application of 5G and the iterative upgrade of 6G technology. Unlike the traditional Sub-6GHz communication band, the millimeter-wave band has extremely strong propagation directionality and also exhibits high path loss. Therefore, millimeter-wave communication systems need to rely on phased array antennas to construct high-gain narrow beams, compensating for propagation loss through antenna array gain to ensure the stability and reliability of the communication link between the base station and user equipment. In conventional millimeter-wave communication mechanisms, the base station and user equipment need to select the optimal transmit and receive beams through beam search, beam training, and codebook matching processes. Existing beam alignment schemes mostly focus on maximizing received power, improving signal-to-noise ratio, reducing bit error rate, or meeting communication quality thresholds. Their core function is to establish and maintain a stable wireless communication link, focusing only on communication performance optimization and lacking the ability to accurately obtain the spatial location information of user equipment. Communication-sensing integration, as a core research direction of next-generation wireless communication, can simultaneously achieve wireless data transmission and target environment perception based on a single communication signal, effectively overcoming the technical limitations of the separation of traditional communication and sensing functions. Millimeter-wave signals, due to their narrow beamwidth and excellent spatial resolution, possess natural technical advantages in sensing scenarios such as user equipment positioning and mobile trajectory tracking. Therefore, the industry has successively proposed various millimeter-wave positioning technologies, with mainstream solutions covering technologies such as full-space beam scanning, multi-antenna array signal processing, angle of arrival estimation, time of arrival, and time difference of arrival estimation. However, existing millimeter-wave communication and positioning technologies have many inherent defects, failing to meet the high-efficiency, low-overhead, and high-precision application requirements of communication-sensing integration, severely restricting the practical application of millimeter-wave communication-sensing integration technology. Specifically, traditional millimeter-wave positioning technology based on full-space beam scanning requires periodically traversing multiple candidate beams within a large angle range to collect spatial information. While this method can obtain basic spatial angle data, the beam training process incurs high resource consumption and long scanning time, easily interfering with currently operating communication links. For high-speed mobile user equipment, frequent full-space scanning leads to insufficient positioning update frequency, making it impossible to track user location in real time. It also significantly increases the risk of communication link interruption, making it difficult to balance real-time positioning with communication stability. Traditional positioning methods based on array signal processing rely on the base station acquiring the independently received signals of each array element or the complete array channel state information (CSI). High-precision angle estimation is achieved using classic algorithms such as MUSIC and ESPRIT. However, in actual engineering prototypes and experimental systems, external millimeter-wave phased arrays only output the equivalent signal after simulated beamforming through a single RF port. The baseband equipment cannot acquire the complete array channel matrix, rendering traditional multi-element CSI positioning algorithms unusable and exhibiting extremely poor technical adaptability.Meanwhile, existing general-purpose software radio platforms, baseband processing platforms, and Sub-6GHz communication platforms lack native millimeter-wave beam transceiver capabilities. Conducting millimeter-wave integrated sensing experiments requires an external millimeter-wave phased array as a radio frequency front-end to expand hardware capabilities. However, external millimeter-wave phased arrays only support fixing beam states through control interfaces, ultimately outputting only a single synthesized radio frequency signal. Their hardware architecture differs fundamentally from all-digital arrays and multi-RF chain arrays, making traditional communication and positioning algorithms adapted to multi-RF architectures unusable. There is a severe disconnect between existing technologies and actual hardware platforms. Furthermore, existing millimeter-wave beam management mechanisms suffer from resource waste. Communication beam alignment results are only used to maintain communication link quality, failing to extract the spatial location information inherent in the beam state. In real-world scenarios, when a user equipment experiences a slight offset relative to the main alignment beam center, the equivalent CSI amplitude, phase, power, and frequency domain characteristics of the main beam and neighboring beams exhibit regular changes. Existing technologies do not utilize this characteristic for local beam fine-tuning and multi-beam feature extraction, making it impossible to achieve user positioning and tracking with low overhead, resulting in extremely low beam resource utilization. Furthermore, existing technologies separate millimeter-wave beam management and user positioning and tracking into two independent functional modules. Beam management focuses on communication link quality optimization, while positioning and tracking focuses on spatial location calculation. These two core processes lack effective coordination and linkage. The millimeter-wave main beam can already lock the approximate spatial direction of user equipment and can serve as accurate prior information for positioning and tracking. However, existing technologies have not achieved the integration and reuse of the two. The positioning process still requires a large-scale spatial search, which not only increases the system's computational overhead but also fails to transform the routine communication link maintenance process into a data source for positioning and tracking. This makes it impossible to achieve integrated coordination of communication performance optimization and high-precision sensing, and it is difficult to meet the comprehensive application requirements of current integrated millimeter-wave communication and sensing systems for low overhead, high precision, and high real-time performance.

[0027] To address the aforementioned issues, this embodiment provides a user equipment (UE) positioning and tracking method based on neighboring beam adjustment using an external millimeter-wave phased array. Specifically, this embodiment first performs beam alignment between the external millimeter-wave phased array configured on the base station side and the UE, determining the primary alignment beam used for communication between the base station and the UE. Then, using the beam direction corresponding to the primary alignment beam as the center, multiple neighboring adjustment beams are generated within its angular neighborhood. This expands the beam detection range based on the precise primary beam direction, overcoming the limitations of a single primary beam detection angle. This lays the foundation for subsequent comprehensive and refined acquisition of channel data from different angles, effectively avoiding the problems of partial information acquisition and insufficient angle coverage caused by single-beam detection. Next, the base station controls the external millimeter-wave phased array to sequentially switch to the multiple neighboring adjustment beams, receiving signals transmitted by the UE under each neighboring adjustment beam to obtain the equivalent channel state information corresponding to each neighboring adjustment beam. This is achieved through multi-beam sequential detection. This system can comprehensively collect channel transmission characteristic data from different spatial angles, enriching the dimensions of channel information and compensating for the deficiency of single-beam channel data in reflecting spatial channel differences. This provides sufficient and detailed data support for subsequent feature extraction and location estimation. Then, it extracts features from the equivalent channel state information corresponding to each neighborhood adjustment beam to obtain multi-beam spatial differential features. Based on these features, it estimates the location state of the user equipment, overcoming the shortcomings of insufficient positioning accuracy of traditional single-channel features. It achieves a precise preliminary estimation of the user equipment's location state based on multi-beam spatial differential features. Next, based on the location state, it constructs a user equipment state tracking model and implements filtered state estimation to obtain the user equipment state. This effectively filters out noise interference and errors caused by channel fluctuations during signal transmission, eliminates abnormal deviations in the positioning data, and achieves accurate tracking and stable output of the user equipment's real-time motion state and location information, significantly improving the smoothness and accuracy of equipment state detection. Overall, this invention, through a complete closed-loop technical process of main beam alignment, neighborhood multi-beam channel acquisition, spatial differential feature extraction, and filter state tracking, fully leverages the advantages of millimeter-wave phased arrays, such as flexible angle adjustment and high spatial resolution. It progressively completes the location perception and state tracking of user equipment, effectively solving the problems of single beam coverage, limited channel data dimensions, large positioning errors, and unstable state tracking in traditional millimeter-wave positioning methods. Furthermore, it significantly improves the accuracy, robustness, and real-time performance of user equipment location detection in complex communication scenarios, adapting to dynamically changing wireless communication environments and providing reliable technical support for accurate positioning, real-time user status monitoring, and efficient communication scheduling in millimeter-wave communication systems.

[0028] The user equipment positioning and tracking method based on neighboring beam adjustment of an external millimeter-wave phased array provided in this embodiment can be applied to smart terminals, such as... Figure 1 As shown, the specific steps include the following: Step S100: Perform beam alignment with the user equipment through the external millimeter-wave phased array configured on the base station side, determine the main alignment beam used for communication between the base station and the user equipment, and generate multiple neighborhood adjustment beams in the angular neighborhood of the beam direction corresponding to the main alignment beam as the center.

[0029] In this embodiment, firstly, a bidirectional beam alignment operation is completed between the base station and the user equipment using an external millimeter-wave phased array configured on the base station side, precisely locking the main alignment beam that can be used for stable communication between the base station and the user equipment. Millimeter-wave communication is characterized by narrow beams and strong directionality, making it highly susceptible to communication attenuation and interruption due to directional deviation. This step utilizes the high-precision beam control capability of the phased array to complete beam matching, enabling the rapid establishment of the core communication link between the base station and the user equipment, ensuring the effectiveness and stability of the basic communication channel, and providing a precise reference direction for subsequent beam optimization and adjustment, thereby fundamentally avoiding the communication loss problem caused by irregular millimeter-wave transmission. Then, using the determined beam direction of the main alignment beam as the core reference, several neighboring adjustment beams are generated within its adjacent angular range. Due to dynamic factors such as obstruction interference, slight movement of user equipment, and environmental signal scattering in real-world communication scenarios, a single main beam cannot adapt to the complex and ever-changing communication environment. Generating multiple sets of adjustment beams in the angular neighborhood of the main beam enriches the selectable range of communication beams, covers the effective communication angle area around the main beam, and compensates for the shortcomings of a single beam, such as limited coverage and weak anti-interference capability. Overall, the entire operation process first establishes a reliable core communication channel through precise beam alignment, and then expands the communication coverage dimension through neighborhood multi-beams, forming a composite beam communication system with the main beam as the core and neighborhood beams as supplements. This ensures the transmission rate and directional gain advantages of millimeter-wave communication while effectively improving the adaptability and fault tolerance of the communication system to dynamic scenarios and environmental interference, significantly reducing the probability of beam switching, signal attenuation, and communication disconnection, and continuously ensuring the continuity and reliability of millimeter-wave communication between the base station and user equipment.

[0030] Specifically, step S100 includes the following steps: Step S101: Control the external millimeter-wave phased array through the base station to form multiple candidate beams according to the preset codebook, and measure the communication quality indicators under different candidate beams; Step S102: Determine the main alignment beam used for communication between the base station and user equipment based on the communication quality indicators.

[0031] In one implementation, this embodiment provides a user equipment positioning and tracking system based on an external millimeter-wave phased array, such as... Figure 2As shown, the system includes user equipment, a millimeter-wave link, baseband equipment, an external millimeter-wave phased array, a beam control module, a CSI (Channel State Information) acquisition module, a CSI feature extraction module, and a user equipment positioning and tracking module. The user equipment transmits pilot signals, synchronization signals, reference signals, or training sequences. The baseband equipment performs pilot reception, synchronization, channel estimation, and CSI calculation. The external millimeter-wave phased array forms the main alignment beam and multiple neighborhood adjustment beams. The beam control module sends control commands to the external millimeter-wave phased array to control its switching between different beam states. The external millimeter-wave phased array can operate in 24GHz, 26GHz, 28GHz, 39GHz, or other millimeter-wave frequency bands, such as... Figure 3 As shown in the figure, the system includes a baseband device, a single RF port, an external millimeter-wave phased array, and a beam control interface. The external millimeter-wave phased array is connected to the baseband device on the base station side via a single RF port. The baseband device can be a software-defined radio device, communication testing equipment, baseband processing equipment, or other devices capable of generating, receiving, and processing wireless signals. This external millimeter-wave phased array differs from traditional built-in multi-RF chain arrays, as it can be used as an independent RF front-end to connect to existing baseband platforms. For deployed Sub-6GHz baseband platforms or software-defined radio systems, no modification to the baseband hardware architecture is required to extend the system's millimeter-wave beamforming functionality. Unlike fully digital array connections that require each antenna element or subarray to have an independent RF link, in this solution, the baseband device can only acquire the equivalent received signal after simulated beamforming by the external millimeter-wave phased array. Therefore, this invention does not rely on element-level CSI, but rather controls the external millimeter-wave phased array to sequentially form multiple neighboring beams and acquires equivalent CSI under different beam states, thereby achieving millimeter-wave positioning and tracking without changing the baseband device's hardware structure. like Figure 4 and Figure 5 As shown, the base station first relies on an external millimeter-wave phased array to complete the beam alignment process with the user equipment (i.e., Figure 4 (As shown in S1, main beam alignment) The base station sequentially forms multiple candidate beams according to a preset beam codebook, receives pilot signals transmitted by user equipment, and synchronously collects communication quality indicators such as received power, signal-to-noise ratio, reference signal received power, error vector amplitude, and channel estimation quality for each candidate beam. Based on the collected communication quality indicators, the main alignment beam used for data communication between the base station and user equipment is selected. The main aligned beam It can be either the beam with the best communication quality performance or a beam whose communication indicators have reached a preset communication quality threshold. For example... Figure 6 As shown, the main alignment beam is determined. Then, the base station aligns the main beam in the direction corresponding to the beam, that is, at an azimuth angle of . Pitch angle is With the direction set as the center direction, the base station no longer performs full-space beam scanning. Instead, it uses the center direction corresponding to the main alignment beam as a reference and generates several neighborhood adjustment beams within its angular neighborhood range to construct a neighborhood beam set (i.e., Figure 4 As shown in S2, the adjusted beam is generated. The neighborhood beam set can be arranged in a one-dimensional azimuth angle single direction, one of which is... Corresponding to include , , Three beams in three directions, the second of which includes... , , , , A collection of beams in five directions ,in, For left-biased beams, For right-biased beams, For up-polarized beam and This is a down-biased beam. A two-dimensional neighborhood beam set suitable for scenarios with simultaneous azimuth and elevation angle control can also be constructed. This two-dimensional local neighborhood adjustment beam set can be represented as... In the formula and These represent the beam adjustment compensation amounts in the azimuth and elevation directions, respectively. The entire scheme first uses a beam matching process to lock the optimal main communication beam, avoiding the resource overhead of continuous full-domain beam scanning. Then, multiple sets of neighborhood adjustment beams are deployed around the central beam in local angular intervals. This not only quickly responds to channel fluctuations caused by small angular offsets by the user, ensuring the continuous stability of the millimeter-wave communication link, but also reduces the upgrade and transformation costs of existing baseband systems by leveraging the hardware characteristics of an external phased array. Subsequently, the CSI acquisition module and CSI feature extraction module complete the acquisition and feature extraction of equivalent CSI under different neighborhood beams. Finally, the user equipment positioning and tracking module performs positioning and tracking calculations based on the above data.

[0032] Step S200: The external millimeter-wave phased array is controlled by the base station to switch sequentially to the multiple neighborhood adjustment beams, and signals sent by user equipment are received under each neighborhood adjustment beam to obtain the equivalent channel state information corresponding to each neighborhood adjustment beam.

[0033] In this embodiment, as Figure 4 and Figure 5 As shown, the base station controls the external millimeter-wave phased array to sequentially switch to the neighborhood adjustment beam set. Any beam element in the set, that is, switching to the neighboring beam set. The various beams within, among which This represents the total number of beams adjusted in the neighborhood. After each beam switch of the phased array completes and forms the corresponding beam state, the user equipment sends pilot signals, reference signals, synchronization signals, or preset training sequences. The baseband equipment on the base station side receives the signals after beamforming by the external millimeter-wave phased array and performs channel estimation calculations on these signals to obtain the equivalent CSI data corresponding to the current beam state (i.e., Figure 4 (S3 multibeam equivalent CSI acquisition shown). The first... The equivalent CSI corresponding to each neighborhood adjusted beam is denoted as a vector. In the formula This represents the total number of subcarriers in the system. Indicates the first The neighborhood adjustment beam is in the first Complex channel responses on each subcarrier. Traverse all After each neighborhood adjustment beam completes channel acquisition, the equivalent CSIs corresponding to all beams are aggregated to form an equivalent CSI set. Because this external millimeter-wave phased array interfaces with the baseband device only through a single RF port, the baseband device cannot directly obtain the independent element-level CSI of each element of the phased array. It can only obtain the beam-equivalent CSI under different simulated beamforming configurations. Therefore, the final set of data is... It is not a multi-element CSI matrix under the traditional architecture, but a set of beam-equivalent CSIs acquired one by one by an external millimeter-wave phased array operating under different simulated beamforming states. The entire beam rotation and channel acquisition process can complete the batch acquisition of multi-beam channel information based on a single RF port hardware architecture. While simplifying the complexity of hardware interfaces, it fully records the channel characteristics corresponding to different directional beams, providing complete channel data source support for subsequent signal processing processes such as beam optimization and channel reconstruction.

[0034] Step S300: Extract features from the equivalent channel state information corresponding to each neighborhood adjustment beam to obtain multi-beam spatial differential features, and estimate the location state of the user equipment based on the multi-beam spatial differential features.

[0035] In this embodiment, firstly, feature extraction is performed on the equivalent channel state information corresponding to each neighborhood adjustment beam. The equivalent channel state information carries core wireless parameters such as transmission loss, phase offset, and spatial propagation differences of wireless signals in different beam neighborhoods. Accurate feature extraction of this information allows for the filtering and condensation of effective information strongly correlated with the spatial location of the beam from complex raw channel data, eliminating invalid and redundant data such as channel noise and transmission interference, thus laying a clean and effective data foundation for the subsequent generation of differentiated features. After completing the basic feature extraction, multi-beam spatial differential features are further generated. This step compares the channel feature differences between different neighborhood beams, uncovers the differences and correlations in signal distribution across the coverage space of different beams, highlights the differences in beam channel characteristics corresponding to different spatial locations, and transforms abstract channel state data into differentiated features that can characterize spatial positional relationships, achieving an effective conversion from channel data to spatial positional features. Finally, based on the accurate multi-beam spatial differential features, the location status of the user equipment is estimated. By matching the spatial characteristics of the beam coverage area with the differentiated spatial features, the spatial location and movement status of the user equipment are accurately determined. The overall process leverages the spatial diversity of multiple beams by mining the spatial characteristics of the beam channel layer by layer, avoiding the problems of single beam channel information representation dimension and insufficient position estimation accuracy. This effectively improves the accuracy and stability of user equipment position status identification, making terminal position perception in wireless communication scenarios more accurate and efficient.

[0036] Specifically, step S300 includes the following steps: Step S301: Based on the normalized power corresponding to the neighborhood adjustment beam and the beam center direction of the neighborhood adjustment beam in the multi-beam spatial differential characteristics, the angle state of the user equipment relative to the base station is obtained. Step S302: Based on the angle state and the distance estimate between the user equipment and the base station, the location state of the user equipment is obtained.

[0037] In one implementation, such as Figure 4 and Figure 5 As shown, firstly, the base station adjusts the equivalent CSI set under different neighborhood beams. Feature extraction is performed to obtain multi-beam spatial difference features (i.e. Figure 4 The S4 spatial difference feature construction is shown. Figure 7 As shown, the multi-beam spatial differential characteristics include average received power characteristics, normalized power characteristics, azimuth and elevation beam differential characteristics, phase differential characteristics, frequency domain correlation characteristics, and continuous-time CSI variation characteristics. Among these, the average received power characteristic is defined as follows: The average received power corresponding to each neighborhood adjusted beam is ,in, Indicates the first The average received power calculated from the equivalent CSI under multiple neighborhood-adjusted beams; the power vector corresponding to multiple neighborhood-adjusted beams is expressed as follows: .

[0038] Normalized power characteristics: To reduce the impact of user equipment transmit power variations, range fading, and hardware gain fluctuations on positioning results, the power vector is normalized to obtain the first... The normalized power corresponding to each neighborhood adjusted beam This yields the normalized power eigenvector. ,in .

[0039] Azimuth and elevation beam differential features: In a one-dimensional azimuth positioning scenario, azimuth differential features are constructed based on left-leaning and right-leaning beams. ,in, This indicates the average received power corresponding to the right-biased beam. This represents the average received power corresponding to the left-biased beam; in a two-dimensional angle positioning scenario, pitch differential features are constructed based on the upper-biased beam and the lower-biased beam. ,in, This indicates the average received power corresponding to the upward-biased beam. This indicates the average received power corresponding to the down-biased beam.

[0040] Phase difference characteristics: Let the first... The first beam and the first The beam at the 1st The CSI phases on each subcarrier are respectively and Then the first The first beam and the first The phase difference characteristics between beams are The phase difference characteristics on multiple subcarriers can be expressed as: .

[0041] Frequency domain correlation characteristics: Frequency domain correlation characteristics are denoted as This is used to describe the correlation structure of the equivalent CSI in the frequency domain; considering that this invention acquires multi-beam equivalent CSI under multiple neighborhood adjusted beams, therefore It includes two types of features: intra-beam frequency domain correlation features and cross-beam frequency domain correlation features; the total frequency domain correlation features can be expressed as: ,in, This indicates the frequency domain correlation characteristics within the beam. Indicates cross-beam frequency domain correlation characteristics; for the first Each neighborhood-adjusted beam has an equivalent CSI of 1. . No. The first beam inside The subcarrier and the first The frequency domain correlation characteristics between subcarriers can be expressed as follows: ,in Representing complex conjugation; therefore, the frequency domain correlation matrix inside the i-th beam can be expressed as equation ,for The frequency domain correlation characteristics within each neighborhood of a beam can be expressed as: Furthermore, for the first The neighborhood adjustment beam and the first Each neighborhood-adjusted beam has an equivalent CSI of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 ... and , No. The first beam The subcarrier and the first The first beam The cross-beam frequency domain correlation characteristics between subcarriers can be expressed as: ,in, Therefore, the first The beam and the first The cross-beam frequency domain correlation matrix between beams can be expressed as: For all different neighborhood-adjusted beam pairs, the cross-beam frequency domain correlation characteristics can be expressed as: Among them, the intra-beam frequency domain correlation feature is used to characterize the variation law of CSI in the frequency domain under the same beam, and the cross-beam frequency domain correlation feature is used to characterize the frequency domain consistency and difference between different neighboring beams, thereby reflecting the spatial offset characteristics of the user equipment relative to the main alignment beam.

[0042] Continuous-time CSI variation characteristics: The continuous-time CSI variation characteristics are denoted as... This is used to describe the variation of the multibeam equivalent CSI over time during the movement of user equipment. Let the th... At the [time]th moment The CSI of each beam is given by formula Then the CSI change characteristics at adjacent time points can be expressed as: The corresponding power change characteristics can be expressed as: If the first The localization feature vector at each time step is Then the characteristic changes at consecutive time points can be expressed as: So, the first The continuous time-series CSI variation characteristics at each moment can be expressed as: ,in, Indicates the length of the time window used for trajectory tracking. Indicates the most recent The temporal variation features constituted by the CSI feature changes within a given time period, based on normalized power features, azimuth differential features, and pitch differential features, can be used to construct a basic positioning feature vector, as shown in equation [equation missing]. Furthermore, phase difference features, frequency domain correlation features, and continuous time-series CSI variation features are added to the positioning feature vector to obtain an extended positioning feature vector, as shown in the equation. ,in, Represents the basic localization feature vector. Indicates the first The extended localization feature vector at each time step. Indicates the first Phase difference characteristics at each moment, Indicates the first Frequency domain correlation characteristics at each time point Indicates the first The characteristics of continuous time-series CSI changes at each moment.

[0043] Then, the location state of the user equipment is estimated based on the multi-beam spatial difference characteristics (i.e., Figure 4 The location estimation of user equipment S4 is shown. The beam center direction of each neighborhood-adjusted beam is given by equation [formula missing]. ,in, Indicates azimuth. Indicates the pitch angle. Based on normalized power. The estimated azimuth angle of the user equipment is given by the formula. The pitch angle estimate of the user equipment is given by the formula. ,because Therefore, the azimuth estimate of the user equipment can be simplified to the following formula: The pitch angle estimate of the user equipment can be simplified to the following formula: This allows us to obtain the angular state of the user equipment relative to the base station. If the system can obtain an estimated distance between the user equipment and the base station. In a two-dimensional planar scene, the user equipment position can be represented as and Therefore, the two-dimensional position estimation of the user equipment is given by equation [equation missing]. In a 3D positioning scenario, if a distance estimate is obtained... Azimuth estimate and pitch angle estimates Then the three-dimensional position of the user equipment can be expressed as: , , Therefore, the three-dimensional position of the user equipment is estimated as follows: In another implementation, multi-beam spatial differential features can also be used. As input, the user equipment location status is used as output to construct a machine learning or deep learning positioning model. This model can be a multilayer perceptron, convolutional neural network, recurrent neural network, long short-term memory network, Transformer network, or a combination thereof. Overall, this invention extracts multi-dimensional, multi-beam spatial differential features, including average received power, normalized power, beam differential, phase differential, frequency domain correlation, and continuous-time CSI changes, to construct basic and extended positioning feature vectors. Combined with weighted beam centroids, accurate angle and position estimation is achieved. Furthermore, it can be combined with deep learning models to complete positioning, effectively avoiding power, fading, and hardware fluctuation interference. It fully exploits the differences in spatial, frequency, and temporal CSI feature correlations, significantly improving the accuracy, stability, and scene adaptability of device positioning.

[0044] Step S400: Based on the location state, construct a user equipment state tracking model and perform filtered state estimation to obtain the user equipment state.

[0045] In this embodiment, firstly, based on the acquired user equipment location status information, a user equipment status tracking model adapted to the scenario is built. This step can standardize and structure the core behaviors of the device, such as location changes, movement trends, and status switching, transforming scattered and instantaneous location status data into a continuous and correlated device status evolution logic. It clarifies the change rules and correlation dimensions of the device status, providing stable model support and data benchmark for subsequent accurate inference of the complete device status, and avoiding status judgment bias caused by fragmented and irregular single location data. Subsequently, based on the constructed tracking model, filtered status estimation processing is performed to optimize and correct problems that are prone to occur during device location acquisition, such as noise interference, data jitter, measurement errors, and positioning offset. This effectively removes invalid and abnormal data, smooths status fluctuations, and further calibrates the real-time status parameters of the device, making up for the deficiencies of insufficient accuracy and poor stability of the original location status data. Overall, through a step-by-step processing approach of modeling and filtering estimation, a complete transformation from raw location status data to high-precision and high-stability user equipment status is achieved. This approach not only ensures the continuity and logic of equipment status tracking through the model, but also improves the accuracy and reliability of status estimation through filtering algorithms. It can realistically, in real time, and accurately restore the actual operating status of user equipment, providing accurate and effective core data support for subsequent upper-level business applications such as equipment management, location monitoring, and behavior analysis.

[0046] Specifically, step S400 includes the following steps: Step S401: Based on the location state, calculate the motion speed by combining the historical location, and construct the current state vector of the user equipment based on the location state and the motion speed; Step S402: Extract the position component from the current state vector of the user equipment to obtain the current position observation value; Step S403: Input the optimal state of the user equipment output by the filter at the previous moment into the preset state transition model, and calculate the predicted value of the prior state at the current moment and the corresponding prediction covariance. Step S404: Input the prior state prediction value, prediction covariance and position observation value into the filtering update process to complete the iterative update and obtain the optimized user equipment state at the current time.

[0047] In one implementation, such as Figure 4 and Figure 5 As shown, continuous positioning and tracking of user equipment (i.e., based on the location status of user equipment at continuous time intervals, continuous time-series CSI change characteristics, or the tracking results of the previous time interval) is carried out. Figure 4 (S6 Trajectory Tracking and Beam Update shown). First, based on the stated position state, the user equipment's motion velocity is calculated using historical positions. Then, the user equipment's current state vector is constructed based on the position state and motion velocity. Taking two-dimensional planar motion as an example, the user equipment's state vector at time t is defined as... ,in and These represent the position coordinates of the user equipment in a two-dimensional plane, derived from the previous positioning formula. The solution is extracted. and These represent the user equipment's velocity along the horizontal and vertical axes, respectively, and are derived from multiple position estimates at different times. The calculation is then performed. Subsequently, the position component is extracted from the current state vector of the user equipment to obtain the current position observation value, and a corresponding observation model is constructed. In the formula These are the position observations obtained by the multibeam CSI positioning method. For the observation matrix, To mitigate noise, the raw data for this location observation is obtained using multi-beam CSI positioning. The corresponding location information was already incorporated when constructing the current state vector. This step extracts the location component of the state vector to obtain the observation vector, thus matching it with the observation model's mapping relationship. Next, it is determined whether the current time is the tracking start time. If it is, the initial optimal state and initial covariance are initialized using the user equipment's current state vector. If it is not the tracking start time, the filtered optimal state of the user equipment from the previous time step is input into the preset state transition model. The calculation yields the predicted value of the prior state at the current time and the corresponding prediction covariance, where... Here is the state transition matrix. This is for process noise. The solved prior state predictions and prediction covariance are then input together with the aforementioned position observations into the filtering update process to complete the iterative update operation. Kalman filtering, extended Kalman filtering, or particle filtering algorithms can be used to obtain the result. The system performs smooth state estimation at each time step, thus solving for the optimized user equipment state at the current time step. Then, iteratively executing the entire process of prediction, observation matching, and filtering update, using the optimized user equipment state at the current time step as input to the state transition model at the next time step, achieves continuous tracking of the user equipment's position and motion state. Finally, it summarizes the state estimation results from consecutive time steps to form the complete motion trajectory of the user equipment. During continuous tracking, the base station extracts the estimated location information of the user equipment at the current moment. This updates the main alignment beam direction for the next moment, satisfying... Simultaneously, a neighborhood adjustment beam set is regenerated around the updated main alignment beam direction. ,in and Representing the first The azimuth and elevation offsets of each neighboring beam relative to the main alignment beam are adjusted, and the communication quality of the main alignment beam is determined in real time. When the conditions are met... hour, This indicates the communication quality index of the main aligned beam. Indicating a preset communication quality threshold, the base station restarts the beam alignment process to obtain a new primary alignment beam. The beam direction adjustment process employs a layered judgment logic. When the estimated user equipment direction still falls within the coverage area of ​​the current primary alignment beam, the base station maintains the current primary alignment beam parameters unchanged and continuously performs fine-tuning scans only within the neighborhood of that primary beam. When the estimated user equipment direction's offset relative to the center of the current primary alignment beam exceeds a preset threshold, the base station sets the estimated user equipment direction as the new primary alignment beam direction and regenerates a matching neighborhood beam set around this new primary alignment beam. This entire operational logic ultimately forms a closed-loop control mechanism of "primary beam alignment—neighborhood CSI acquisition—position estimation—trajectory tracking—beam update." By relying on motion state filtering to smooth and reduce single-time positioning noise interference, and continuously matching the user's dynamic beam direction for synchronous iteration, it ensures both the continuity of positioning and tracking and the smoothness of the trajectory, while also reducing the probability of beam mismatch and stabilizing the transmission quality of the millimeter-wave communication link.

[0048] In one implementation, the present invention builds an experimental and application system based on the system architecture of an external millimeter-wave phased array single-port access baseband device. At the hardware level, the core hardware platform consists of an external 28GHz band millimeter-wave phased array connected to the software radio equipment on the base station side. The user equipment is responsible for transmitting OFDM pilot signals and can flexibly configure different beam directions through the phased array control interface, providing hardware support for multi-beam signal acquisition and positioning sensing. The specific experimental implementation and positioning tracking process are as follows: First, a base station beam scanning operation is performed. By scanning the entire space beam, the main alignment beam around the user equipment is locked, and the user's basic beam alignment reference is determined. Then, with this main alignment beam as the core center, multiple neighborhood adjustment beams are generated according to a preset angle step size to construct a multi-beam detection set covering the local space around the user. This eliminates the need for a full-space beam rescan, effectively reducing the resource overhead of beam training. During the experiment, the user equipment is placed on a movable rail. The precise position data of the rail is used as the real position label of the user equipment to ensure the accuracy of the benchmark for subsequent model training and accuracy verification. The user equipment can move along the rail to simulate the dynamic motion state of the terminal in a real scenario. The base station will switch the various neighborhood adjustment beams sequentially at each rail position point of the user equipment, and simultaneously collect the multi-beam equivalent CSI data under the corresponding beam state to complete the batch collection of raw positioning and sensing data. In the data processing stage, basic features are first extracted from the collected multi-beam equivalent CSI data. The average power, normalized power, left-right differential features, up-down differential features, and phase differential features of each beam's corresponding CSI are calculated to uncover the signal differences across different spatial dimensions of the beams. Based on this, the frequency domain correlation matrix between different subcarriers within the same beam and the cross-beam frequency domain correlation matrix between different beams are further calculated to fully explore the frequency domain correlation characteristics of the CSI signal. Simultaneously, the dynamic changes in CSI at different times during continuous device movement are combined to fuse spatial, frequency, and temporal multi-dimensional information to construct an extended positioning feature vector, enriching the positioning feature dimensions and improving feature representation accuracy. After feature construction, the weighted beam centroid method can be used to estimate the basic angle of the user device, meeting the positioning requirements in conventional scenarios. For high-precision positioning scenarios, the extracted multi-beam spatial differential features, frequency domain correlation features, and continuous-time CSI change features can be used as model input features. Using the actual device position data recorded by the sliding rail as training labels, a dedicated mapping model mapping CSI data to device position is built and trained, further optimizing positioning accuracy through a data-driven approach. During the online testing phase, the real-time collected terminal multi-beam equivalent CSI data is input into the trained system model, which can then output the accurate position estimation results and continuous motion trajectory of the user equipment in real time, enabling dynamic tracking of the mobile terminal.This implementation relies on a combined architecture of an external millimeter-wave phased array and software-defined radio equipment. It can effectively verify the technical feasibility of achieving user equipment positioning and tracking using multi-beam equivalent CSI under single-port access conditions. Furthermore, this technical solution is widely adaptable to various millimeter-wave communication and sensing integrated application scenarios, specifically covering high-precision positioning and dynamic tracking of mobile terminals from indoor millimeter-wave base stations, intelligent beam management assistance in millimeter-wave communication links, beam pre-switching optimization based on user equipment motion trajectory prediction, terminal location sensing in indoor scenarios such as smart factories, warehouses, laboratories, and conference rooms, joint verification of communication and positioning functions in a 6G integrated sensing prototype system, and a low-cost millimeter-wave positioning experimental system built using an external phased array combined with a software-defined radio platform. Overall, this invention is fully adapted to the system structure characteristics of external millimeter-wave phased array single-port access baseband devices. It innovatively adopts the technical approach of adjusting the beam in the neighborhood of the main alignment beam. Relying on the equivalent CSI spatial difference characteristics, frequency domain correlation characteristics, and time domain dynamic change characteristics of the multi-beam state within the main beam neighborhood, it completes the device position estimation and continuous trajectory tracking. The entire process does not require high-precision array element-level CSI data, avoiding the cumbersome process of full-space beam scanning in traditional solutions. It significantly reduces the hardware deployment complexity and beam training resource overhead of millimeter-wave positioning and sensing systems, and significantly improves the practicality and scenario adaptability of millimeter-wave communication and sensing integration technology.

[0049] This invention, through its external millimeter-wave phased array architecture, effectively extends ordinary baseband equipment, software-defined radio platforms, or Sub-6GHz communication platforms to millimeter-wave beamforming applications without requiring full digital array-level hardware modifications to the baseband equipment. Furthermore, this solution is compatible with single-port output system architectures of external millimeter-wave phased arrays. The baseband equipment does not need to acquire the independent received signals of each element within the phased array; it can achieve user equipment positioning and tracking solely based on the equivalent CSI under different beam states, significantly reducing the complexity of system hardware deployment and implementation. This invention eliminates the need for a full-space beam scan for positioning tasks; instead, it performs local neighborhood beam adjustments after the main communication beam is aligned, significantly reducing beam training overhead and positioning latency. Simultaneously, it utilizes the equivalent CSI differences among multiple neighboring beams near the main aligned beam to construct spatial differential features, which can more sensitively reflect the user equipment's positional offset relative to the main beam center direction. In addition, this invention introduces… The frequency domain correlation feature Cf is further divided into intra-beam frequency domain correlation features and cross-beam frequency domain correlation features. It can simultaneously characterize the frequency domain structure characteristics of subcarriers within the same beam and the frequency domain differences between different beams, effectively enhancing the ability to perceive changes in the spatial position of user equipment. At the same time, the continuous-time CSI change feature Gt is introduced, which can accurately describe the motion trend of user equipment based on the CSI change law at multiple times, greatly improving the continuity and stability of positioning and tracking. This invention uses the communication beam alignment result as the positioning prior and constructs a collaborative working mechanism for communication link maintenance and user equipment positioning and tracking. It can achieve position estimation and trajectory tracking through various methods such as weighted beam centroid, analytical model, machine learning model or state filtering model. It has strong feasibility and scalability. The overall solution is compatible with millimeter-wave communication sensing integrated system and can be widely used in various practical application scenarios such as indoor positioning, terminal tracking, smart base stations, beam management assistance, and 6G sensing integrated prototype verification.

[0050] In summary, this embodiment first performs beam alignment between the external millimeter-wave phased array configured on the base station side and the user equipment to determine the main alignment beam used for communication between the base station and the user equipment. Then, using the beam direction corresponding to the main alignment beam as the center, multiple neighborhood adjustment beams are generated within its angular neighborhood. This expands the beam detection range based on the precise main beam direction, overcoming the limitations of a single main beam detection angle. This lays the foundation for subsequent comprehensive and refined acquisition of channel data from different angles, effectively avoiding the problems of partial information acquisition and insufficient angle coverage caused by single-beam detection. Next, the base station controls the external millimeter-wave phased array to sequentially switch to the multiple neighborhood adjustment beams, receiving signals transmitted by the user equipment under each neighborhood adjustment beam to obtain the equivalent channel state information corresponding to each neighborhood adjustment beam. Through multi-beam sequential detection, channel transmission characteristic data under different spatial angles can be comprehensively collected, enriching the data. The channel information dimension compensates for the deficiency of single-beam channel data in reflecting spatial channel differences, providing sufficient and detailed data support for subsequent feature extraction and location estimation. Then, feature extraction is performed on the equivalent channel state information corresponding to each neighborhood adjustment beam to obtain multi-beam spatial differential features. The location state of the user equipment is estimated based on these multi-beam spatial differential features, overcoming the shortcomings of insufficient positioning accuracy in traditional single-channel feature positioning. Accurate preliminary estimation of the user equipment's location state is achieved based on multi-beam spatial differential features. Then, based on the location state, a user equipment state tracking model is constructed and filtered state estimation is implemented to obtain the user equipment state. This effectively filters out noise interference and errors caused by channel fluctuations during signal transmission, eliminates abnormal deviations in the positioning data, and achieves accurate tracking and stable output of the user equipment's real-time motion state and location information, significantly improving the smoothness and accuracy of equipment state detection. Overall, this invention, through a complete closed-loop technical process of main beam alignment, neighborhood multi-beam channel acquisition, spatial differential feature extraction, and filter state tracking, fully leverages the advantages of millimeter-wave phased arrays, such as flexible angle adjustment and high spatial resolution. It progressively completes the location perception and state tracking of user equipment, effectively solving the problems of single beam coverage, limited channel data dimensions, large positioning errors, and unstable state tracking in traditional millimeter-wave positioning methods. Furthermore, it significantly improves the accuracy, robustness, and real-time performance of user equipment location detection in complex communication scenarios, adapting to dynamically changing wireless communication environments and providing reliable technical support for accurate positioning, real-time user status monitoring, and efficient communication scheduling in millimeter-wave communication systems.

[0051] like Figure 8As shown in the illustration, this embodiment also provides a user equipment positioning and tracking system based on neighboring beam adjustment of an external millimeter-wave phased array. This system includes: a neighboring beam adjustment generation module 10, an equivalent channel state information acquisition module 20, a location state acquisition module 30, and a user equipment state acquisition module 40. Specifically, the neighboring beam adjustment generation module 10 is used to perform beam alignment between the external millimeter-wave phased array configured on the base station side and the user equipment, determine the main alignment beam used for communication between the base station and the user equipment, and generate multiple neighboring beam adjustment beams within their angular neighborhood, centered on the beam direction corresponding to the main alignment beam. The equivalent channel state information acquisition module 20 is used to control the external millimeter-wave phased array to sequentially switch to the multiple neighboring beam adjustment beams through the base station, and receive signals sent by the user equipment under each neighboring beam adjustment beam to obtain the equivalent channel state information corresponding to each neighboring beam adjustment beam. The location state acquisition module 30 is used to extract features from the equivalent channel state information corresponding to each neighborhood adjustment beam to obtain multi-beam spatial differential features, and estimate the location state of the user equipment based on the multi-beam spatial differential features. The user equipment state acquisition module 40 is used to construct a user equipment state tracking model based on the location state and perform filtered state estimation to obtain the user equipment state.

[0052] In one implementation, the neighborhood-adjusted beam generation module 10 includes: The communication quality index measurement unit is used to control an external millimeter-wave phased array through a base station to form multiple candidate beams according to a preset codebook, and to measure the communication quality index under different candidate beams. The main alignment beam determination unit is used to determine the main alignment beam for communication between the base station and the user equipment based on the communication quality indicators.

[0053] In one implementation, the location status acquisition module 30 includes: Angle state acquisition unit is used to obtain the angle state of the user equipment relative to the base station based on the normalized power corresponding to the neighborhood adjustment beam and the beam center direction of the neighborhood adjustment beam in the multi-beam spatial differential characteristics. The location status acquisition unit is used to obtain the location status of the user equipment based on the angle status and the distance estimate between the user equipment and the base station.

[0054] In one implementation, the user equipment status acquisition module 40 includes: The user equipment current state vector construction unit is used to calculate the motion speed based on the position state and combined with the historical position, and to construct the user equipment current state vector based on the position state and the motion speed. The current position observation value acquisition unit is used to extract the position component in the current state vector of the user equipment to obtain the current position observation value; The unit for obtaining the prior state prediction value and the corresponding prediction covariance at the current moment is used to input the optimal state of the user equipment output by the filter at the previous moment into the preset state transition model and calculate the prior state prediction value and the corresponding prediction covariance at the current moment. The user equipment status acquisition unit is used to input the prior state prediction value, prediction covariance and position observation value into the filtering update process, complete the iterative update, and obtain the optimized user equipment status at the current time.

[0055] The working principle of each module in the user equipment positioning and tracking system based on the external millimeter-wave phased array neighborhood beam adjustment in this embodiment is the same as the principle of each step in the above method embodiment, and will not be repeated here.

[0056] Based on the above embodiments, the present invention also provides a terminal, the principle block diagram of which can be as follows: Figure 9 As shown. The terminal may include one or more processors 100 ( Figure 9 (Only one is shown in the image), memory 101, and a computer program 102 stored in memory 101 and executable on one or more processors 100, such as a user equipment location and tracking program based on external millimeter-wave phased array neighborhood beamforming. When one or more processors 100 execute computer program 102, they can implement the various steps in the user equipment location and tracking method embodiment based on external millimeter-wave phased array neighborhood beamforming. Alternatively, when one or more processors 100 execute computer program 102, they can implement the functions of various modules / units in the user equipment location and tracking method embodiment based on external millimeter-wave phased array neighborhood beamforming, which is not limited here.

[0057] In one embodiment, the processor 100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0058] In one embodiment, memory 101 may be an internal storage unit of an electronic device, such as a hard drive or RAM. Memory 101 may also be an external storage device of the electronic device, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, memory 101 may include both internal and external storage units. Memory 101 is used to store computer programs and other programs and data required by the terminal. Memory 101 can also be used to temporarily store data that has been output or will be output.

[0059] Those skilled in the art will understand that Figure 9 The schematic diagram shown is merely a partial structural diagram related to the present invention and does not constitute a limitation on the terminal to which the present invention is applied. A specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0060] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, operational databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual operating data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A user equipment positioning and tracking method based on neighboring beam adjustment of an external millimeter-wave phased array, characterized in that, The method includes: The base station performs beam alignment with the user equipment by using an external millimeter-wave phased array configured on the base station side to determine the main alignment beam used for communication between the base station and the user equipment, and generates multiple neighborhood adjustment beams in the angular neighborhood of the beam direction corresponding to the main alignment beam as the center. The base station controls the external millimeter-wave phased array to switch sequentially to the multiple neighborhood adjustment beams, and receives signals sent by user equipment under each neighborhood adjustment beam to obtain the equivalent channel state information corresponding to each neighborhood adjustment beam. Feature extraction is performed on the equivalent channel state information corresponding to each neighborhood adjustment beam to obtain multi-beam spatial differential features, and the location state of the user equipment is estimated based on the multi-beam spatial differential features. Based on the location state, a user equipment state tracking model is constructed and filtered state estimation is performed to obtain the user equipment state.

2. The user equipment positioning and tracking method based on neighboring beam adjustment of an external millimeter-wave phased array according to claim 1, characterized in that, The external millimeter-wave phased array is connected to the baseband device on the base station side through a single radio frequency port.

3. The user equipment positioning and tracking method based on neighboring beam adjustment of an external millimeter-wave phased array according to claim 1, characterized in that, The determination of the primary alignment beam used for communication between the base station and the user equipment includes: The base station controls an external millimeter-wave phased array to form multiple candidate beams according to a preset codebook, and measures the communication quality indicators under different candidate beams. Based on the communication quality indicators, the main alignment beam used for communication between the base station and user equipment is determined.

4. The user equipment positioning and tracking method based on neighboring beam adjustment of an external millimeter-wave phased array according to claim 1, characterized in that, The step of estimating the location status of the user equipment based on the multi-beam spatial differential features includes: Based on the normalized power of the neighborhood adjustment beam and the beam center direction of the neighborhood adjustment beam in the multi-beam spatial differential characteristics, the angle state of the user equipment relative to the base station is obtained. Based on the angle state and the estimated distance between the user equipment and the base station, the location state of the user equipment is obtained.

5. The user equipment positioning and tracking method based on neighboring beam adjustment of an external millimeter-wave phased array according to claim 1, characterized in that, The step of constructing a user equipment state tracking model based on the location state and performing filtered state estimation to obtain the user equipment state includes: Based on the location state, the motion speed is calculated by combining the historical location, and the current state vector of the user equipment is constructed based on the location state and the motion speed. Extract the position component from the current state vector of the user equipment to obtain the current position observation value; The optimal state of the user equipment output from the previous time step is input into the preset state transition model, and the predicted value of the prior state at the current time step and the corresponding prediction covariance are calculated. The prior state prediction value, prediction covariance and position observation value are input into the filtering update process to complete the iterative update and obtain the optimized user equipment state at the current time.

6. The user equipment positioning and tracking method based on neighboring beam adjustment of an external millimeter-wave phased array according to claim 5, characterized in that, The optimized user equipment state at the current moment, followed by: The optimized user equipment state at the current moment is used as the input to the state transition model at the next moment. The user equipment is continuously tracked in a loop to obtain the user equipment's motion trajectory.

7. The user equipment positioning and tracking method based on neighboring beam adjustment of an external millimeter-wave phased array according to claim 6, characterized in that, The process of obtaining the user equipment's motion trajectory then includes: The current user equipment orientation is obtained. If the current user equipment orientation deviates from the center of the current main alignment beam by more than a preset threshold, the main alignment beam orientation is updated through the base station, and a neighboring beam set is regenerated around the updated main alignment beam.

8. A user equipment positioning and tracking system based on external millimeter-wave phased array neighborhood beam adjustment, characterized in that, The system includes: The neighborhood adjustment beam generation module is used to perform beam alignment with the user equipment through an external millimeter-wave phased array configured on the base station side, determine the main alignment beam used for communication between the base station and the user equipment, and generate multiple neighborhood adjustment beams in its angular neighborhood with the beam direction corresponding to the main alignment beam as the center. The equivalent channel state information acquisition module is used to control the external millimeter-wave phased array to switch sequentially to the multiple neighboring adjustment beams through the base station, and to receive the signal sent by the user equipment under each neighboring adjustment beam to obtain the equivalent channel state information corresponding to each neighboring adjustment beam. The location status acquisition module is used to extract features from the equivalent channel status information corresponding to each neighborhood adjustment beam to obtain multi-beam spatial differential features, and estimate the location status of the user equipment based on the multi-beam spatial differential features. The user equipment status acquisition module is used to construct a user equipment status tracking model and perform filtered status estimation based on the location status to obtain the user equipment status.

9. A terminal, characterized in that, The terminal includes a memory, a processor, and a user equipment positioning and tracking program based on external millimeter-wave phased array neighborhood beam adjustment stored in the memory and executable on the processor. When the processor executes the user equipment positioning and tracking program based on external millimeter-wave phased array neighborhood beam adjustment, it implements the steps of the user equipment positioning and tracking method based on external millimeter-wave phased array neighborhood beam adjustment as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a user equipment positioning and tracking program based on the neighboring beam adjustment of an external millimeter-wave phased array. When the user equipment positioning and tracking program based on the neighboring beam adjustment of an external millimeter-wave phased array is executed by a processor, it implements the steps of the user equipment positioning and tracking method based on the neighboring beam adjustment of an external millimeter-wave phased array as described in any one of claims 1-7.

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