A communication and perception control integrated design method for unmanned aerial vehicle platform

Abstract

The application discloses a communication and perception control integrated design method for a UAV platform, and comprises the following steps: in the process that the UAV transmits communication signals to users by using a transmitting beam forming, a special radar signal is simultaneously transmitted to improve the communication and perception performance; a six-degree-of-freedom model of horizontal flight of the UAV is constructed based on a direction vector of the UAV, total lift of the UAV and a six-degree-of-freedom model, and optimization is carried out to maximize the average weighted sum of communication and transmission rate; convex optimization is carried out on the communication and perception beam forming vectors; gradient optimization is carried out on the UAV trajectory; and the communication and perception beam forming vectors and the UAV trajectory are alternately solved and optimized, so that a continuous and smooth flight trajectory is obtained, vibration and mutation in the flight process of the UAV are effectively reduced, and stability and traceability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle communication, and particularly relates to a communication and sensing control integrated design method for unmanned aerial vehicle platforms. BACKGROUND

[0002] With the gradual development of mobile communication towards the era of intelligent connection of all things, the future mobile communication system not only needs to realize ultra-high speed, ultra-low latency, ultra-high reliability and other communication performances, but also needs to have millimeter-level precision sensing capabilities to support automatic driving, traffic monitoring, human activity recognition, smart home and other intelligent applications. Therefore, the integration of sensing and communication has attracted great attention from the academic and industrial communities. Compared with the information embedding method [1] and the waveform combination scheme [2], the multi-antenna system provides additional spatial information, which can significantly improve the sensing performance and effectively improve the system throughput. The beamforming technology focuses the signal on multiple specific directions at the same time, which can guarantee high-quality service for multiple users and high-precision sensing for multiple targets. However, due to the non-line-of-sight signal paths or clutter caused by obstacles and scatterers in the surrounding environment, the multiple-input multiple-output ISAC of the ground network is prone to cause a serious decline in sensing performance.

[0003] Due to the high altitude advantage of unmanned aerial vehicles, they are expected to become a promising new type of aerial ISAC platform to overcome the existing limitations. Therefore, unmanned aerial vehicle ISAC with transmit beamforming function has been widely studied. For example, document [3] considers the scenario where an unmanned aerial vehicle performs sensing tasks in a target area while providing communication services for multiple users, and studies the target of maximizing the weighted average communication rate under the constraint of sensing performance. To this end, the two-dimensional trajectory of the unmanned aerial vehicle and the transmit beamforming vector are optimized. Document [4] designs a new adaptive ISAC mechanism in the unmanned aerial vehicle assisted system to avoid excessive sensing, in which the sensing duration does not need to be consistent with the communication duration and can be flexibly configured according to application requirements. Document [5] discusses two joint optimization schemes in different states of the unmanned aerial vehicle. On the one hand, a joint design method of communication precoding and unmanned aerial vehicle flight trajectory is proposed to solve the minimum user rate maximization problem; on the other hand, a joint optimization method of unmanned aerial vehicle sensing position, communication and sensing precoding is proposed to solve the minimum target detection probability maximization problem. Document [6] studies a multi-unmanned aerial vehicle assisted ISAC scenario, that is, the unmanned aerial vehicle detects the target and simultaneously transmits the corresponding data to the user. A joint design scheme of unmanned aerial vehicle trajectory, user association and beamforming is proposed to maximize the sum of the weighted bit rates of all ground users while guaranteeing the minimum radar sensing service requirement.

[0004] Existing research works solve the trajectory optimization problem of UAVs under discrete velocity constraints by modeling the UAV as a point mass. They obtain a series of piecewise trajectories by using time discretization techniques. However, existing models only focus on the position and velocity of the UAV, while ignoring its rotational motion, internal forces, and torques. This means that the complex dynamic characteristics of the UAV as a rigid body are ignored, which can lead to inaccuracies in trajectory optimization and control strategies. In other words, it is difficult for the UAV controller to accurately follow the planned trajectory in practical applications. The mismatch between the planned trajectory and the actual trajectory can lead to an increase in communication link delays and acquisition errors, as well as a decrease in the target recognition ability of the perception system, ultimately degrading the performance of communication and perception.

[0005] [1] A. Hassanien, M. G. Amin, Y. D. Zhang, and F. Ahmad, “Dual-Function Radar-Communications: Information Embedding Using Sidelobe Control and Waveform Diversity,” IEEE Transactions on Signal Processing, vol. 64, no. 8, pp. 2168-2181, Apr. 2016.

[0006] [2] Q. Li, K. Dai, Y. Zhang, and H. Zhang, “Integrated Waveform for a Joint Radar-Communication System With High-Speed Transmission,” IEEE Wireless Communications Letters, vol. 8, no. 4, pp. 1208-1211, Aug. 2019.

[0007] [3] Z. H. Lyu, G. X. Zhu, and J. Xu, “Joint Maneuver and Beamforming Design for UAV-Enabled Integrated Sensing and Communication,” IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2424-2440, Apr. 2023.

[0008] [4] C. Deng, X. Fang, and X. Wang, “Beamforming Design and Trajectory Optimization for UAV-Empowered Adaptable Integrated Sensing and Communication,” IEEE Transactions on Wireless Communications, vol. 22, no. 11, pp. 8512–8526, Nov. 2023.

[0009] [5] R. Chai, X. Cui, R. Sun, D. Zhao, and Q. Chen, “Precoding and Trajectory Design for UAV-Assisted Integrated Communication and Sensing Systems,” IEEE Transactions on Vehicular Technology, pp. 13151–13163, Sep. 2024.

[0010] [6] R. Zhang, Y. Zhang, R. Tang, H. Zhao, Q. Xiao, and C. Wang, “A Joint UAV Trajectory, User Association, and Beamforming Design Strategy for Multi-UAV Assisted ISAC Systems,” IEEE Internet of Things Journal, vol. 11, no. 18, pp. 29360–29374, Sep. 2024. SUMMARY

[0011] In order to overcome the shortcomings of the prior art, the application provides a communication and sensing control integrated design method for a UAV platform, which realizes the alternately optimized solution of communication and sensing beamforming vectors and UAV trajectories, obtains a continuous and smooth flight trajectory, effectively reduces the vibration and mutation in the flight process of the UAV, and improves the stability and traceability.

[0012] In order to achieve the above application purposes, the application adopts the following technical solutions:

[0013] The first aspect of the application provides a communication and sensing control integrated design method for a UAV platform, comprising the following steps:

[0014] S101, in the process of transmitting communication signals to users by the unmanned aerial vehicle using transmit beamforming, a dedicated radar signal is simultaneously sent to improve communication and perception performance;

[0015] S102, based on the unmanned aerial vehicle direction vector, the total lift of the unmanned aerial vehicle and the six-degree-of-freedom model, a six-degree-of-freedom model for horizontal flight of the unmanned aerial vehicle is constructed and optimized to maximize the average weighted sum of communication and transmission rate;

[0016] S103, the communication and perception beamforming vectors are processed by convex optimization;

[0017] S104, the trajectory of the unmanned aerial vehicle is processed by gradient optimization.

[0018] Further, in the process of transmitting communication signals to users by the unmanned aerial vehicle using transmit beamforming, a dedicated radar signal is simultaneously sent to improve communication and perception performance, including the following steps:

[0019] Based on the transmit array response vector and the channel power gain, the channel vector from the unmanned aerial vehicle to the mth user is determined;

[0020] Based on the channel vector, the transmit signal of the unmanned aerial vehicle and the additive white Gaussian noise, the signal received at the mth user is determined;

[0021] Based on the channel vector and the transmit beamforming vector, the signal-to-noise ratio is determined to determine the spectral efficiency;

[0022] Based on the transmit beam pattern gain and the perception distance d(p(t),o j ), the perception performance index is determined.

[0023] Further, based on the transmit beam pattern gain and the perception distance d(p(t),o j ), the perception performance index is determined, and its expression is as follows:

[0024]

[0025] Where G d is the covariance matrix, w m is the transmit beamforming vector of the mth user, d(p(t),o j ) is the distance between the unmanned aerial vehicle and the jth target at time t, p(t) is the position of the unmanned aerial vehicle at time t, and o j is the position of the jth target.

[0026] Further, based on the unmanned aerial vehicle direction vector, the total lift of the unmanned aerial vehicle and the six-degree-of-freedom model, a six-degree-of-freedom model for horizontal flight of the unmanned aerial vehicle is constructed and optimized to maximize the average weighted sum of communication and transmission rate, including the following steps:

[0027] A six-degree-of-freedom model is constructed based on the translational dynamics of the Lagrange-Euler equation and the rotational dynamics of the Newton-Euler equation;

[0028] A six-degree-of-freedom model for horizontal flight of the UAV is constructed based on the six-degree-of-freedom model, the UAV direction vector and the total lift of the UAV.

[0029] The optimization is performed by constructing a constraint optimization library to maximize the average weighted sum of the communication and the transmission rate.

[0030] Further, a six-degree-of-freedom model for horizontal flight of the UAV is constructed based on the six-degree-of-freedom model, the UAV direction vector and the total lift of the UAV, and the expression of the six-degree-of-freedom model for horizontal flight of the UAV is as follows:

[0031]

[0032] wherein γ(t) represents the yaw angle, I xx , I yy and I zz represent the moments of inertia in the x-axis, the y-axis and the z-axis, l represents the length of the crossbar of the rigid body, K dx and K dy represent the drag coefficients in the x-axis and the y-axis, K dmx , K dmy and K dmz represent the damping moment coefficients in the x-axis, the y-axis and the z-axis, K m represents the moment coefficient, Λ(t) = -ξ1(t) + ξ2(t) - ξ3(t) + ξ4(t) and sign(b) represents the sign of b. For the sake of simplicity, the six-degree-of-freedom model for horizontal flight can be simply written as M(p(t), Φ(t)) = 0.

[0033] Further, a six-degree-of-freedom model for horizontal flight of the UAV is constructed based on the UAV direction vector, the total lift of the UAV and the six-degree-of-freedom model, and the expression of the optimization to maximize the average weighted sum of the communication and the transmission rate is as follows:

[0034]

[0035] s.t.C1:M(p(t), Φ(t)) = 0,

[0036]

[0037]

[0038] C4:p(0) = p I ,

[0039] C5:p(T) = p F ,

[0040]

[0041] wherein R m (t) denotes the achievable spectral efficiency of the mth user, constraint C1 denotes a six-degree-of-freedom model of the UAV, constraint C2 denotes a UAV receive beam pattern gain constraint, wherein is the beam pattern gain threshold of the jth target, constraint C3 denotes a UAV flight speed limit, wherein V max denotes the maximum flight speed, constraints C4 and C5 denote initial position and terminal position constraints, respectively, the initial position vector is and the terminal position vector is constraint C6 denotes a UAV transmit power limit.

[0042] Further, the convex optimization processing of the communication and sensing beamforming vectors comprises the following steps:

[0043] constructing an optimization problem of the communication and sensing beamforming vectors based on the communication beamforming vectors, the user weights, the achievable spectral efficiency of the users, the sensing covariance matrix, the UAV receive beam pattern gain constraint, and the UAV transmit power limit constraint;

[0044] discretizing the time interval T into P equal sub-intervals and p+1 time slots, and decoupling the optimization problem at different time instants;

[0045] processing the non-convex rank constraint by an approximation technique and a semi-definite relaxation method to obtain the convex-optimized communication and sensing beamforming vectors.

[0046] Further, the expression of the convex-optimized communication and sensing beamforming vectors obtained by processing the non-convex rank constraint by an approximation technique and a semi-definite relaxation method is as follows:

[0047]

[0048] wherein w m is the communication beamforming vector, G d is the covariance matrix, G d is the covariance matrix, is the conjugate transpose of g m , g m is the channel vector of the UAV to the mth user, and p is the UAV position.

[0049] Further, the gradient optimization processing of the UAV trajectory comprises the following steps:

[0050] constructing a UAV trajectory optimization problem based on the beam pattern gain constraint, the flight speed constraint, the UAV trajectory constraint, the user weights, and the achievable spectral efficiency of the users;

[0051] The UAV trajectory optimization problem is converted into a control problem based on a state space model.

[0052] The continuous-time control vector is discretized using a control parameterization method.

[0053] The constraint nonlinear programming problem is converted into an unconstrained optimization problem using an exact penalty function method to optimize the UAV trajectory.

[0054] Further, the expression of converting the UAV trajectory optimization problem into a control problem based on a state space model is as follows:

[0055]

[0056] s.t.

[0057]

[0058]

[0059]

[0060]

[0061] C'6:x1(T)=x F ,x2(T)=y F ,

[0062] wherein, Considering actual factors, the constraint C'2 is introduced to limit the maneuverability of the UAV, in addition, the constraint C5' provides the initial state vector necessary for solving the differential equation in C1', and the remaining constraints are obtained by re-expressing the constraints in the original problem in the form of x(t) and u(t).

[0063] The beneficial effects of the present application: the scene of the multi-antenna UAV sensing the target in a specific area while providing communication services to multiple users is realized, which has potential application prospects in border surveillance and environmental monitoring. In order to solve the influence of dynamic constraints on the performance of the UAV, the optimization problem is divided into a beamforming optimization sub-problem and a UAV trajectory optimization sub-problem, and is solved alternately. Different from the trajectory discretization of the existing method, the control variable is parameterized based on the state space model, and the state variable (such as position and velocity) of the UAV is described as a function of the control variable, so as to realize the continuous smooth flight trajectory.

[0064] Compared with the existing scheme, the present application can significantly reduce the decline of communication performance and the violation of sensing constraints. BRIEF DESCRIPTION OF DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced. Obviously, the accompanying drawings in the following description only represent some embodiments of the present application, and all other drawings obtained by those of ordinary skill in the art without creative effort based on these drawings are within the scope of the present application.

[0066] Figure 1 is a schematic diagram of the steps of a communication and perception control integrated design method for a UAV platform according to the present application;

[0067] Figure 2 is a planned trajectory diagram obtained by the three schemes according to the present application;

[0068] Figure 3 is a planned trajectory diagram of P0 and P1 according to the present application;

[0069] Figure 4 is an actual trajectory of P0 and P1 according to the present application;

[0070] Figure 5 is a diagram of the change of the receive beam pattern gain with time under different trajectories according to the present application;

[0071] Figure 6 is a comparison curve of average data throughput under different receive beam pattern gain thresholds according to the present application. DETAILED DESCRIPTION

[0072] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0073] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0074] Embodiment one:

[0075] A communication and perception control integrated design method for a UAV platform, comprising the following steps:

[0076] S101, in the process of transmitting communication signals to users by the unmanned aerial vehicle using transmit beamforming, a dedicated radar signal is simultaneously sent to improve communication and sensing performance;

[0077] Radar sensing is performed on potential targets while providing communication services for multiple single-antenna users. It is assumed that the unmanned aerial vehicle needs to fly from a predetermined initial position to a final position within a limited time range T∈[0,T]; the position vector of the unmanned aerial vehicle is wherein, denotes the transpose of a matrix; the flight height of the unmanned aerial vehicle is z u ; the sensing target set of the region of interest is J∈{1,...,J}, and the position of the jth target is It is assumed that there are M users, and the user set is represented as M∈{1,...,M}, and the position of the mth user is

[0078] The unmanned aerial vehicle transmits a dedicated radar signal while transmitting communication signals to users using transmit beamforming to improve communication and sensing performance, including the following steps:

[0079] Based on the transmit array response vector and the channel power gain, the channel vector from the unmanned aerial vehicle to the mth user is determined;

[0080] Based on the channel vector, the transmit signal of the unmanned aerial vehicle, and the additive white Gaussian noise, the signal received at the mth user is determined;

[0081] Based on the channel vector and the transmit beamforming vector, the signal-to-noise ratio is determined, and thus the spectral efficiency is determined;

[0082] Based on the transmit beam pattern gain and the sensing distance d(p(t),o j ), the sensing performance index is determined.

[0083] In the process of transmitting communication signals to users by the unmanned aerial vehicle using transmit beamforming, a dedicated radar signal is simultaneously sent to improve communication and sensing performance, and its expression is as follows:

[0084]

[0085] wherein c(t) is the signal transmitted by the unmanned aerial vehicle at time t, w m is the transmit beamforming vector of the mth user, c m is the communication signal, and c0 is the dedicated radar signal.

[0086] It is noted that the drone transmits dedicated radar signals and communication signals to the users, where the drone uses transmit beamforming to transmit the communication signals to the users, e.g., the drone uses transmit beamforming to transmit the communication signal to the mth user at time t. While the communication signals can be used for sensing, the performance of sensing is limited, therefore, a dedicated radar signal c0is designed to further improve the communication and sensing performance. The communication signals are independent, i.e. while the dedicated radar signal has zero mean and covariance matrix where (·) H denotes the matrix conjugate transpose, ≥ denotes positive semi-definite. In addition, the communication signals are uncorrelated with the dedicated radar signal, i.e.

[0087] The expression of the total transmit power of the drone is given as follows:

[0088]

[0089] where E is the total transmit power of the drone, ‖·‖ is the vector Frobenius norm, tr(·) is the matrix trace, w m is the transmit beamforming vector of the mth user, G d is the covariance matrix; the total transmit power of the drone is constrained, P max is the maximum communication power.

[0090] The channel link between the drone and the ground users is dominated by the line-of-sight link, and the Doppler effect caused by the drone maneuverability is fully compensated at both the users and the targets. Therefore, the air-to-ground channel is considered to follow the free-space path loss model. The channel power gain from the drone to the mth user is denoted as β m (p(t),p m ), whose expression is given as follows:

[0091]

[0092] where β0is the channel power gain at a distance of 1 meter, a is the path loss exponent, is the position vector of the drone at time t, is the distance between the drone and the mth user at time t, x(t) and y(t) are the components of the drone in the x-axis and y-axis.

[0093] The transmit array response vector of the drone in the direction of the mth user is denoted as b(p(t),p m ), whose expression is given as follows:

[0094]

[0095] where N denotes the number of antennas of the uniform linear array mounted on the UAV, d = λ / 2 denotes the antenna adjacent element spacing, λ denotes the carrier wavelength, is the angle of departure of the signal from the UAV to the user.

[0096] The channel vector from the UAV to the mth user is denoted as g m (p(t)) is expressed as follows:

[0097]

[0098] where b(p(t), p m ) is the transmit array response vector of the UAV in the direction of the mth user, β m (p(t), p m ) is the channel power gain from the UAV to the mth user.

[0099] The received signal at the mth user is denoted as s m (t) and is expressed as follows:

[0100]

[0101] where, denotes the additive white Gaussian noise at the mth user.

[0102] The signal-to-noise ratio of the mth user is denoted as φ m (p(t), {w i (t)}, G d (t)) and is expressed as follows:

[0103]

[0104] where w i is the transmit beamforming vector of the ith user, G d is the covariance matrix, p(t) is the position vector of the UAV at time t, G d is the covariance matrix, is the noise power of the mth user, is the conjugate transpose of g m , g m is the channel vector from the UAV to the mth user.

[0105] The achievable spectral efficiency (data rate per unit bandwidth) of the mth user is denoted as η

[0106]

[0107] where the signal-to-noise ratio is φ m(p(t),{w i (t)},G d (t))。

[0108] Consider the radar sensing service provided by the UAV. In order to improve the sensing performance, the user's communication signal can be used to estimate the target parameter. The power of the sensing signal directed to the target j is called the transmit beam pattern gain, which is expressed as follows:

[0109]

[0110] where G d is the covariance matrix, w m is the transmit beamforming vector of the mth user, p(t) is the position vector of the UAV at time t, b(p(t),o j is the channel vector from the UAV to the jth target, p(t) is the UAV position at time t, o j is the position of the jth target, b H (p(t),o j ) is the conjugate transpose of b(p(t),o j ).

[0111] Due to path loss, the beam pattern gain received by the UAV depends on d(p(t),o j ), which is taken as the sensing performance indicator, expressed as:

[0112]

[0113] where G d is the covariance matrix, w m is the transmit beamforming vector of the mth user, d(p(t),o j ) is the sensing distance from the UAV to the jth target, p(t) is the UAV position at time t, o j is the position of the jth target.

[0114] S102, based on the UAV direction vector, the total lift of the UAV and the six degree of freedom model, a six degree of freedom model of horizontal flight of the UAV is constructed and optimized to maximize the average weighted sum of communication and transmission rate;

[0115] The constraint optimization library includes: UAV trajectory constraints, flight speed constraints, transmit power limitations and beam pattern gain constraints. The UAV is regarded as a rigid body, and the UAV is operated by controlling the speed of its propellers. For example, vertical motion is achieved by simultaneously increasing or decreasing the speed of the four propellers. The direction vector of the UAV is represented by . The lift is proportional to the square of the propeller speed, expressed as:

[0116] Li (t) = K p ξ i 2 (t), i e {1, 2, 3, 4}.

[0117] wherein, ξ i (t) denotes the velocity of the i-th propeller, L i (t) denotes the lift generated by the i-th propeller, K p denotes the lift coefficient.

[0118] Since the UAV is flying horizontally, the total lift acting on it can be derived as:

[0119]

[0120] wherein, ζ(t) denotes the roll angle, η(t) denotes the pitch angle, m a denotes the mass of the UAV, and g denotes the gravitational acceleration.

[0121] Based on the UAV orientation vector, the total lift of the UAV, and the six degrees of freedom model, a six degrees of freedom model for the horizontal flight of the UAV is constructed and optimized to maximize the average weighted sum of communication and transmission rate, including the following steps:

[0122] constructing the six degrees of freedom model based on the translational dynamics of the Lagrange-Euler equation and the rotational dynamics based on the Newton-Euler equation;

[0123] constructing a six degrees of freedom model for the horizontal flight of the UAV based on the six degrees of freedom model, the UAV orientation vector, and the total lift of the UAV;

[0124] optimizing by constructing a constraint optimization library to maximize the average weighted sum of communication and transmission rate.

[0125] The six degrees of freedom model can be constructed by the translational dynamics of the Lagrange-Euler equation and the rotational dynamics based on the Newton-Euler equation. The expression of the six degrees of freedom model for the horizontal flight of the UAV is as follows:

[0126]

[0127] wherein, γ(t) denotes the yaw angle, I xx , I yy , and I zz denote the moments of inertia in the x-axis, y-axis, and z-axis, respectively, l denotes the length of the crossbar of the rigid body, K dx and K dy denote the drag coefficients in the x-axis and y-axis, K dmx , K dmy , and K dmz denote the damping torque coefficients in the x-axis, y-axis, and z-axis, respectively, and K mdenotes the moment coefficient, Λ(t) = -ξ1(t) + ξ2(t) - ξ3(t) + ξ4(t) and sign(b) denotes the sign of b. For simplicity, the six-degree-of-freedom model for horizontal flight can be written as M(p(t), Φ(t)) = 0.

[0128] Under the consideration of the UAV dynamics model, the perception requirement and the transmit power constraint, the average weighted sum rate of the communication can be maximized by optimizing the UAV trajectory, the communication and the perception beamforming vectors. Therefore, based on the UAV direction vector, the total UAV lift and the six-degree-of-freedom model, the six-degree-of-freedom model for horizontal flight of the UAV is constructed and optimized to maximize the expression of the average weighted sum rate of the communication as follows:

[0129]

[0130] s.t.C1:M(p(t),Φ(t))=0,

[0131]

[0132]

[0133] C4:p(0)=p I ,

[0134] C5:p(T)=p F ,

[0135]

[0136] wherein R m (m) denotes the achievable spectral efficiency of the mth user, the constraint C1 denotes the six-degree-of-freedom model of the UAV, the constraint C2 denotes the UAV receive beam pattern gain constraint, wherein is the beam pattern gain threshold of the jth target, the constraint C3 denotes the UAV flight speed limit, wherein V max denotes the maximum flight speed, the constraints C4 and C5 respectively denote the initial position and the terminal position constraint, the initial position vector is and the terminal position vector is the constraint C6 denotes the UAV transmit power limit.

[0137] S103, performing convex optimization processing on the communication and the perception beamforming vectors;

[0138] Since there is a strong coupling relationship between the UAV trajectory point and the beamforming vector, the problem can be solved by adopting an alternating optimization strategy to optimize the communication and the perception beamforming vector.

[0139] The convex optimization processing on the communication and the perception beamforming vectors comprises the following steps:

[0140] Based on the communication beamforming vector, user weight, user achievable spectral efficiency, perception covariance matrix, UAV receive beam pattern gain constraint and UAV transmit power limit constraint, an optimization problem of communication and perception beamforming vector is constructed;

[0141] The time interval T is discretized into P equal sub-intervals and P+1 time slots, and the optimization problem is decoupled at different time instants;

[0142] The non-convex rank constraint is handled by approximation technique and semi-definite relaxation method, and the convex optimized communication and perception beamforming vector is obtained.

[0143] Based on the communication beamforming vector, user weight, user achievable spectral efficiency, perception covariance matrix, UAV receive beam pattern gain constraint and UAV transmit power limit constraint, an optimization problem of communication and perception beamforming vector is constructed, and its expression is as follows:

[0144]

[0145] s.t.C2,C6.

[0146] Wherein, ρ m represents the weight of the mth user, R m represents the achievable spectral efficiency of the mth user, w m (t) is the communication beamforming vector, G d (t) is the perception covariance matrix.

[0147] In order to convert the above sub-problems into an easy-to-handle form, the time interval is discretized into P equal sub-intervals and P+1 time slots, and the expression of P+1 time slots is as follows:

[0148] 0=τ0<τ1<τ2<...<τ P-1 <τ P =T.

[0149] According to the UAV receive beam pattern gain constraint and the UAV transmit power limit constraint, the optimization variables {w m [τ n ]} and G d [τ n ] at different time instants are independent. Therefore, the problem is decoupled at different time instants, and the problem is equivalently decomposed into P sub-problems. The optimization problem at time τ n is:

[0150]

[0151]

[0152]

[0153] where, in order to solve the above problem, define Thus, the constraint rank(W m [τ n ])≤1 and W m [τ n ]≥0 is introduced, and then the objective function is approximated to a concave function by using sequential convex approximation technique, which is expressed as follows:

[0154]

[0155] where and denote the local point of {W m [τ n ]} and G d [τ n ] at the kth iteration.

[0156] Therefore, by using the approximation technique and the semi-definite relaxation method to deal with the non-convex rank constraint rank(W m [τ n ])≤1, the optimization sub-problem is converted to:

[0157]

[0158] Obviously, the above problem is convex, which can be effectively solved by using the CVX solving package, and the optimal solution satisfying the rank constraint rank(W m [τ n ])≤1 always exists, so the solution of the original sub-problem can be obtained by solving the above problem. Let and denote the obtained optimal solution, and thus the optimal and Therefore, by using the approximation technique and the semi-definite relaxation method to deal with the non-convex rank constraint, the expression of the communication and sensing beamforming vector after convex optimization is as follows:

[0159]

[0160] where w m is the communication beamforming vector, G d is the covariance matrix, G d is the covariance matrix, is the conjugate transpose of g m , and g mis the channel vector from the UAV to the mth user, and p is the UAV position.

[0161] S104, gradient optimization processing is performed on the UAV trajectory.

[0162] Based on the beam pattern gain constraint, the flight speed constraint, the UAV trajectory constraint, the user weight, and the achievable spectral efficiency of the user, a UAV trajectory optimization problem is constructed, and its expression is as follows:

[0163]

[0164] wherein ρ m represents the weight of the mth user, R m represents the achievable spectral efficiency of the mth user, p(t) is the UAV trajectory, constraint C1 represents the six-degree-of-freedom model of the UAV, and constraint C2 represents the UAV receiving beam pattern gain constraint, wherein is the beam pattern gain threshold of the jth target, and constraint C3 represents the UAV flight speed limit, wherein V max represents the maximum flight speed, and constraints C4 and C5 represent the initial position and terminal position constraints, respectively, the initial position vector is and the terminal position vector is Constraint C6 represents the UAV transmit power limit.

[0165] In order to obtain a high-quality feasible solution, the above problem can be re-expressed as an optimal control problem based on a state space model. Then, the continuous-time control vector is discretized by using a control parameterization method. Further, the continuous state inequality constraint is handled by using an exact penalty function method. Based on this, an efficient gradient-based UAV trajectory optimization algorithm is designed.

[0166] The gradient optimization processing of the UAV trajectory includes the following steps:

[0167] Based on the beam pattern gain constraint, the flight speed constraint, the UAV trajectory constraint, the user weight, and the achievable spectral efficiency of the user, a UAV trajectory optimization problem is constructed.

[0168] The UAV trajectory optimization problem is converted into a control problem based on a state space model.

[0169] The continuous-time control vector is discretized by using a control parameterization method.

[0170] The constraint nonlinear programming problem is converted into an unconstrained optimization problem by using an exact penalty function method to optimize the UAV trajectory.

[0171] The UAV trajectory optimization problem is converted into a control problem based on a state space model. Combining the physical meaning of the variables in the six-degree-of-freedom model, the state vector is defined as:

[0172]

[0173] The control variable is defined as:

[0174]

[0175] Therefore, the control vector is represented as Based on the control vector and the state vector, the six-degree-of-freedom model can be simplified as Therefore, the UAV trajectory optimization problem is converted into an optimal control problem, which can be represented as:

[0176]

[0177]

[0178]

[0179]

[0180]

[0181]

[0182] C'6: x1(T) = x F , x2(T) = y F ,

[0183] where Considering practical factors, the constraint C'2 is introduced to limit the maneuverability of the UAV. In addition, the constraint C5' provides the initial state vector necessary for solving the differential equation in C1'. The remaining constraints are obtained by re-expressing the constraints in the original problem in terms of x(t) and u(t).

[0184] By discretizing the continuous-time control vector using the control parameterization method, the control variable u i (t), i = 1, 2, 3, is represented as a parameterized function where is the value of the control variable u i (t) in the time interval [τ n-1 , τ n ), and is defined as follows:

[0185]

[0186] Let The constraint C1' can be written as By replacing u(t) with θ n , the constraints C'2 and C3' are rewritten as

[0187]

[0188] where is the control variable u i (t) at the time interval [τ n-1 ,τ n ),

[0189] The constraint nonlinear programming problem is converted into an unconstrained optimization problem by using the exact penalty function method to optimize the trajectory of the UAV. In order to handle the numerous state inequality constraints C3' and C'4, the exact penalty function method is used to incorporate them into the objective function, thereby converting the constraint nonlinear programming problem into an unconstrained optimization problem. The objective function is redefined using the exact penalty function method as follows:

[0190]

[0191] where is a new variable introduced, is the original objective function represented as

[0192]

[0193]

[0194] where is the conjugate transpose of g m , g m is the channel vector of the UAV to the mth user, w m is the transmit beamforming vector of the mth user, φ m is the signal-to-noise ratio received by the mth user, is the noise power of the mth user.

[0195] is the continuous constraint violation term, is the terminal constraint violation term, represented as

[0196]

[0197]

[0198] where μ > 0 represents the penalty parameter, κ, θ, ∈, O are given constants satisfying κ > 0, θ > 2, ∈ > 0, O ∈ (0, 1).

[0199] It should be noted that, given systems C1' and C5', the original problem is transformed into a static nonlinear programming problem, with only box constraints C'2' and C'5'. Once the target function is obtained about and The gradient can be used to solve the problem of transformation.

[0200] For example, this invention considers a two-dimensional space consisting of one drone, eight users, and an area of ​​1600m². 2 The synesthetic scene is composed of the sensing areas. The user's position is p1 = [370m, 400m], p2 = [380m, 345m], p3 = [420m, 300m], p4 = [470m, 275m], p5 = [530m, 275m], p6 = [580m, 300m], p7 = [620m, 345m] and p8 = [630m, 400m]. Consider a matrix sensing area with a length of 80m and a width of 20m, and a total of J = 18 sensing points. In order to prove the superiority of the proposed scheme, the problem in reference [3] that does not consider the dynamic model is compared as the benchmark scheme, and this problem is regarded as P0. In addition, under the framework of this invention, the problem considering the three-degree-of-freedom model is regarded as P1, and the problem studied in this invention is regarded as P2.

[0201] Solve for P0, P1, and P2 to obtain the planned trajectory as follows: Figure 2 As shown, the planned trajectories of these three schemes are very similar. During flight, the drone continuously strives to approach the user to gain more communication throughput. However, the drone cannot reach the user's location because it needs to maintain a suitable sensing distance from the sensing area to meet the requirements of sensing beam pattern gain.

[0202] The actual trajectories of P0 and P1 are obtained by designing a proportional-integral-derivative controller with a six-degree-of-freedom model to track the planned trajectory, such as... Figure 3 and Figure 4 As shown, the actual trajectory of P0 cannot reach the destination, while the actual trajectory of P1 can. This is because the planned trajectory of P0 did not consider the dynamic characteristics of the UAV, resulting in the control input required for the planned trajectory exceeding the execution capability of the UAV system. Therefore, the planned trajectory of P0 cannot be tracked. Although the actual trajectory of P1 can complete the main task as required, it also cannot accurately follow the planned trajectory because it does not consider the six-degree-of-freedom model. This illustrates the importance of considering the six-degree-of-freedom model in the trajectory planning process, and also emphasizes that the planned trajectory of P2 is the actual flight trajectory of the UAV.

[0203] The change in beam pattern gain received by the UAV at the sensing point [500m, 600m] over time, such as... Figure 5As shown, in all trajectories, the receiver beam pattern gain of the UAV first decreases and then increases. This is because as the UAV gets closer to the user, the sensing distance between the UAV and the sensing location gradually increases, leading to a gradual decrease in sensing performance. Subsequently, as the UAV flies towards the destination location, the sensing distance gradually decreases, thereby improving sensing performance. Furthermore, it can be observed that although the beam pattern gain of the planned trajectories P0, P1, and P2 is always greater than the predetermined threshold Θ... th However, the beam pattern gain of the actual trajectories of P0 and P1 is less than Θ. th The occurrence of these moments indicates that the actual trajectory violated the perception performance constraints. This is because the actual trajectory of the UAV deviated from the planned trajectory, causing the actual trajectory points to fail to meet the perception performance constraints. Compared to scheme P1, the actual trajectory of P0 deviated more significantly from the planned trajectory, thus resulting in more instances of perception constraint violations.

[0204] Average rate and received beam pattern gain threshold Θ under different trajectories th Relationship such as Figure 6 As shown. With Θ th As the velocity increases, the average velocity of each scheme decreases. This is because the UAV requires more transmission power to meet higher perception requirements, thus leaving less transmission power for communication. Furthermore, it can be observed that the communication performance of the planned trajectories of P0 and P1 is worse than that of the planned trajectory of P2. However, the actual communication performance of the trajectories of P0 and P1 is worse than their respective planned trajectories, and also lower than that of the planned trajectory of P2. The performance degradation of P0 and P1 is mainly due to the lack of six-degree-of-freedom dynamics of the UAV. Therefore, the superiority of the proposed scheme P2 is obvious.

[0205] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0206] The terms "first," "second," and "third," etc., used in this application's specification and the foregoing drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0207] The above-described embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A design method for integrated communication, sensing, and control for unmanned aerial vehicle (UAV) platforms, characterized in that, Includes the following steps: S101. During the process of transmitting communication signals to users using beamforming, the UAV simultaneously transmits dedicated radar signals to improve communication and sensing performance. Specifically, the following steps are included: The channel vector from the UAV to the m-th user is determined based on the transmit array response vector and the channel power gain. Based on the channel vector, the UAV's transmitted signal, and additive white Gaussian noise, determine the signal received at the m-th user. The signal-to-noise ratio is determined based on the channel vector and the transmitted beamforming vector, thereby determining the spectral efficiency; Based on the transmitted beam pattern gain and sensing distance d(p(t),o j The perceived performance index is determined as follows: Among them, G d Let w be the covariance matrix. m For the transmitted beamforming vector of the m-th user, d(p(t),o j Let p(t) be the distance between the UAV and the j-th target at time t, and p(t) be the position of the UAV at time t. j The position of the j-th target; S102. Based on the UAV's direction vector, total lift, and six-degree-of-freedom model, construct and optimize the UAV's horizontal flight six-degree-of-freedom model to maximize the average weighted communication and transmission rate. S103. Perform convex optimization processing on the communication and sensing beamforming vectors; S104. Perform gradient optimization processing on the drone trajectory.

2. The integrated design method for communication, sensing, and control of unmanned aerial vehicle (UAV) platforms according to claim 1, characterized in that, The process of constructing and optimizing a six-degree-of-freedom model for UAV horizontal flight, based on the UAV's direction vector, total lift, and six-degree-of-freedom model, to maximize the average weighted average communication and transmission rate includes the following steps: A six-degree-of-freedom model is constructed based on translational mechanics of the Lagrange-Euler equations and rotational dynamics based on the Newton-Euler equations. Based on the six-degree-of-freedom model, the UAV's direction vector, and the UAV's total lift, a six-degree-of-freedom model for the UAV's horizontal flight is constructed. Optimization is performed by building a constraint optimization library to maximize the average weighted sum of communication and transmission rate.

3. The integrated design method for communication, sensing, and control of unmanned aerial vehicle (UAV) platforms according to claim 2, characterized in that, Based on the six-degree-of-freedom model, the UAV's direction vector, and the UAV's total lift, a six-degree-of-freedom model for the UAV's horizontal flight is constructed. The expression for the six-degree-of-freedom model for the UAV's horizontal flight is shown below: Where γ(t) represents the yaw angle, I xx I yy and I zz The moment of inertia is represented by the x-axis, y-axis, and z-axis; l represents the length of the rigid body frame; K represents the moment of inertia. dx and K dy K represents the drag coefficient along the x and y axes. dmx K dmy and K dmz K represents the damping moment coefficient along the x, y, and z axes. m The torque coefficient is represented by Λ(t)=-ξ1(t)+ξ2(t)-ξ3(t)+ξ4(t) and sign(b) represents the sign of b. The six-degree-of-freedom model of horizontal flight is simplified as M(p(t),Φ(t))=0.

4. The integrated design method for communication, sensing, and control of unmanned aerial vehicle (UAV) platforms according to claim 2, characterized in that, The following expression describes the construction and optimization of a six-degree-of-freedom model for UAV horizontal flight, based on the UAV's direction vector, total lift, and six degrees of freedom model, to maximize the average weighted sum of communication and transmission rate: Among them, R m (t) represents the achievable spectral efficiency for the m-th user, constraint C1 represents the six-degree-of-freedom model of the UAV, and constraint C2 represents the UAV's receiver beam pattern gain constraint. Let V be the beam pattern gain threshold for the j-th target, and let constraint C3 represent the UAV flight speed limit, where V max Represents the maximum flight speed, constraints C4 and C5 represent the initial position and terminal position constraints, respectively, and the initial position vector is... and the terminal position vector are Constraint C6 indicates the drone's transmit power limit.

5. The integrated design method for communication, sensing, and control of unmanned aerial vehicle (UAV) platforms according to claim 1, characterized in that, The convex optimization process for the communication and sensing beamforming vectors includes the following steps: Based on the communication beamforming vector, user weights, user-achievable spectral efficiency, sensing covariance matrix, UAV receiver beam pattern gain constraints, and UAV transmit power limitations, an optimization problem for the communication and sensing beamforming vectors is constructed. Discretize the time interval T into P equal sub-intervals and p+1 time slots, and decouple the optimization problem at different times; By processing the non-convex rank constraints using approximation techniques and semi-positive definite relaxation methods, we obtain convex optimized communication and sensing beamforming vectors.

6. The integrated design method for communication, sensing, and control of unmanned aerial vehicle (UAV) platforms according to claim 5, characterized in that, The non-convex rank constraints are processed using approximation techniques and semi-positive definite relaxation methods, resulting in the following expressions for the convex optimized communication and sensing beamforming vectors: Among them, w m G is the communication beamforming vector. d Let G be the covariance matrix. d Let covariance matrix be the variance matrix. For g m The conjugate transpose of g m Let p be the channel vector from the UAV to the m-th user, and p be the UAV's position.

7. The integrated design method for communication, sensing, and control of unmanned aerial vehicle (UAV) platforms according to claim 1, characterized in that, The gradient optimization process for the UAV trajectory includes the following steps: Based on beam pattern gain constraints, flight speed constraints, UAV trajectory constraints, user weights, and user-achievable spectral efficiency, a UAV trajectory optimization problem is constructed. The UAV trajectory optimization problem is transformed into a control problem based on a state-space model. The continuous-time control vector is discretized using a control parameterization method; The exact penalty function method is used to transform the constrained nonlinear programming problem into an unconstrained optimization problem in order to optimize the trajectory of the UAV.

8. The integrated design method for communication, sensing, and control of unmanned aerial vehicle (UAV) platforms according to claim 7, characterized in that, The expression for transforming the UAV trajectory optimization problem into a control problem based on a state-space model is shown below: in, Considering practical factors, constraint C'2 is introduced to limit the maneuverability of the UAV. In addition, constraint C5' provides the initial state vector necessary to solve the differential equation in C1'. The remaining constraints are restated in the form of x(t) and u(t) from the constraints in the original problem.

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