A method for interaction between UAVs and marine buoys based on marine sensory multiplexing

Abstract

This application relates to the field of next-generation information technology and discloses a method for interaction between a UAV and a marine buoy based on marine sensing-charging multiplexing. The method includes: S1, defining the interaction scenario between the UAV and the marine buoy; S2, designing the UAV's signal waveform as a SBFD-AFDM-based sensing-charging multiplexed signal waveform; S3, designing a parallel duplex frame structure matching the SBFD-AFDM-based sensing-charging multiplexed signal waveform; and S4, based on the waveform designed in S2 and the parallel duplex frame structure designed in S3, realizing communication, sensing, and charging between the UAV and the marine buoy. This application enables the UAV to perform microwave-based sensing orientation and tracking during data collection from marine buoys, thereby achieving high transmission rate, high resolution, and low latency. Simultaneously, it can wirelessly charge the buoy, extending its lifespan and overcoming the current problems of single data transmission methods and low transmission rates for data from distant marine buoys.

Description

Technical Field

[0001] This application belongs to the field of next-generation information technology, and specifically relates to a method for interaction between unmanned aerial vehicles and marine buoys based on marine sensor multiplexing. Background Technology

[0002] Currently, the world's largest ocean observation system, GOOS (Global Ocean Observing System), and the widely used Argo buoy observation network both use ocean satellites, buoys, and shore-based base stations as the main communication nodes, and transmit data back along the "sea buoy-ocean satellite-shore base station" link. The ocean satellite-shore base station link can achieve high-speed data transmission at the Gbps level by equipping high-power gain antennas and laser communication technologies. Therefore, the total transmission rate of ocean observation data is mainly limited by the sea buoy-ocean satellite link.

[0003] Because the buoy-ocean satellite link is too long and affected by wave fluctuations and the marine atmosphere, the ocean satellite cannot provide high-resolution and low-latency position calibration, making it difficult to establish a stable communication link with the buoy and meet the demand for real-time, high-capacity data backhaul. Furthermore, due to the limited power supply capacity of the buoy, the system needs to reduce signal transmission power and data backhaul frequency to ensure long-term operation, thereby compressing the data backhaul capacity per unit time and affecting communication efficiency.

[0004] With the continuous improvement of the payload and long-range flight capabilities of unmanned aerial vehicles (UAVs), maritime UAVs equipped with communication modules are becoming an important component of cross-domain transmission in marine observation networks, and are expected to fill the gap in airspace platforms in traditional marine observation networks. As a flexibly deployable airspace platform, UAVs can achieve high-resolution microwave sensing, orientation, and tracking, high-speed communication, and efficient wireless charging with marine observation equipment. They can also utilize high-performance radio frequency modules to establish stable and high-speed communication links with satellites in complex sea conditions. However, considering the limited payload and energy of UAVs, how to achieve multi-functional transmission (i.e., communication + sensing + charging) of maritime buoys using limited resources in practical scenarios has not yet been publicly reported by researchers both domestically and internationally. Summary of the Invention

[0005] The technical problem to be solved by this application is to overcome the shortcomings of the prior art. This application provides a method for interaction between a UAV and a marine buoy based on marine sensing and charging multiplexing. This method enables the UAV to perform microwave-based sensing orientation and tracking during the collection of marine buoy data, thereby achieving high transmission rate, high resolution and low latency. At the same time, it can wirelessly charge the buoy, extend the life cycle of the buoy, and make up for the problems of the single data transmission method and low transmission rate of the current offshore buoy data transmission.

[0006] To achieve the above objectives, this application provides a method for interaction between a UAV and a marine buoy based on marine sensor-multiplexing, comprising the following steps:

[0007] S1. Define the interaction scenario between the drone and the marine buoy:

[0008] Define a marine buoy interaction scenario based on the multiplexing of sensing and charging capabilities of a marine UAV, including a UAV, a ground base station, and a marine buoy. Define the UAV as... Each marine buoy provides a sensing and charging service and transmits the collected buoy information back to the ground base station;

[0009] S2. The signal waveform of the drone designed based on the interactive scenario defined in S1 is an inductively charged multiplexed signal waveform based on SBFD-AFDM:

[0010] The SBFD-AFDM-based inductive charging multiplexed signal waveform modulates multiple time-overlapping information symbols onto a dynamic chirped signal, utilizing time-frequency two-dimensional orthogonality to replace traditional frequency-domain orthogonality, thereby achieving multiplexing and microwave sensing directionality.

[0011] S3. Design a parallel duplex frame structure to match the SBFD-AFDM-based inductively charged multiplexed signal waveform:

[0012] In the initial stage of the system, full-band beam scanning is first performed to obtain the precise position information of the buoy. After entering the stable working stage, wireless charging, data collection and beam tracking are processed in parallel by optimizing resource allocation. During the entire flight of the UAV, the beam scanning, wireless charging, data collection and beam tracking steps are repeated for each buoy in sequence to ensure that the preset buoy communication information rate and minimum charging energy value are reached.

[0013] S4, based on the waveform designed by S2 and the parallel duplex frame structure designed by S3, enables communication, sensing and charging between the UAV and the marine buoy.

[0014] Preferably, in the design of the SBFD-AFDM-based inductive charging multiplexed signal waveform in S2, the chirp parameter... Controlling time-domain scalability, chirp parameters Controlling frequency domain scalability, chirp parameters and chirp parameters Together, they determine the extended shape of the SBFD-AFDM-based inductively charged multiplexed signal waveform in the time-delay-Doppler domain, as well as the orthogonality and anti-interference capability between chirped signals;

[0015] chirp parameters The following design is proposed:

[0016] When the channel's time-delay impulse response exhibits sparsity, to ensure that the DAFT impulse response distributions of each path do not overlap, for the integer Doppler frequency shift case, the chirp parameter... Set to:

[0017] (1);

[0018] in, This is the maximum Doppler extended index that the system can accommodate. Number of chirp signals, and Paths and path Integer delay;

[0019] For the fractional Doppler frequency shift case, the chirp parameter Selected as:

[0020] (2);

[0021] in, and Let represent the spread compensation amount caused by the fractional Doppler frequency shift and the maximum observable integer Doppler index, respectively, and they must satisfy . ;

[0022] If the minimum time delay interval between any two paths in the system satisfies Then, the above formulas (1) and (2) simplify to:

[0023] (3);

[0024] (4);

[0025] For chirp parameters Choose any irrational number or a number less than 0. The rational number is used to improve the spectral conformality of the signal under Doppler spread, but the chirp parameter... The value does not directly affect the management of interference between paths.

[0026] Preferably, in the interaction scenario between drones and marine buoys:

[0027] Signals transmitted by the drone transmitter It includes sensing signals and charging signals, wherein the sensing signals are used for microwave sensing orientation and beam alignment of the marine buoy, and the charging signals are used for wirelessly transmitting energy to the marine buoy;

[0028] Define the total number of chirped signals as The set formed by them is denoted as Based on communication, sensing, and charging, they are uniformly divided into three non-overlapping subsets. ;

[0029] Based on the chirp signal function mapping, the sensing signal and the charging signal are modulated onto two orthogonal chirp signal sets respectively, i.e., chirp signal set allocation. The sensing signal, the chirping signal set To the charging signal, .

[0030] Preferably, in the interaction scenario between drones and marine buoys:

[0031] Signal received by the drone receiver It includes sensing echo signals and uplink data. The sensing echo signals are used for microwave sensing orientation and beam feedback of the marine buoy, while the uplink data is used to receive transmitted information from the marine buoy.

[0032] The sensed echo signal and the uplink data are respectively mapped to different chirped signal sets, and the sensed echo signal is assigned to a chirped signal set. The uplink data is assigned to a chirped signal set. ,in This enables signal separation in the DAFT domain;

[0033] Finally, after digital precoding, the different functional signals are demodulated and optimized to recover the sensing echo signal and the uplink data, thus completing the signal extraction and data decoding.

[0034] Preferably, after S3, a step of optimizing the inductive charging multiplexing performance is included, including:

[0035] Define that each time slot of the drone can only perform one mission for a single buoy or TBS;

[0036] The optimization objective is to maximize radar mutual information, and the optimization variables are UAV mission scheduling, UAV power allocation, and UAV flight trajectory.

[0037] The initial optimization problem (P1) is divided into three parts: UAV mission scheduling optimization (P2), UAV power allocation optimization (P3), and UAV flight trajectory optimization (P4).

[0038] Preferably, the initial optimization problem (P1) is expressed as:

[0039] (10);

[0040] Among them, drone mission scheduling is represented as The power allocation of the drone is represented as The drone's flight trajectory is represented as , and Two discrete binary variables represent time slots. Drones on buoys Scheduling of synesthetic charging and data feedback. and They represent time slots respectively. Power allocation factors for drone sensing and charging. Indicates time slot The flight speed of the UAV; constraints 10a-10c are task scheduling restrictions, defining that the UAV can only perform one task for one buoy or TBS per time slot; constraints 10d-10e are basic communication and power supply guarantees. and These represent the minimum values ​​for communication and charging between the drone and the buoy, respectively. and They represent time slots respectively. Drone receiving buoy Information rate and buoy The charging value; constraint 10f is the data return limit, where and They represent time slots respectively. Drones on buoys Radar mutual information and data return rate; constraint 10g is the UAV transmit power limit, in which This represents the maximum transmit power of the UAV; constraint 10h-10n represents the flight constraints of the UAV, where... and They represent time slots respectively. and time slot The drone's horizontal position and They represent time slots respectively. and time slot The flight altitude of the drone and They represent time slots respectively. The horizontal and vertical flight speeds of the drone For each coherent time slot duration, and They represent time slots respectively. The horizontal and vertical flight speeds of the drone and These represent the maximum horizontal and vertical flight accelerations of the drone, respectively. and These represent the maximum horizontal and vertical flight speeds of the drone, respectively. and These represent the minimum and maximum flight altitude limits for the drone, respectively.

[0041] Preferably, for solving UAV mission scheduling Discrete binary variables and relaxation Given continuous variables within a given range, and considering fixed optimization problems (P3) and (P4), we present the UAV mission scheduling optimization problem (P2), expressed as:

[0042] (11);

[0043] Among them, constraints 11a-11c are task scheduling restrictions, constraints 11d-11e are basic communication and power supply guarantees, and constraint 11f is a data return restriction.

[0044] Preferably, given the fixed optimization problem (P2) and the optimization problem (P4), the UAV power allocation optimization problem (P3) is given as follows:

[0045] (12);

[0046] Among them, constraints 12a-12b represent basic communication and power supply guarantees, constraint 12c is a data return restriction, and constraints 12d-12f are UAV power allocation and transmission power restrictions.

[0047] Preferably, given the fixed optimization problem (P2) and the optimization problem (P3), the UAV flight trajectory optimization problem (P4) is given as follows:

[0048] (13);

[0049] Among them, constraints 13a-13b are basic communication and power supply guarantees, constraint 13c is data transmission restriction, and constraints 13d-13j are UAV flight constraints.

[0050] By adopting the above technical solution, this application has the following beneficial effects compared with the prior art:

[0051] In this application, a multiplexed signal waveform based on sub-band full-duplex simulated radio frequency multiplexing is designed for inductive charging, and a transmission frame structure adapted to this waveform is proposed to efficiently realize three functions: beam scanning / tracking, wireless charging, and data collection between UAVs and marine buoys. This waveform enables UAVs to perform inductive charging tasks simultaneously most of the time, greatly reducing cross-link interference and thus improving the system's inductive charging performance. Furthermore, this waveform can effectively combat Doppler frequency shift and time delay changes caused by high-speed movement, exhibiting good communication performance in high-mobility scenarios.

[0052] This application addresses the resource competition problem among communication, sensing, and charging functions in a multi-channel multiplexed system for maritime unmanned aerial vehicles (UAVs). Focusing on UAV mission scheduling, power allocation, and flight trajectory optimization, it proposes a low-complexity and efficient airspace platform deployment optimization algorithm to achieve global optimization of three-dimensional performance under different data transmission, wireless charging, and sensing accuracy requirements.

[0053] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. Attached Figure Description

[0054] The accompanying drawings, which form part of this application, are used to provide a further understanding of the application. The illustrative embodiments and descriptions of the application are used to explain the application, but do not constitute an undue limitation of the application. Obviously, the drawings described below are merely some embodiments, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0055] In the attached diagram:

[0056] Figure 1 This is a flowchart illustrating the interaction method between a UAV and a marine buoy based on marine sensing multiplexing in this specific embodiment.

[0057] Figure 2 (a) is a schematic diagram of the time-frequency performance of the MDD-OFDM waveform in this specific embodiment, and (b) is a schematic diagram of the time-frequency performance of the SBFD-AFDM waveform in this specific embodiment.

[0058] Figure 3 This is a flowchart illustrating the interaction between the UAV transmitter and the buoy receiver in the UAV-buoy interaction method based on marine sensing multiplexing in this specific embodiment.

[0059] Figure 4 This is a flowchart illustrating the interaction method between a UAV and a marine buoy based on marine sensing multiplexing in this specific embodiment.

[0060] Figure 5 This is a schematic diagram of the parallel duplex frame structure that matches the SBFD-AFDM-based inductive charging multiplexed signal waveform in this specific embodiment.

[0061] Figure 6 This is a schematic diagram of the three-layer iterative optimization algorithm in the UAV-maritime buoy interaction method based on marine sensing multiplexing in this specific embodiment. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments will be clearly and completely described below with reference to the accompanying drawings. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application.

[0063] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0064] Regarding the existing technologies mentioned in the background section, the following detailed explanation is provided: To achieve multi-functional transmission of signals from UAVs to maritime buoys using limited resources, two major technical challenges need to be addressed at the signal transmission level:

[0065] (1) In order to save the energy consumption of UAVs and simplify the system complexity, so as to efficiently realize beam scanning / tracking, wireless charging and data collection of marine buoys, the inductive-charging multiplexed signal waveform is essential. However, since the inductive-charging functions under the same waveform are coupled with each other, if anti-interference processing is not carried out, excessive interference will significantly reduce the communication, sensing and charging performance. Although the existing multi-carrier split duplex and orthogonal frequency division multiplexing (MDD-OFDM) waveforms can effectively alleviate the interference problem between multi-function signal transmissions, in the high mobility communication scenario of UAVs, when the system faces high latency and significant Doppler frequency shift, the performance of MDD-OFDM has obvious limitations. Specifically, the large Doppler frequency shift destroys the orthogonality between subcarriers, resulting in a significant decrease in the performance of the above scheme, and its performance cannot meet the system requirements;

[0066] (2) Given time and frequency resources, the performance of communication, sensing and charging is mutually constrained. Although increasing the sensing resources will improve the beam alignment accuracy, the reduction of communication and charging resources will lead to the corresponding performance degradation. Existing research has not addressed the inherent relationship and trade-off optimization algorithm between the microwave communication, sensing and charging multiplexing functions of maritime UAVs.

[0067] Based on this, please see Figure 1 This application provides a method for interaction between a UAV and a marine buoy based on marine sensor multiplexing, including the following steps:

[0068] S1. Define the interaction scenario between the drone and the marine buoy:

[0069] Define a marine buoy interaction scenario based on the multiplexing of sensing and charging capabilities of a marine UAV, including a UAV, a ground base station, and a marine buoy. Define the UAV as... Each marine buoy provides a sensing and charging service and transmits the collected buoy information back to the ground base station;

[0070] S2. The signal waveform of the UAV is designed as an inductively charged multiplexed signal waveform based on SBFD-AFDM:

[0071] The SBFD-AFDM-based inductive charging multiplexed signal waveform modulates multiple time-overlapping information symbols onto a dynamic chirped signal, utilizing time-frequency two-dimensional orthogonality to replace traditional frequency-domain orthogonality, thereby achieving multiplexing and microwave sensing directionality.

[0072] S3. Design a parallel duplex frame structure to match the SBFD-AFDM-based inductively charged multiplexed signal waveform:

[0073] In the initial stage of the system, full-band beam scanning is first performed to obtain the precise position information of the buoy. After entering the stable working stage, wireless charging, data collection and beam tracking are processed in parallel by optimizing resource allocation. During the entire flight of the UAV, the beam scanning, wireless charging, data collection and beam tracking steps are repeated for each buoy in sequence to ensure that the preset buoy communication information rate and minimum charging energy value are reached.

[0074] S4, based on the waveform designed by S2 and the parallel duplex frame structure designed by S3, enables communication, sensing and charging between the UAV and the marine buoy.

[0075] Consider a scenario of interaction between a marine buoy and a drone based on inductive charging multiplexing. This involves deploying a drone with inductive charging capabilities to... Each marine buoy provides sensing and charging services and transmits the collected buoy information back to the ground base station TBS.

[0076] Specifically, definition Indicating the index of marine buoys, which are evenly distributed throughout the area to be measured; ground base station TBS and the... The fixed positions of the marine buoys are as follows: and ,in and These represent the horizontal positions of the two, and These represent the coordinates of the two along the east-west and north-south directions, respectively. and These represent the heights of the two objects, respectively.

[0077] To facilitate modeling and analysis, the UAV flight trajectory is discretized in the time domain at equal intervals. The total flight time is... Discretize into There are coherent time slots, and the duration of each coherent time slot is . .

[0078] Specifically, when When the time is sufficiently short, the drone's flight trajectory in each coherent time slot can be approximated as a short straight line. Therefore, the drone's flight path in each time slot... The three-dimensional coordinates can be represented as ,in , and These represent the time slots of the drone. Horizontal position and flight altitude and These represent the time slots of the drone. Coordinates along the east-west and north-south directions.

[0079] It should be noted that traditional MDD-OFDM waveforms maintain orthogonality in the frequency domain by distributing multiple time-overlapping information symbols onto subcarriers at appropriate intervals and fixed frequencies. However, the high mobility of UAVs disrupts the orthogonality between subcarriers to some extent, resulting in the signal observed by the receiver exhibiting nonlinear distortion superposition characteristics. This reduces the practicality of MDD-OFDM waveforms in dynamic interaction scenarios such as UAV-buoy.

[0080] Please see Figure 2 Considering the high mobility of UAVs and the need for UAVs to simultaneously possess communication, sensing, and charging capabilities for interaction with marine buoys, this embodiment proposes a SBFD-AFDM-based inductive-charging multiplexed signal waveform. Unlike MDD-OFDM waveforms, the proposed SBFD-AFDM waveform no longer uses fixed-frequency subcarriers, but rather chirped signals whose frequency varies linearly with time. Chirped signals are orthogonal and occupy the entire bandwidth, naturally possessing advantages in resisting multipath and Doppler shift, exhibiting high efficiency in high-mobility scenarios. The essential differences between the two in the time-frequency domain are as follows: Figure 2 As shown in the figure. Since these two multi-carrier modulation techniques involve multiple carriers with different functions, for ease of illustration, only one carrier from each of the three functions—communication, sensing, and charging—is shown in the figure.

[0081] It is worth noting that the basic principle of the SBFD-AFDM waveform is as follows: a set of orthogonal chirped signals are generated through Discrete Affine Fourier Transform (DAFT). This orthogonality is manifested in the fact that different chirped signals are orthogonal to each other in the time and frequency domains, allowing multiple signals to be transmitted simultaneously without mutual interference. This is achieved by dynamically adjusting the chirped parameters. By matching the time delay and Doppler spread characteristics of the channel, the time delay Doppler representation of the channel can be effectively reconstructed, thereby achieving the optimal diversity order of the time-frequency bicolor dispersive channel.

[0082] It should be noted that in the SBFD-AFDM waveform design used in this embodiment, the system performance largely depends on the chirp parameters in the Discrete Affine Fourier Transform (DAFT) domain. and A reasonable choice. Chirp parameters. Controlling temporal scalability, while chirp parameters The frequency domain extension of the control signal, together with the frequency domain extension, determines the shape of the SBFD-AFDM waveform in the time delay-Doppler domain, as well as the orthogonality and anti-interference capability between chirped signals.

[0083] To ensure that the delay-Doppler responses of different paths do not overlap under multipath channel conditions, thereby improving signal demodulation performance and anti-interference capability, and to address chirp parameters... The following design is proposed:

[0084] When the channel's time-delay impulse response exhibits sparsity, to ensure that the discrete affine Fourier transform (DAFT) domain impulse response distributions of each path do not overlap, for the integer Doppler frequency shift case, the chirp parameter... Set to:

[0085] (1);

[0086] in, This is the maximum Doppler extended index that the system can accommodate. For the number of chirp signals, and Paths and path Integer delay;

[0087] For the fractional Doppler frequency shift case, the chirp parameter Selected as:

[0088] (2);

[0089] in, and Let represent the spread compensation amount caused by the fractional Doppler frequency shift and the maximum observable integer Doppler index, respectively, and they must satisfy . ;

[0090] If the minimum time delay interval between any two paths in the system satisfies Then, the above formulas (1) and (2) can be simplified to:

[0091] (3);

[0092] (4);

[0093] For chirp parameters Its effect on the Discrete Affine Fourier Transform (DAFT) kernel function is mainly reflected in the additional phase rotation in the frequency domain. Generally speaking, the chirp parameter... It can be chosen as any irrational number or a number much smaller than 1. The rational number is used to improve the spectral conformality of the signal under Doppler spread, but its value does not directly affect the interference management between paths.

[0094] In the above chirping parameters and chirp parameters Under this setting, it can be ensured that the impulse response distribution between paths does not overlap, thereby improving the diversity performance and anti-interference capability of SBFD-AFDM waveform signals in time-delay-Doppler sparse channels.

[0095] The definition of the Inverse Discrete Affine Fourier Transform (IDAFT) is as follows:

[0096] (5);

[0097] in, Represents the transmitted signal vector The One portion, Indicates the first One modulation symbol, Indicates the first The chirp signal is related to the first Affine Fourier Transform (AFT) kernel function related to each modulation symbol.

[0098] According to the definition of Inverse Discrete Affine Fourier Transform (IDAFT), the transmitted signal vector can be processed before the IDAFT. Perform inductive charging function mapping and control the use of each chirp signal accordingly.

[0099] Will A set of mutually orthogonal chirping signals is denoted as . To achieve multiplexing and avoid local frequencies, the data is functionally divided into three mutually orthogonal subsets, namely... and They are used for communication, sensing, and charging, respectively. For easy identification, each chirp signal is... Function types are defined by binary variables. If the sign This indicates a chirp signal. ,in These represent communication, sensing, and charging, respectively, and vice versa. Considering that the three subsets of chirped signals are orthogonal, binary variables... Must meet:

[0100] (6).

[0101] Please see Figure 3 The signal transmitted by the drone transmitter It includes sensing signals and charging signals. The sensing signals are used for microwave sensing orientation and beam alignment of the buoy, while the charging signals are used to wirelessly transmit energy to the buoy. To reduce interference from the charging signals during sensing echo reception, the sensing and charging signals are modulated onto two orthogonal sets of chirped signals respectively, based on chirped signal function mapping; that is, chirped signal set allocation. Provide sensing signals, chirping signal set Send a charging signal, .

[0102] Drones in time slots The transmitted signal is represented as:

[0103] (7);

[0104] in and They are time slots In the chirping signal The sensing signals and wireless charging signals on the surface and They are time slots In the chirping signal The sensing transmission vector and the wireless charging transmission vector on the surface;

[0105] Buoy in time slot The received charging signal is represented as:

[0106] (8);

[0107] in and They are time slots In the chirping signal The wireless charging channel and device self-charging channel on the device. For time slots In the chirping signal The buoy communication signal on the surface.

[0108] By pre-defining the set of functional chirped signals before transmission and loading each type of signal with different functions onto its assigned chirped signal, multiplexing in the Discrete Affine Fourier Transform (DAFT) domain can be achieved while avoiding frequency domain overlap and improving spectral efficiency. The chirped signal partitioning strategy ensures that different functions (sensing, charging) are orthogonal to each other in the DAFT domain and do not interfere with each other.

[0109] Before the inverse discrete affine Fourier transform (IDAFT), the SBFD technique is used to map the sensing and charging signals, mapping signals with different functions onto different chirped signals to achieve physical isolation between functions.

[0110] To convert the Discrete Affine Fourier Transform (DAFT) domain signal into a time-domain signal, the Inverse Discrete Affine Fourier Transform (IDAFT) is performed. After IDAFT processing, a time-domain SBFD-AFDM signal is obtained, where each chirped signal carries a different function and naturally resists Doppler shift and time delay spread at sea. Finally, the signal is processed by a digital-to-analog converter and an RF transmission module, and then transmitted using the UAV's MIMO antenna array, enabling simultaneous sensing and power generation.

[0111] Please see Figure 4 The buoy receiver captures the sensing and charging signals transmitted by the UAV via a radio frequency antenna and performs impedance matching to optimize energy transmission efficiency. Subsequently, the signal is envelope-detected to extract usable energy, rectified by a bridge rectifier to convert it into DC power, and then stored by a capacitor or electric pump to maintain the buoy's normal operation. After acquiring sufficient energy, the buoy uses adaptive modulation and coding techniques to modulate the sensed marine environmental data and encodes it in conjunction with system status information. Then, the buoy uses an optimized transmission strategy to transmit the feedback information to the UAV receiver with minimal energy overhead. To improve communication reliability, the buoy may employ frequency hopping, spread spectrum, or diversity techniques to adapt to the complex marine channel environment and ensure effective transmission of sensed data.

[0112] The drone receiver is in the time slot Received signal It includes the sensed echo signal and uplink data, as shown in formula (4).

[0113] (9);

[0114] in and The time slots are represented sequentially. In the chirping signal The target sensing channel, sea clutter interference channel, and data collection channel are located on the surface. and They are time slots In the chirping signal The sensing reception vector and data collection reception vector on the top, and They are time slots In the chirping signal The drone receives signals and additive white Gaussian noise (AWGN).

[0115] The sensing echo signal is used for microwave sensing direction finding and beam feedback of marine buoys, while the uplink data is used to receive transmitted information from the marine buoys. To effectively extract various signals, the received composite multiplexed signal is first processed by an RF receiving and analog-to-digital conversion module to improve signal quality and convert the analog signal into a digital signal. Next, the digital signal is converted to the Discrete Affine Fourier Transform (DAFT) domain using a Discrete Affine Fourier Transform (SBFD) module. Then, SBFD technology is used to extract the sensing echo and data collection signals, extracting the signals transmitted on each chirped signal.

[0116] Specifically, the sensed echo signal and uplink data are mapped to different chirped signal sets, and the sensed signal is assigned to a chirped signal set. Data transmission is allocated to the chirped signal set. ,in This enables signal separation in the Discrete Affine Fourier Transform (DAFT) domain. Finally, through digital precoding, the signals of different functions are demodulated and optimized to recover the sensing echo signal and uplink data, thus completing signal extraction and data decoding.

[0117] Please see Figure 5 While the SBFD-AFDM-based inductive-charge multiplexed signal waveform can eliminate digital domain interference between different links, chirp signal segmentation leads to a decrease in the frequency resource utilization of the system. Therefore, this embodiment further designs a parallel duplex frame structure that matches the SBFD-AFDM-based inductive-charge multiplexed signal waveform.

[0118] Specifically, due to the total flight time of drones Discretized into There are coherent time slots, assuming each time slot corresponds to one transmission frame. When When large enough, the drone and the buoy remain relatively stationary within each transmission frame, and the channel state information remains unchanged. Each transmission frame contains 10 subframes, and each subframe contains 8 time slots. Thanks to the full-duplex nature of SBFD, links occupying different chirped signal resource blocks can transmit corresponding signals simultaneously, such as... Figure 5 The frame structure shown.

[0119] In the initial stage of the system ( First, a full-band beam scan is performed to obtain the buoy's precise location information, providing spatial alignment for subsequent beam tracking, wireless charging, and data collection. Then, it enters the stable operating phase. Afterwards, the system optimizes resource allocation to achieve parallel processing of multiple tasks (wireless charging + data collection + beam tracking). Due to the influence of sea wave fluctuations, the UAV, with the assistance of beam tracking, achieves microwave sensing orientation and real-time beam calibration, while simultaneously wirelessly charging the buoys and collecting data from them. If the sensing-charging link is disconnected due to sea disturbances or severe weather, beam scanning will be performed again to obtain the precise location information of the buoys. Throughout the UAV's flight, the above steps are repeated for each buoy to ensure that the preset buoy communication rate and minimum charging energy values ​​are achieved. Therefore, unlike traditional Time Division Multiple Access (TDMA) systems that can only transmit different signals sequentially at different times, the proposed network can achieve parallel transmission and reception of multiple signals through a parallel frame structure.

[0120] After designing the inductive charging multiplexed signal waveform for the UAV and the buoy, and the transmission frame structure adapted to this waveform, further optimization of the inductive charging performance is needed. The optimization goal is to maximize the performance of all coherent time slots. all buoys The sum of radar mutual information, the optimization variable is the UAV mission scheduling. Drone power allocation and drone flight trajectory ,in and Two discrete binary variables represent time slots. Drones on buoys Scheduling of synesthetic charging and data feedback. and They represent time slots respectively. Power allocation factors for drone sensing and charging. Indicates time slot The flight speed of the drone.

[0121] The optimization problem can be represented as (P1):

[0122] (10);

[0123] Specifically, constraints 10a-10c are task scheduling restrictions, assuming that the UAV can only perform one task for one buoy or TBS per time slot. Constraints 10d-10e are basic communication and power supply guarantees, in which... and These represent the minimum values ​​for communication and charging between the drone and the buoy, respectively. and They represent time slots respectively. Drone receiving buoy Information rate and buoy The charging value. Constraint 10f is the data return limit, where and They represent time slots respectively. Drones on buoys Radar mutual information and data return rate; constraint 10g is the UAV transmit power limit, in which This represents the maximum transmit power of the UAV; constraint 10h-10n represents the flight constraints of the UAV, where... and They represent time slots respectively. and time slot The drone's horizontal position and They represent time slots respectively. and time slot The flight altitude of the drone and They represent time slots respectively. The horizontal and vertical flight speeds of the drone For each coherent time slot duration, and They represent time slots respectively. The horizontal and vertical flight speeds of the drone and These represent the maximum horizontal and vertical flight accelerations of the drone, respectively. and These represent the maximum horizontal and vertical flight speeds of the drone, respectively. and These represent the minimum and maximum flight altitude limits for the drone, respectively.

[0124] Solving the optimization problem (P1) directly affects the UAV's sensory perception performance of the buoy. This is due to the UAV mission scheduling... To mitigate the impact of the non-convex constraint 10d-10f, a three-layer iterative algorithm is employed to solve the mixed integer optimization problem. This algorithm divides the initial optimization problem (P1) into three parts: UAV task scheduling optimization (P2), UAV power allocation optimization (P3), and UAV flight trajectory optimization (P4).

[0125] To solve the UAV mission scheduling problem Discrete binary variables and relaxation Continuous variables within a given range. Given the fixed optimization problem (P3) and the optimization problem (P4), we present the UAV mission scheduling optimization problem (P2), namely:

[0126] (11);

[0127] Among them, constraints 11a-11c are task scheduling restrictions, constraints 11d-11e are basic communication and power supply guarantees, and constraint 11f is a data return restriction.

[0128] Given the fixed optimization problem (P2) and the optimization problem (P4), we present the UAV power allocation optimization problem (P3), namely:

[0129] (12);

[0130] Among them, constraints 12a-12b are for basic communication and power supply, constraint 12c is for data transmission restrictions, and constraints 12d-12f are for UAV power allocation and transmission power restrictions.

[0131] Given the fixed optimization problem (P2) and the optimization problem (P3), we present the UAV flight trajectory optimization problem (P4), namely:

[0132] (13);

[0133] Among them, constraints 13a-13b are basic communication and power supply guarantees, constraint 13c is data transmission restriction, and constraints 13d-13j are UAV flight constraints.

[0134] By solving optimization problems (P2), (P3), and (P4), we can obtain the results respectively. , and The optimal solution.

[0135] Please see Figure 6 To obtain the optimal solution to the original optimization problem (P1), a three-layer iterative optimization algorithm is proposed, which iteratively optimizes the three subproblems until the objective function converges. The optimization algorithm flow is as follows: Figure 6 As shown. First, the parameters are initialized and the objective function value is calculated. During the iteration process, it is determined whether the difference between two adjacent objective function values ​​is greater than the maximum tolerance. If the conditions are met, then perform three layers of iterative optimization in sequence: First, fix the power allocation in the i-th round. and flight trajectory Solve the optimization problem (P2) to obtain the task scheduling for the (i+1)th round. Second, fixed and Solve the optimization problem (P3) to obtain the power allocation in the (i+1)th round. Third, fix and Solve the optimization problem (P4) to obtain the flight trajectory in the (i+1)th round. Then ordered Repeat the above steps until the difference between two consecutive objective function values ​​is no greater than the maximum tolerance. Finally, the optimal solution to the optimization problem (P1) is output. and .

[0136] The above are merely preferred embodiments of this application and are not intended to limit this application in any way. Although this application has disclosed preferred embodiments as described above, it is not intended to limit this application. Any person skilled in the art can make some modifications or alterations to the above-mentioned technical content to create equivalent embodiments without departing from the scope of the technical solution of this application. The implementation schemes in the above embodiments can also be further combined or replaced. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of this application without departing from the content of the technical solution of this application shall still fall within the scope of this application.

Claims

1. A method for interaction between a UAV and a marine buoy based on marine sensor-multiplexing, characterized in that, Includes the following steps: S1. Define the interaction scenario between the drone and the marine buoy: Define a marine buoy interaction scenario based on the multiplexing of sensing and charging capabilities of a marine UAV, including a UAV, a ground base station, and a marine buoy. Define the UAV as... Each marine buoy provides a sensing and charging service and transmits the collected buoy information back to the ground base station; S2. The signal waveform of the drone designed based on the interactive scenario defined in S1 is an inductively charged multiplexed signal waveform based on SBFD-AFDM: The SBFD-AFDM-based inductive charging multiplexed signal waveform modulates multiple time-overlapping information symbols onto a dynamic chirped signal, utilizing time-frequency two-dimensional orthogonality to replace traditional frequency-domain orthogonality, thereby achieving multiplexing and microwave sensing directionality. S3. Design a parallel duplex frame structure to match the SBFD-AFDM-based inductively charged multiplexed signal waveform: In the initial stage of the system, full-band beam scanning is first performed to obtain the precise position information of the buoy. After entering the stable working stage, wireless charging, data collection and beam tracking are processed in parallel by optimizing resource allocation. During the entire flight of the UAV, the beam scanning, wireless charging, data collection and beam tracking steps are repeated for each buoy in sequence to ensure that the preset buoy communication information rate and minimum charging energy value are reached. S4, based on the waveform designed by S2 and the parallel duplex frame structure designed by S3, enables communication, sensing and charging between the UAV and the marine buoy.

2. The method according to claim 1, characterized in that, In the design of the S2-based SBFD-AFDM inductive charging multiplexed signal waveform, the chirp parameter... Controlling time-domain scalability, chirp parameters Controlling frequency domain scalability, chirp parameters and chirp parameters Together, they determine the extended shape of the SBFD-AFDM-based inductively charged multiplexed signal waveform in the time-delay-Doppler domain, as well as the orthogonality and anti-interference capability between chirped signals; chirp parameters The following design is proposed: When the channel's time-delay impulse response exhibits sparsity, to ensure that the DAFT impulse response distributions of each path do not overlap, for the integer Doppler frequency shift case, the chirp parameter... Set to: （1）； in, This is the maximum Doppler extended index that the system can accommodate. Number of chirp signals, and Paths and path Integer delay; For the fractional Doppler frequency shift case, the chirp parameter Selected as: （2）； in, and Let represent the spread compensation amount caused by the fractional Doppler frequency shift and the maximum observable integer Doppler index, respectively, and they must satisfy . ; If the minimum time delay interval between any two paths in the system satisfies Then, the above formulas (1) and (2) simplify to: （3）； （4）； For chirp parameters Choose any irrational number or a number less than 0. The rational number is used to improve the spectral conformality of the signal under Doppler spread, but the chirp parameter... The value does not directly affect the management of interference between paths.

3. The method according to claim 2, characterized in that, In the interaction scenario between drones and marine buoys: Signals transmitted by the drone transmitter It includes sensing signals and charging signals, wherein the sensing signals are used for microwave sensing orientation and beam alignment of the marine buoy, and the charging signals are used for wirelessly transmitting energy to the marine buoy; Define the total number of chirped signals as The set formed by them is denoted as Based on communication, sensing, and charging, they are uniformly divided into three non-overlapping subsets. ; Based on the chirp signal function mapping, the sensing signal and the charging signal are modulated onto two orthogonal chirp signal sets respectively, i.e., chirp signal set allocation. The sensing signal, the chirping signal set To the charging signal, .

4. The method according to claim 3, characterized in that, In the interaction scenario between drones and marine buoys: Signal received by the drone receiver It includes sensing echo signals and uplink data. The sensing echo signals are used for microwave sensing orientation and beam feedback of the marine buoy, while the uplink data is used to receive transmitted information from the marine buoy. The sensed echo signal and the uplink data are respectively mapped to different chirped signal sets, and the sensed echo signal is assigned to a chirped signal set. The uplink data is assigned to a chirped signal set. ,in This enables signal separation in the DAFT domain; Finally, after digital precoding, the different functional signals are demodulated and optimized to recover the sensing echo signal and the uplink data, thus completing the signal extraction and data decoding.

5. The method according to claim 2, characterized in that, Following S3, steps are included to optimize the performance of inductive charging multiplexing, including: Define that each time slot of the drone can only perform one mission for a single buoy or TBS; The optimization objective is to maximize radar mutual information, and the optimization variables are UAV mission scheduling, UAV power allocation, and UAV flight trajectory. The initial optimization problem (P1) is divided into three parts: UAV mission scheduling optimization (P2), UAV power allocation optimization (P3), and UAV flight trajectory optimization (P4).

6. The method according to claim 5, characterized in that, The initial optimization problem (P1) is expressed as: （10）； Among them, drone mission scheduling is represented as The power allocation of the drone is represented as The drone's flight trajectory is represented as , and Two discrete binary variables represent time slots. Drones on buoys Scheduling of inductive charging and data feedback. and They represent time slots respectively. Power allocation factors for drone sensing and charging. Indicates time slot The flight speed of the UAV; constraints 10a-10c are task scheduling restrictions, defining that the UAV can only perform one task for one buoy or TBS per time slot; constraints 10d-10e are basic communication and power supply guarantees. and These represent the minimum values ​​for communication and charging between the drone and the buoy, respectively. and They represent time slots respectively. Drone receiving buoy Information rate and buoy The charging value; constraint 10f is the data return limit, where and They represent time slots respectively. Drones on buoys Radar mutual information and data return rate; constraint 10g is the UAV transmit power limit, in which This represents the maximum transmit power of the UAV; constraint 10h-10n represents the flight constraints of the UAV, where... and They represent time slots respectively. and time slot The horizontal position of the drone and They represent time slots respectively. and time slot The flight altitude of the drone and They represent time slots respectively. The horizontal and vertical flight speeds of the drone For the duration of each coherent time slot, and They represent time slots respectively. The horizontal and vertical flight speeds of the drone and These represent the maximum horizontal and vertical flight accelerations of the drone, respectively. and These represent the maximum horizontal and vertical flight speeds of the drone, respectively. and These represent the minimum and maximum flight altitude limits for the drone, respectively.

7. The method according to claim 6, characterized in that, To solve the UAV mission scheduling problem Discrete binary variables and relaxation Given continuous variables within a given range, and considering fixed optimization problems (P3) and (P4), we present the UAV mission scheduling optimization problem (P2), expressed as: （11）； Among them, constraints 11a-11c are task scheduling restrictions, constraints 11d-11e are basic communication and power supply guarantees, and constraint 11f is a data return restriction.

8. The method according to claim 7, characterized in that, Given the fixed optimization problem (P2) and the optimization problem (P4), we present the UAV power allocation optimization problem (P3), which is expressed as: （12）； Among them, constraints 12a-12b represent basic communication and power supply guarantees, constraint 12c is a data return restriction, and constraints 12d-12f are UAV power allocation and transmission power restrictions.

9. The method according to claim 8, characterized in that, Given the fixed optimization problem (P2) and the optimization problem (P3), we present the UAV flight trajectory optimization problem (P4), which is expressed as: （13）； Among them, constraints 13a-13b are basic communication and power supply guarantees, constraint 13c is data transmission restriction, and constraints 13d-13j are UAV flight constraints.

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