Low-altitude unmanned aerial vehicle cooperative sensing system based on 5G-A communication and sensing integration

Abstract

A low-altitude unmanned aerial vehicle cooperative sensing system based on 5G-A communication and sensing integration comprises a plurality of 5G-A communication and sensing integration base stations deployed on the ground, a plurality of low-altitude flight unmanned aerial vehicle nodes and a cooperative sensing dispatching center located on the core network side, and the 5G-A communication and sensing integration base stations are provided with millimeter wave large-scale MIMO antenna arrays integrating communication and radar sensing functions. The unmanned aerial vehicle node is used for transmitting communication signals and sensing detection beams to the air at the same time, the unmanned aerial vehicle node carries a communication sensing fusion terminal supporting a 5G-A air interface protocol, and the terminal comprises a radio frequency receiving and transmitting module, a beam forming processor and a local sensing data caching unit. According to the invention, the millimeter wave large-scale MIMO antenna array of the 5G-A communication and sensing integrated base station is utilized and is divided into the communication sub-array and the sensing sub-array which are independent and controllable, so that the extreme multiplexing of a frequency spectrum and hardware resources is realized, and the communication and the sensing can run in parallel on the same frequency band and the same equipment without interfering with each other.

Description

Technical Field

[0001] This invention belongs to the field of UAV sensing technology, specifically a low-altitude UAV collaborative sensing system based on 5G-A integrated sensing. Background Technology

[0002] With the rapid development of the urban low-altitude economy, applications such as logistics drones, aerial inspection, emergency communications, and future urban air mobility (UAM) are creating an urgent need for real-time, high-precision, and comprehensive low-altitude airspace perception capabilities. However, existing low-altitude perception technologies exhibit the following technical problems when facing complex urban environments, high-density aircraft swarms, and dynamically changing targets: First, traditional solutions generally separate communication and sensing functions: relying on independently deployed radar systems (such as primary / secondary radars, millimeter-wave radars, or ADS-B receiving stations) for target detection, while simultaneously using 4G / 5G communication networks for UAV control and data transmission. Because the communication and sensing systems lack native coordination and cannot share waveform, timing, and beam information, this results in large sensing blind spots, low update rates, and ambiguous target identification. Especially in the millimeter-wave band, traditional radars, limited by antenna aperture and transmission power, struggle to achieve high-resolution detection at low altitudes; while communication base stations completely lack sensing capabilities, creating a structural defect of "seeing but not transmitting, transmitting but not seeing."

[0003] Secondly, existing drones generally function only as passive communication terminals. Their onboard sensors (such as cameras and lidar) are limited by power consumption, line-of-sight range, and weather conditions, making it difficult to support all-weather, all-airspace perception. Even though some research attempts to introduce onboard radar, it is difficult to deploy on a large scale due to issues of size, cost, and electromagnetic compatibility. Existing systems lack an effective compensation mechanism for the coupling relationship between the drone's own motion state and the sensing signals. When the drone maneuvers at high speed, its platform jitter will severely pollute the echo Doppler frequency shift, leading to distorted velocity estimation and thus affecting trajectory tracking accuracy. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, the present invention provides a low-altitude UAV collaborative perception system based on 5G-A integrated sensing, so as to at least partially solve the above-mentioned technical problems.

[0005] The technical solution adopted in this invention is as follows: This invention proposes a low-altitude unmanned aerial vehicle (UAV) cooperative sensing system based on 5G-A sensing integration, comprising: Multiple 5G-A integrated sensing base stations deployed on the ground, several low-altitude flying drone nodes, and a collaborative sensing and dispatch center located on the core network side; The 5G-A integrated sensing base station is equipped with a millimeter-wave massive MIMO antenna array that integrates communication and radar sensing functions. It is used to simultaneously transmit communication signals and sensing and detection beams into the air, and receive uplink communication signals from UAV nodes and echo signals reflected by target objects. The drone node is equipped with a communication and sensing fusion terminal that supports the 5G-A air interface protocol. The terminal includes a radio frequency transceiver module, a beamforming processor, and a local sensing data cache unit. The collaborative sensing and scheduling center is connected to each 5G-A integrated sensing base station through the Xn interface. It is used to aggregate the raw sensing data reported by each base station and the auxiliary sensing information uploaded by the UAV node, and generate a unified low-altitude target trajectory map based on the spatiotemporal alignment algorithm. The 5G-A integrated sensing base station and the UAV node adopt a time-division duplex frame structure, which divides the communication time slot and sensing time slot within the same subframe. The two achieve orthogonal multiplexing of communication and sensing functions in the time dimension through a predefined time slot ratio.

[0006] In one embodiment of the present invention, the millimeter-wave massive MIMO antenna array in the 5G-A integrated sensing base station consists of at least 64 antenna elements and is divided into multiple subarrays. Each subarray is independently configured with a digital beamformer. One part of the subarray is dedicated to sending downlink control information and user data to UAV nodes, while another part of the subarray periodically transmits linear frequency modulated continuous waves as sensing and detection signals. The beam pointing of each subarray is dynamically coordinated by the beam scheduler on the base station side.

[0007] In one embodiment of the present invention, the communication and sensing fusion terminal of the UAV node integrates a dual-channel radio frequency front-end. The first channel is connected to a 5G-A communication demodulator for processing downlink communication signals from the base station; the second channel is connected to a sensing signal processor for acquiring and digitizing the sensing echo signal transmitted by the base station and reflected back by the target. The two channels share the same antenna interface, but the connection path is switched between the sensing time slot and the communication time slot by a radio frequency switch to ensure that only one channel is active at any given time.

[0008] In one embodiment of the present invention, the collaborative sensing scheduling center is equipped with a spatiotemporal data alignment engine. The engine receives raw sensing point cloud data with timestamps from different 5G-A integrated sensing base stations and local sensing snapshots uploaded by UAV nodes after correction based on their own position and attitude information. The spatiotemporal data alignment engine projects all sensing data onto a unified Earth coordinate system based on the global synchronization clock source of each base station and UAV node, and uses a Kalman filter to fuse the position estimates of the same target under multi-source observation, and outputs a high-confidence target trajectory sequence.

[0009] In one embodiment of the present invention, the base station calculates the target's position, velocity, and size parameters by analyzing the Doppler frequency shift, angle of arrival, and round-trip time delay of the received reflected signal; simultaneously, the UAV node transmits the pose data output by its onboard inertial measurement unit and global navigation satellite system module back to the base station within the communication time slot to assist the base station in performing motion compensation on the perception results.

[0010] In one embodiment of the present invention, the system further includes a perception task allocation module deployed on an edge computing node. The module dynamically assigns some UAV nodes to enter the "perception enhancement mode" based on the distribution density, remaining power, and communication link quality of the UAV nodes in the current airspace. In this mode, the assigned drone node configures the sensing signal processor of its communication sensing fusion terminal to cooperative reception mode, receives the sidelobe or scattering components of the sensing detection signals transmitted by the adjacent base station, and transmits the received raw IQ samples back to the cooperative sensing scheduling center through the uplink to supplement the sensing loss caused by the main base station due to obstruction or beam coverage blind spots.

[0011] In one embodiment of the present invention, in the frame structure of the 5G-A integrated sensing base station, the duration of the sensing time slot is fixed at 0.5 milliseconds, and the duration of the communication time slot is variable, ranging from 0.5 to 2 milliseconds. The sum of the two constitutes a complete 5-millisecond superframe. The base station dynamically adjusts the ratio of the sensing time slot to the communication time slot through higher-layer signaling according to the number of targets in the airspace and the dynamic change rate, and broadcasts the ratio information to all associated UAV nodes through the physical broadcast channel, so that each UAV node synchronously adjusts the switching sequence of its radio frequency switch.

[0012] In one embodiment of the present invention, the local sensing data caching unit of the UAV node adopts a ring buffer structure with a capacity of 100 milliseconds of raw sensing data. When the UAV node detects that the uplink channel quality is lower than a preset threshold, it automatically starts the local caching mechanism to temporarily store the IQ samples output by the sensing signal processor in the caching unit. After the link is restored, the cached data is segmented and packaged in chronological order and uploaded to the collaborative sensing scheduling center through the PUSCH channel to ensure the integrity and timing consistency of the sensing data.

[0013] In one embodiment of the present invention, the collaborative sensing dispatch center and each 5G-A integrated sensing base station exchange base station location, antenna orientation, and beam configuration parameters through the Xn-C control plane interface, and transmit raw sensing point cloud data through the Xn-U user plane interface; the Xn-U interface adopts an extended encapsulation format based on the GTP-U protocol, adding a sensing metadata field after the standard GTP header. The field includes the transmit beam ID, sensing time slot number, carrier frequency, and antenna subarray index, which are used to accurately reconstruct the spatial-temporal-frequency three-dimensional context of the sensing signal on the dispatch center side.

[0014] In one embodiment of the present invention, the system further deploys a perception security verification module on the core network side. The module performs source authentication and integrity verification on the received perception auxiliary data from the UAV node. Specifically, before uploading perception data, the UAV node uses its pre-set private key to digitally sign the data packet. The perception security verification module uses the corresponding public key to verify the validity of the signature and compares whether the UAV identity identifier in the data packet is consistent with the network registration information. If the verification fails, the data packet is discarded and an abnormal event log is recorded to prevent malicious nodes from injecting false perception information to interfere with the collaborative perception results.

[0015] The beneficial effects of the technical solution of this invention are as follows: This invention utilizes a millimeter-wave massive MIMO antenna array (≥64 elements) of a 5G-A integrated sensing base station and divides it into independently controllable communication subarrays and sensing subarrays to achieve ultimate reuse of spectrum and hardware resources. This allows communication and sensing to operate in parallel on the same frequency band and the same device without interfering with each other: the communication subarray focuses on high-reliability data transmission, ensuring the transmission of UAV control commands and status; the sensing subarray periodically transmits linear frequency modulated continuous wave (LFMCW), providing centimeter-level distance resolution and sub-meter-level angular accuracy.

[0016] The communication-sensing fusion terminal carried by the UAV of this invention adopts a dual-channel RF front-end and a shared antenna interface, and switches between the communication time slot and the sensing time slot in milliseconds through a high-speed RF switch to ensure that only a single functional channel is active at any given time. The UAV not only acts as a communication terminal, but also as a mobile sensing probe. In the sensing time slot, it passively receives the reflected echo of the signal transmitted by the base station, and actively transmits high-precision GNSS / IMU pose data back in the communication time slot, providing motion compensation reference for the ground base station. This effectively suppresses the contamination of Doppler estimation by platform jitter and greatly improves the accuracy of velocity measurement of low, slow and small targets.

[0017] This invention's base station optimizes time slot allocation in real time based on airspace target density and dynamic change rate, and synchronizes all UAVs in the network through broadcast channels, ensuring that their radio frequency switches are strictly aligned with the switching sequence. The flexible frame structure enables the system to adaptively switch between scenarios with "high sensing load" (such as emergency response) and "high communication load" (such as high-definition video backhaul), achieving an on-demand balance between service and sensing needs. Simultaneously, the sensing task allocation module deployed on edge computing nodes further enhances the system's intelligence: by comprehensively evaluating UAV distribution, power consumption, and link quality, it dynamically assigns some nodes to enter "sensing enhancement mode," enabling them to collaboratively receive sidelobe or scattered signals from neighboring base stations. This effectively fills the sensing blind spots created by building obstructions at the main base station, forming a multi-base collaborative detection network of "main station + auxiliary UAVs," improving the non-line-of-sight (NLOS) target detection capability in complex urban environments.

[0018] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0019] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a schematic diagram of the working framework of the low-altitude UAV cooperative sensing system based on 5G-A integrated sensing proposed in an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process framework of the low-altitude UAV cooperative perception system based on 5G-A integrated sensing proposed in an embodiment of the present invention; Figure 3 This is a schematic diagram of the second process framework of the low-altitude UAV collaborative perception system based on 5G-A integrated sensing proposed in an embodiment of the present invention. Detailed Implementation

[0020] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0021] The following describes an embodiment of the present invention, a low-altitude unmanned aerial vehicle (UAV) collaborative sensing system based on 5G-A integrated sensing, with reference to the accompanying drawings.

[0022] like Figures 1 to 3 As shown, this embodiment of the invention provides a low-altitude unmanned aerial vehicle (UAV) collaborative sensing system based on 5G-A sensing integration, including multiple 5G-A sensing integration base stations deployed on the ground, several low-altitude flying UAV nodes, and a collaborative sensing scheduling center located on the core network side. The 5G-A integrated sensing base station is equipped with a millimeter-wave massive MIMO antenna array that integrates communication and radar sensing functions. It is used to simultaneously transmit communication signals and sensing beams into the air, and receive uplink communication signals from UAV nodes and echo signals reflected by target objects. The drone node is equipped with a communication and sensing fusion terminal that supports the 5G-A air interface protocol. The terminal includes a radio frequency transceiver module, a beamforming processor, and a local sensing data cache unit. The collaborative sensing and scheduling center connects to each 5G-A integrated sensing base station via the Xn interface. It is used to aggregate the raw sensing data reported by each base station and the auxiliary sensing information uploaded by the UAV nodes, and generate a unified low-altitude target trajectory map based on the spatiotemporal alignment algorithm. The 5G-A integrated sensing base station and the UAV nodes adopt a time-division duplex frame structure, which divides the communication time slot and the sensing time slot in the same subframe. The two achieve orthogonal multiplexing of communication and sensing functions in the time dimension through a predefined time slot ratio.

[0023] In specific applications, the low-altitude airspace perception system of this invention uses a 5G-A integrated sensing base station as the ground sensing anchor, low-altitude flying UAV nodes as airborne mobile sensing units, and the core network-side collaborative sensing and scheduling center as the global data fusion and decision-making hub. These three elements are tightly coupled through a unified 5G-A air interface protocol and standardized interfaces, forming a collaborative sensing network with high spatiotemporal resolution, strong robustness, and dynamic scalability. During system operation, the 5G-A integrated sensing base station continuously utilizes its built-in millimeter-wave massive MIMO antenna array to simultaneously perform communication and sensing tasks on the same hardware platform: on the one hand, it sends downlink control information and user data to the UAV nodes in the air according to the 5G-A standard protocol; on the other hand, it periodically transmits sensing and detection beams with specific modulation forms (such as linear frequency modulated continuous waves), covering a low-altitude area hundreds to kilometers above it. If the sensing beam encounters aircraft, birds, or other obstacles during propagation, it will generate reflected echoes, which will be captured by the receiving channel of the same base station. Within the preset communication time slot, the terminal receives downlink signals from the base station through the radio frequency transceiver module and transmits its own status information or service data back to the uplink. Within the sensing time slot, the terminal disables the active transmission function and instead configures its radio frequency front-end to a high-sensitivity receiving mode to collect weak echo signals transmitted by the base station and scattered by the target, or to record its own position and attitude auxiliary information and temporarily store it in the local sensing data buffer unit.

[0024] All raw sensing data collected by base stations and UAVs (including echo IQ samples, angle of arrival, Doppler shift, and round-trip delay) are timestamped and uploaded to the collaborative sensing scheduling center in real time via the Xn interface. First, based on the global synchronization clock shared by the base station and UAV nodes (e.g., based on the IEEE 1588 precision time protocol or the 5G-A system frame synchronization mechanism), heterogeneous sensing data from different spatial locations and sensor perspectives are projected onto a unified Earth coordinate system. Then, spatiotemporal alignment algorithms (such as fusion models based on Kalman filtering or multi-hypothesis tracking) are used to correlate and optimize the position and velocity estimates of the same target under multiple observation sources, eliminating false detections and missed detections caused by occlusion, multipath, or noise, and finally generating a high-confidence, continuously updated low-altitude target trajectory map. The entire system adopts a time-division duplex (TDD) frame structure, which strictly divides communication time slots and sensing time slots within each subframe. The two achieve orthogonal multiplexing in the time dimension through a predefined and dynamically adjustable time slot ratio. Although communication and sensing share the same spectrum and hardware resources, they will not interfere with each other. The base station can dynamically adjust the proportion of sensing time slots according to the current airspace complexity (such as target density and intensity of movement) through higher-layer signaling and broadcast it to all associated UAVs. The latter will then synchronously switch their radio frequency switch states accordingly, ensuring that the entire network is strictly aligned on the time axis.

[0025] In one specific implementation, the millimeter-wave massive MIMO antenna array in the 5G-A integrated sensing base station consists of at least 64 antenna elements and is divided into multiple subarrays. Each subarray is independently configured with a digital beamformer. One part of the subarray is dedicated to sending downlink control information and user data to UAV nodes, while another part of the subarray periodically transmits linear frequency modulated continuous waves as sensing and detection signals. The beam pointing of each subarray is dynamically coordinated by the beam scheduler on the base station side.

[0026] The communication and sensing fusion terminal of the drone node integrates a dual-channel radio frequency front-end. The first channel is connected to the 5G-A communication demodulator to process downlink communication signals from the base station. The second channel is connected to the sensing signal processor to collect and digitize the sensing echo signal transmitted by the base station and reflected back by the target. The two channels share the same antenna interface, but the connection path is switched between the sensing time slot and the communication time slot through a radio frequency switch to ensure that only one channel is active at any given time.

[0027] In a specific application of this invention, in the low-altitude UAV collaborative sensing system, the 5G-A integrated sensing base station and the UAV node achieve deep integration and efficient collaboration of communication and sensing functions on the same physical platform through hardware architecture and time and space resource scheduling mechanisms. During system operation, some subarrays are dynamically assigned to communication tasks, continuously transmitting downlink data streams encoded and modulated by the 5G-A physical layer to UAV nodes in the airspace, including system information, scheduling instructions, and user plane services. Meanwhile, another part of the subarrays is configured as a dedicated sensing channel, transmitting linear frequency modulated continuous wave (LFMCW) signals with high time-frequency resolution according to a preset period. This signal has excellent range and velocity resolution in the millimeter-wave band and can effectively detect various moving or stationary targets in the low-altitude environment. The beams of all subarrays are optimized in real time by the beam scheduler inside the base station based on the current airspace situation, UAV distribution, and sensing coverage requirements. For example, when a dense UAV swarm appears in a certain area, the scheduler can align the main lobe of the communication subarray with the center of the swarm to improve link quality, while scanning the beams of the sensing subarray to blind spots where there are intrusion targets in the surrounding area, avoiding spatial overlap between the communication main lobe and the sensing main lobe, which would cause self-interference or energy waste.

[0028] Meanwhile, the aerial drone nodes, acting as an extension of the sensing network, employ a dual-channel RF front-end architecture in their communication-sensing fusion terminals. This addresses the conflict between communication and sensing signal paths under a single antenna interface. Within the communication time slot defined by the system frame structure, the RF switch connects the antenna to the first channel, i.e., the 5G-A communication demodulator, to receive, synchronize, demodulate, and decode the downlink signal from the base station and prepare for uplink feedback. Once the sensing time slot is entered, the RF switch immediately switches to the second channel, connecting the antenna to a high dynamic range sensing signal processor. At this point, the terminal no longer actively transmits signals but passively receives the weak echoes transmitted by the ground base station and reflected back by aerial targets. Since sensing echoes are typically tens of dB weaker than communication signals and are mixed with multipath propagation, clutter, and noise, the second channel is specifically configured with a low-noise amplifier, a high-precision analog-to-digital converter, and a bandpass filter structure to ensure that the echo signal maintains a high signal-to-noise ratio before digitization.

[0029] In one specific implementation, the collaborative sensing scheduling center is equipped with a spatiotemporal data alignment engine. The engine receives raw sensing point cloud data with timestamps from different 5G-A integrated sensing base stations, as well as local sensing snapshots uploaded by UAV nodes after correction based on their own position and attitude information. The spatiotemporal data alignment engine projects all sensing data onto a unified Earth coordinate system based on the global synchronization clock source of each base station and UAV node, and uses a Kalman filter to fuse the position estimates of the same target under multi-source observations, outputting a high-confidence target trajectory sequence. The base station calculates the target's position, velocity, and size parameters by analyzing the Doppler frequency shift, angle of arrival, and round-trip time delay of the received reflected signals. Simultaneously, the UAV node transmits its own pose data output by its onboard inertial measurement unit and global navigation satellite system module back to the base station within the communication time slot to assist the base station in performing motion compensation on the sensing results.

[0030] In practical applications of this invention, during system operation, multiple 5G-A integrated sensing base stations deployed on the ground continuously transmit sensing and detection signals, and extract the physical characteristic parameters of the target based on the received reflected echoes. Specifically, each base station uses its millimeter-wave MIMO array to measure the angle of arrival (AoA), round-trip time (RTT), and Doppler shift of the echo signal, which correspond to the target's azimuth, range, and radial velocity, respectively. Combining the geometric layout of the antenna array and the signal processing model, the three-dimensional position, velocity, and even the size information reflected by the scattering cross-section of the target in the local coordinate system are further calculated.

[0031] Meanwhile, the aerial drone nodes not only act as communication terminals but also as mobile sensing auxiliary units participating in data acquisition. Within their communication time slots, in addition to transmitting service data, they actively transmit high-precision attitude information calculated jointly by their onboard inertial measurement unit (IMU) and Global Navigation Satellite System (GNSS) module, including three-dimensional position, heading angle, pitch angle, roll angle, and velocity vector. Furthermore, some drones with local sensing capabilities utilize their communication-sensing fusion terminals to capture environmental echoes within their sensing time slots, generating a "local sensing snapshot" after attitude correction—that is, target observation data in a local coordinate system with the drone's current position as the origin and its attitude as the reference frame.

[0032] All the aforementioned sensing data streams from ground base stations and aerial drones, upon arriving at the collaborative sensing dispatch center, are first preprocessed by a spatiotemporal data alignment engine. This engine relies on a unified global synchronization clock source across the entire network (e.g., based on 5G-A system frame synchronization or PTP precise time protocol) to ensure strict time alignment for each frame of data. Subsequently, based on the known spatial locations of each data source (base station geographic coordinates are preset by engineering surveys, and drone positions are dynamically updated by the GNSS / IMU data they report) and their coordinate system definitions, the engine projects all local observation results onto a unified Earth-fixed coordinate system (such as WGS-84 or ENU local geocentric coordinate system) using a coordinate transformation matrix. During this process, the pose data reported by the drones is not only used for coordinate transformation but also as a motion compensation factor: for example, when a drone collects echoes during high-speed maneuvers, its own motion causes Doppler frequency shifts, which, if not corrected, will severely distort the target velocity estimate; however, by introducing acceleration and angular velocity information provided by the IMU, the dispatch center can inversely compensate for the impact of platform motion on the sensing signal, thereby restoring the true dynamic characteristics of the target.

[0033] After completing spatiotemporal normalization, the engine enters the multi-source fusion stage. For the same low-altitude target, it is simultaneously observed by multiple base stations from different angles and captured by one or more UAVs from a near-field perspective, forming a redundant but complementary set of observations. At this point, the system calls a Kalman filter (or its extended forms such as EKF or UKF) to perform state estimation fusion on these heterogeneous observations: the filter uses the target's position and velocity as state variables, takes the position and velocity measurements provided by each observation source as input, and combines them with their respective covariance matrices (reflecting sensor accuracy and environmental noise levels) to iteratively update the optimal state estimate of the target. In this way, even if a base station temporarily loses the target due to obstruction, or a UAV uploads abnormal data due to link jitter, the filter can still maintain trajectory continuity relying on other reliable sources and automatically suppress the impact of outliers. Finally, the engine outputs a temporally continuous, spatially smooth, and confidence-quantified target trajectory sequence, with each point containing position, velocity, direction, and uncertainty ellipsoid information, which can be directly called by upper-layer applications (such as airspace management, intrusion alarms, and path planning).

[0034] In one specific implementation, the system also includes a perception task allocation module deployed on edge computing nodes. The module dynamically assigns some UAV nodes to enter "perception enhancement mode" based on the distribution density, remaining power, and communication link quality of UAV nodes in the current airspace. In this mode, the assigned UAV nodes configure the perception signal processor of their communication perception fusion terminal to cooperative reception mode, receive the sidelobe or scattering components of the perception detection signals transmitted by adjacent base stations, and transmit the received raw IQ samples back to the cooperative perception scheduling center via the uplink to supplement the perception loss caused by the main base station due to obstruction or beam coverage blind spots.

[0035] In the frame structure of the 5G-A integrated sensing base station, the duration of the sensing time slot is fixed at 0.5 milliseconds, while the duration of the communication time slot is variable, ranging from 0.5 to 2 milliseconds. The sum of the two constitutes a complete 5-millisecond superframe. The base station dynamically adjusts the ratio of the sensing time slot to the communication time slot through higher-layer signaling based on the number of targets and the rate of change in the airspace. The ratio information is then broadcast to all associated UAV nodes through the physical broadcast channel, enabling each UAV node to synchronously adjust the switching timing of its radio frequency switch.

[0036] In practical applications, the entire system relies on a sensing task allocation module deployed on the edge computing nodes of the 5G-A network to monitor the status information of all UAV nodes in the airspace in real time, including their three-dimensional spatial distribution density, remaining battery power, uplink / downlink signal-to-noise ratio (SNR), block error rate (BLER), and connection stability with each base station. Based on these multi-dimensional indicators, the task allocation module uses lightweight optimization algorithms (such as weighted scoring or constraint satisfaction models) to dynamically evaluate whether each UAV is currently suitable as a "sensing enhancement unit." For example, when a UAV with good communication link quality, sufficient power, and low load appears in a certain area, and this area happens to be located at the edge of the main base station's beam coverage or in a sensing blind spot caused by building obstruction, the UAV will be preferentially assigned to "sensing enhancement mode."

[0037] Once activated, the UAV immediately adjusts the working state of its communication and sensing fusion terminal: the sensing signal processor, originally used only to receive echoes from its own associated base stations, is reconfigured as a generalized cooperative receiver. It is no longer limited to the main lobe signal of the primary serving base station, but actively listens to the sidelobe energy, ground / building scattering components, and even non-line-of-sight (NLOS) path signals of sensing and detection beams emitted by multiple nearby 5G-A integrated sensing base stations. Because millimeter-wave signals are easily interrupted in complex urban low-altitude environments due to obstruction, their rich multipath components still carry indirect information about the target's presence. These "non-ideal" echoes, which were previously considered interference and filtered out, are consciously collected and utilized in this system. The UAV temporarily stores the received raw IQ samples (including amplitude, phase, timestamp, and signal source identifier) ​​in a local buffer and transmits them back to the cooperative sensing dispatch center with high priority via the uplink shared channel (PUSCH).

[0038] The efficient operation of the aforementioned perception enhancement mechanism relies on a strictly unified and dynamically reconfigurable frame structure as the time reference. The system adopts a superframe structure with a period of 5 milliseconds, in which the perception time slot occupies a fixed 0.5 milliseconds to ensure basic radar detection performance; the remaining 4.5 milliseconds are divided into one or more communication time slots of variable length, with the total length flexibly adjusted between 0.5 and 2 milliseconds. Each 5G-A integrated sensing base station makes dynamic decisions based on its real-time perception load: when a surge in the number of targets or violent movement (such as dense formation flight or high-speed crossing) is detected in the airspace, the base station will report the perception demand level upwards through RRC (Radio Resource Control) layer signaling; after the collaborative perception scheduling center summarizes the overall network situation, it instructs relevant base stations to increase the proportion of perception time slots (for example, compressing the communication time slot to 0.5 milliseconds, thereby inserting multiple perception sub-time slots within a superframe). The adjusted time slot allocation parameters are then broadcast to all associated UAVs within its coverage area through the Physical Broadcast Channel (PBCH) or System Information Block (SIB).

[0039] Upon receiving the broadcast information, all drone nodes immediately update their internal radio frequency switch switching timing tables synchronously. Within the new superframe period, they switch precisely between the communication channel and the sensing channel at the millisecond level according to the updated time slot boundaries. The network-wide synchronization mechanism ensures that even with dynamic adjustments to resource allocation, communication and sensing operations remain strictly orthogonal, avoiding self-interference. At the same time, drones assigned as sensing enhancement nodes can accurately predict the sensing transmission time of neighboring base stations, thereby opening a cooperative reception window in their corresponding time slot to maximize the probability of capturing effective scattered signals.

[0040] In one specific implementation, the local sensing data caching unit of the UAV node adopts a ring buffer structure with a capacity of 100 milliseconds of raw sensing data. When the UAV node detects that the uplink channel quality is lower than a preset threshold, it automatically activates the local caching mechanism to temporarily store the IQ samples output by the sensing signal processor in the caching unit. After the link is restored, the cached data is segmented and packaged in chronological order and uploaded to the collaborative sensing scheduling center through the PUSCH channel to ensure the integrity and timing consistency of the sensing data. The collaborative sensing scheduling center and each 5G-A integrated sensing base station exchange base station location, antenna orientation, and beam configuration parameters through the Xn-C control plane interface, and transmit the raw sensing point cloud data through the Xn-U user plane interface. The Xn-U interface adopts an extended encapsulation format based on the GTP-U protocol, adding a sensing metadata field after the standard GTP header. The field includes the transmit beam ID, sensing time slot number, carrier frequency, and antenna subarray index, which is used to accurately reconstruct the spatial-temporal-frequency three-dimensional context of the sensing signal on the scheduling center side.

[0041] In specific applications, each UAV integrates a circular buffer with a capacity of 100 milliseconds of raw sensing data within its communication-sensing fusion terminal. This buffer continuously receives IQ sample streams output from the sensing signal processor. These samples represent the digitized results of echo signals captured by the UAV within the sensing time slot, transmitted from a ground base station and reflected by an aerial target. Under normal circumstances, this data is uploaded in real-time to the collaborative sensing scheduling center via the Physical Uplink Shared Channel (PUSCH). However, in complex scenarios such as urban canyons, strong electromagnetic interference, or high-speed movement, uplink quality deteriorates instantaneously, manifesting as a Channel Quality Indicator (CQI) below a preset threshold, an excessive number of HARQ retransmissions, or a significantly increased BLER. Upon detecting such an anomaly, the UAV node immediately triggers a local caching mechanism: pausing real-time uploading and instead writing subsequently generated IQ samples into the circular buffer in chronological order. Due to the circular structure, when the buffer is full, the oldest data is automatically overwritten, thus always retaining the latest sensing information within the last 100 milliseconds, avoiding memory overflow or system blockage.

[0042] Once the uplink quality returns to a stable level, the UAV will not dump all the cached data at once. Instead, it will package the cached content into segments based on the number of available uplink resource blocks (RBs) and modulation and coding scheme (MCS), prioritizing the transmission of data that is earlier in the time sequence. A start timestamp and sequence number will be embedded in the header of each data packet. The data packets will then be uploaded in an orderly manner through the PUSCH channel. This ensures that even if there is a brief interruption, the dispatch center can still receive a complete, seamless, and time-continuous sensing and observation sequence, providing the necessary input for subsequent target tracking and trajectory smoothing.

[0043] Meanwhile, the 5G-A integrated sensing base station on the ground side is continuously transmitting its collected raw sensing point cloud data back to the collaborative sensing dispatch center. To support the dispatch center in performing high-precision fusion and 3D context reconstruction of this data, the system has deeply customized the Xn interface between the base station and the dispatch center: the control plane (Xn-C) is used to transmit the static and semi-static configuration parameters of the base station, including geographical latitude and longitude, antenna installation height, mechanical downtilt angle, and electronic beam scanning range; while the user plane (Xn-U) carries the raw sensing data stream and adopts an extended encapsulation format based on GTP-U in its transmission layer. After the standard GTP-U header, a new "sensing metadata field" is added. This field structurally records the physical layer context information corresponding to each batch of point cloud data, including the specific beam ID used for transmission (corresponding to the beam index of the base station subarray), the sensing time slot number (identifying its precise position in the superframe), the operating carrier frequency (such as the 26GHz or 28GHz band), and the active antenna subarray index (used to distinguish between the communication subarray and the sensing subarray).

[0044] When the collaborative sensing dispatch center receives any data packet, whether it's cached data from a UAV or real-time point cloud data from a base station, it can reconstruct the spatiotemporal-spectrum-beam three-dimensional context of the observation by parsing the attached timestamp and metadata. For example, a point cloud data can be located to "January 16, 2026, 09:00:05.1234, transmitted and received by subarray #3 of base station #07 in the 128th sensing time slot, using beam ID=45, on a 26.5GHz carrier". This allows the dispatch center to not only determine whether two observations point to the same target when performing multi-source data correlation, but also to assess their observation geometry (such as baseline length, viewing angle), signal coherence, and potential error sources, thus providing high-quality input features for Kalman filters or deep learning fusion models. Especially for cached data from UAVs, the dispatch center can also combine the UAV's own pose information and base station metadata to reverse-calculate the actual propagation path and scattering point location of the echo signal, achieving indirect target localization in non-line-of-sight scenarios.

[0045] In one specific implementation, the system also deploys a perception security verification module on the core network side. The module performs source authentication and integrity verification on the perception auxiliary data received from the UAV node. Specifically, before uploading perception data, the UAV node uses its pre-set private key to digitally sign the data packet. The perception security verification module uses the corresponding public key to verify the validity of the signature and compares whether the UAV identity identifier in the data packet is consistent with the network registration information. If the verification fails, the data packet is discarded and an abnormal event log is recorded to prevent malicious nodes from injecting false perception information to interfere with the collaborative perception results.

[0046] In practical applications of this invention, after each legally registered UAV completes 5G-A network attachment and device authentication, a pair of asymmetric encryption keys (such as those based on ECC or RSA algorithms) are pre-configured by the Network Management System (NMS) or Security Credential Distribution Center (SCDC). The private key is securely stored in the Trusted Execution Environment (TEE) or Hardware Security Module (HSM), and its reading by the application layer is strictly prohibited. The public key is uniformly registered by the core network and associated with the UAV's unique device identifier (such as IMEI, IMSI, or dedicated UAV-ID). When the UAV completes a local perception snapshot acquisition (e.g., acquiring echo IQ samples corrected by its own pose, GNSS / IMU fused pose, or environmental point cloud fragments) and is ready to upload it, its communication protocol stack, in the final stage of encapsulating the PUSCH uplink data packet, calls the security module to perform a hash operation on the entire perception payload (including timestamp, coordinates, original samples, and metadata), and uses the local private key to generate a digital signature for the hash value. This signature is embedded as an additional field at the end of the data packet and sent along with the perception content.

[0047] After the data packet arrives at the core network via the wireless access network, it is first routed to the independently deployed perception security verification module. This module maintains a dynamically updated database of legitimate drone public keys, where each record contains the device ID, public key, registration status, validity period, and security policy level. Upon receiving the uplink perception data packet, the verification module first extracts the drone's identity identifier carried in plaintext and checks if it is in a valid registration state. If it exists, it further retrieves the corresponding public key and performs a signature verification operation on the digital signature in the data packet. This involves using the public key to decrypt the signature to obtain the original hash value, then recalculating the hash of the data packet body, and comparing the two to see if they match. Only when the identity is legitimate, the signature is valid, and the hash matches is simultaneously true, is the data packet deemed a "trusted sensing input" and forwarded to the collaborative sensing scheduling center for subsequent spatiotemporal alignment and multi-source fusion. If any step fails (such as invalid signature, unregistered ID, mismatched public key, or data tampering), the verification module immediately discards the data packet to prevent it from contaminating the global sensing results. At the same time, it generates a structured security event log, recording the attack time, source IP / cell ID, device identifier, and anomaly type information, and triggers an alarm to be reported to the security management platform for operation and maintenance personnel to conduct source tracing analysis or initiate countermeasures (such as temporarily blacklisting the device or adjusting the airspace access policy).

[0048] In this system, because these forged data cannot pass signature verification, they will never enter the fusion engine, thus eliminating "phantom target" attacks at the source. For example, in perception enhancement mode, edge computing nodes assign specific drones as cooperative receivers. Although the sidelobe scattering data uploaded by these drones has a low signal-to-noise ratio, it can still be assigned a reasonable confidence weight by the scheduling center because it has a valid digital signature. Conversely, even if a piece of data seems reasonable, it is considered untrustworthy if it lacks valid authentication. Furthermore, to balance security and efficiency, the system adopts a lightweight signature algorithm (such as ECDSA-P256) and a batch verification strategy, ensuring millisecond-level verification latency while supporting thousands of concurrent authentication requests per second, meeting the real-time requirements of high-density, low-altitude scenarios.

[0049] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0050] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A low-altitude unmanned aerial vehicle (UAV) collaborative sensing system based on 5G-A sensing integration, characterized in that, include: Multiple 5G-A integrated sensing base stations deployed on the ground, several low-altitude flying drone nodes, and a collaborative sensing and dispatch center located on the core network side; The 5G-A integrated sensing base station is equipped with a millimeter-wave massive MIMO antenna array that integrates communication and radar sensing functions. It is used to simultaneously transmit communication signals and sensing and detection beams into the air, and receive uplink communication signals from UAV nodes and echo signals reflected by target objects. The drone node is equipped with a communication and sensing fusion terminal that supports the 5G-A air interface protocol. The terminal includes a radio frequency transceiver module, a beamforming processor, and a local sensing data cache unit. The collaborative sensing and scheduling center is connected to each 5G-A integrated sensing base station through the Xn interface. It is used to aggregate the raw sensing data reported by each base station and the auxiliary sensing information uploaded by the UAV node, and generate a unified low-altitude target trajectory map based on the spatiotemporal alignment algorithm. The 5G-A integrated sensing base station and the UAV node adopt a time-division duplex frame structure, which divides the communication time slot and sensing time slot within the same subframe. The two achieve orthogonal multiplexing of communication and sensing functions in the time dimension through a predefined time slot ratio.

2. The low-altitude UAV cooperative sensing system based on 5G-A integrated sensing as described in claim 1, characterized in that, The millimeter-wave massive MIMO antenna array in the 5G-A integrated sensing base station consists of at least 64 antenna elements and is divided into multiple subarrays. Each subarray is independently configured with a digital beamformer. One part of the subarray is dedicated to sending downlink control information and user data to UAV nodes, while another part of the subarray periodically transmits linear frequency modulated continuous waves as sensing and detection signals. The beam pointing of each subarray is dynamically coordinated by the beam scheduler on the base station side.

3. The low-altitude UAV cooperative sensing system based on 5G-A integrated sensing as described in claim 1, characterized in that, The communication and sensing fusion terminal of the UAV node integrates a dual-channel radio frequency front-end. The first channel is connected to a 5G-A communication demodulator to process downlink communication signals from the base station. The second channel is connected to a sensing signal processor to collect and digitize the sensing echo signals transmitted by the base station and reflected back by the target. The two channels share the same antenna interface, but the connection path is switched between the sensing time slot and the communication time slot by a radio frequency switch to ensure that only one channel is active at any given time.

4. The low-altitude UAV cooperative sensing system based on 5G-A integrated sensing as described in claim 1, characterized in that, The collaborative sensing scheduling center is equipped with a spatiotemporal data alignment engine. The engine receives raw sensing point cloud data with timestamps from different 5G-A integrated sensing base stations, as well as local sensing snapshots uploaded by UAV nodes after correction based on their own position and attitude information. The spatiotemporal data alignment engine projects all sensing data onto a unified Earth coordinate system based on the global synchronization clock source of each base station and UAV node, and uses a Kalman filter to fuse the position estimates of the same target under multi-source observation, outputting a high-confidence target trajectory sequence.

5. The low-altitude UAV cooperative sensing system based on 5G-A integrated sensing as described in claim 1, characterized in that, The base station calculates the target's position, velocity, and size parameters by analyzing the Doppler shift, angle of arrival, and round-trip delay of the received reflected signals. Simultaneously, the UAV node transmits its own pose data output by its onboard inertial measurement unit and global navigation satellite system module back to the base station within the communication time slot to assist the base station in performing motion compensation on the perception results.

6. The low-altitude UAV cooperative sensing system based on 5G-A integrated sensing as described in claim 1, characterized in that, The system also includes a perception task allocation module deployed on edge computing nodes. The module dynamically assigns some drone nodes to enter "perception enhancement mode" based on the distribution density, remaining power, and communication link quality of drone nodes in the current airspace. In this mode, the assigned drone node configures the sensing signal processor of its communication sensing fusion terminal to cooperative reception mode, receives the sidelobe or scattering components of the sensing detection signals transmitted by the adjacent base station, and transmits the received raw IQ samples back to the cooperative sensing scheduling center through the uplink to supplement the sensing loss caused by the main base station due to obstruction or beam coverage blind spots.

7. The low-altitude UAV cooperative sensing system based on 5G-A integrated sensing as described in claim 1, characterized in that, In the frame structure of the 5G-A integrated sensing base station, the duration of the sensing time slot is fixed at 0.5 milliseconds, while the duration of the communication time slot is variable, ranging from 0.5 to 2 milliseconds. The sum of the two constitutes a complete 5-millisecond superframe. The base station dynamically adjusts the ratio of the sensing time slot to the communication time slot through higher-layer signaling based on the number of targets and the rate of change in the airspace. The ratio information is then broadcast to all associated UAV nodes through a physical broadcast channel, enabling each UAV node to synchronously adjust the switching sequence of its radio frequency switch.

8. The low-altitude UAV cooperative sensing system based on 5G-A integrated sensing as described in claim 1, characterized in that, The local sensing data caching unit of the UAV node adopts a ring buffer structure with a capacity of 100 milliseconds of raw sensing data. When the UAV node detects that the uplink channel quality is lower than a preset threshold, it automatically starts the local caching mechanism to temporarily store the IQ samples output by the sensing signal processor in the caching unit. Once the link is restored, the cached data will be segmented and packaged in chronological order and uploaded to the collaborative sensing scheduling center via the PUSCH channel to ensure the integrity and timing consistency of the sensing data.

9. The low-altitude UAV cooperative sensing system based on 5G-A integrated sensing as described in claim 1, characterized in that, The collaborative sensing and dispatch center exchanges base station location, antenna orientation, and beam configuration parameters with each 5G-A integrated sensing base station via the Xn-C control plane interface, and transmits raw sensing point cloud data via the Xn-U user plane interface. The Xn-U interface adopts an extended encapsulation format based on the GTP-U protocol, adding a sensing metadata field after the standard GTP header. The field includes the transmit beam ID, sensing time slot number, carrier frequency, and antenna subarray index, which is used to accurately reconstruct the spatial-temporal-frequency three-dimensional context of the sensing signal on the dispatch center side.

10. The low-altitude UAV cooperative sensing system based on 5G-A integrated sensing as described in claim 1, characterized in that, The system also deploys a perception security verification module on the core network side. This module performs source authentication and integrity verification on the perception auxiliary data received from UAV nodes. Specifically, before uploading perception data, the UAV node uses its pre-set private key to digitally sign the data packet. The perception security verification module uses the corresponding public key to verify the validity of the signature and compares whether the UAV identity identifier in the data packet is consistent with the network registration information. If the verification fails, the data packet is discarded and an abnormal event log is recorded to prevent malicious nodes from injecting false perception information to interfere with the collaborative perception results.

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