Design method of joint trajectory and beamforming in synaesthesia-integrated UAV networks

Abstract

The present invention discloses a design method for joint trajectory and beamforming in a synaesthesia-integrated unmanned aerial vehicle (UAV) network, relating to the field of UAV communication and perception integration technology. The method includes: constructing a network scenario; constructing a model of the communication process between the UAV and ground users, and constructing a model of the perception process between the UAV and ground detection targets; establishing constraints for the UAV communication process model and the UAV perception process model; constructing an optimization problem for the radar beam pattern in the UAV perception function; constructing an optimization problem for the UAV trajectory and beamforming in the communication and perception integration scenario based on the constraints and the radar beam pattern optimization problem; simplifying the UAV trajectory and beamforming optimization problem; and solving the UAV trajectory and beamforming optimization problem based on the Riemann conjugate gradient algorithm to obtain the UAV flight trajectory and beamforming matrix. This application provides a highly reliable algorithm for UAVs to plan paths and beams.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated communication and perception of unmanned aerial vehicles (UAVs), and in particular relates to a design method for joint trajectory and beamforming in a synaesthesia-integrated UAV network. Background Art

[0002] With the continuous development of wireless communication technology, drones are being used as aerial communication platforms to improve the performance of ground communication systems due to their flight flexibility and the ability of their communication systems to provide line-of-sight channels in vast terrains, making them a method that can provide real-time high-data-rate communication services and have attracted much attention.

[0003] Among them, beamforming and trajectory design are the research focuses of UAV communication systems. Due to the frequent changes in the position of UAVs, the direction of the communication beam changes. In order to ensure communication performance, the UAV trajectory and beamforming are generally designed jointly. UAV application scenarios are often more complex. Channel changes and noise interference caused by variable environmental factors seriously affect the communication performance of UAVs. As the operating frequency band of wireless communication continues to increase, the communication band and the radar band overlap, resulting in an increasing shortage of spectrum resources. In addition, some new 5G applications also require the integration of communication and perception (radar). Based on this, the concept of integrated communication and perception has been proposed.

[0004] In existing technologies, first, only the communication problems of drone networks are considered. In fact, the perception function itself and the assistance to communication are very important for drones. UAV networks that only consider communication performance are difficult to adapt to the rapidly developing communication technology; second, the existing technology has limited communication and perception combination, and is achieved under the conditions of consuming additional hardware and spectrum resources, which is difficult to achieve for drones with limited power and size; finally, existing optimization methods are difficult to adapt to complex synaesthesia-integrated drone scenarios and cannot solve the proposed joint trajectory and synaesthesia-integrated beamforming optimization problem.

[0005] Therefore, it is urgent to overcome the defects in existing technologies and realize the integration of communication perception and UAV communication network. Summary of the Invention

[0006] To address the above-mentioned problems in the prior art, the present invention provides a design method for joint trajectory and beamforming in a synaesthesia-integrated UAV network. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0007] In a first aspect, the present application provides a design method for joint trajectory and beamforming in a synaesthesia-integrated UAV network, comprising:

[0008] Construct a network scenario, including drones, ground users, and ground detection targets. The drone completes a flight from a starting point to a destination, communicates with ground users during the flight, and senses ground detection targets.

[0009] Build a model for the communication process between the UAV and the ground user, and build a model for the perception process between the UAV and the ground detection target;

[0010] Construct the constraint conditions of the model of the communication process between the UAV and the ground user and the model of the perception process between the UAV and the ground detection target;

[0011] Optimizing radar beam patterns for drone perception.

[0012] Based on the constraints and the optimization problem of radar beam pattern, the UAV trajectory and beamforming optimization problem in the communication and perception integrated scenario is constructed;

[0013] Simplify the UAV trajectory and beamforming optimization problem;

[0014] The UAV trajectory and beamforming optimization problems are solved based on the Riemann conjugate gradient algorithm to obtain the UAV flight trajectory and beamforming matrices.

[0015] Beneficial effects of the present invention:

[0016] The present invention provides a design method for joint trajectory and beamforming in a synaesthesia-integrated UAV network. Based on the MIMO radar beam diagram optimization problem, a communication-perception-integrated beamforming problem is proposed. According to the UAV trajectory constraints and communication, power and other constraints, a joint trajectory and synaesthesia-integrated beamforming optimization problem is proposed. Since the optimization problem is a multivariate non-convex optimization problem, a penalty function method and matrix transformation are used to simplify the optimization problem, and then an alternating optimization method based on the Riemann conjugate gradient algorithm is used to solve it. It can be understood that the original problem is first converted into an unconstrained optimization problem on a Riemann manifold using a search method, and then the Riemann conjugate gradient algorithm is used to solve it. Compared with the traditional method, the semi-positive definite optimization algorithm of this method has lower complexity, faster calculation speed, and approximate reliability. In this way, a low-latency, high-reliability algorithm is provided for the UAV to plan the path and beam, so that the UAV can provide reliable communication and perception services under power constraints.

[0017] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a design method for joint trajectory and beamforming in a synaesthesia-integrated UAV network provided by an embodiment of the present invention;

[0019] Figure 2This is a schematic diagram of a multi-user communication and perception integrated drone network scenario provided by an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of the motion trajectory of the UAV and the position of the ground user during the entire flight process provided by an embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of the relationship between beam gain and receive SINR threshold for the communication-awareness integration solution and the radar-only solution provided in an embodiment of the present invention;

[0022] Figure 5 This is a schematic diagram of the convergence speed of the Riemann conjugate gradient algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The present invention will be further described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.

[0024] In existing technologies, efficient collaboration of perception and communication functions is difficult to achieve due to hardware and power limitations of drones, as well as the low integration of traditional communication and perception systems. In current communication and perception integration, communication and perception functions must communicate information through a control center, using separate hardware and waveforms. This requires more complex hardware design, consumes more spectrum resources, and generates additional power consumption. Secondly, in high-speed drone scenarios, the joint optimization of trajectory and integrated synaesthesia beamforming is a complex non-convex problem, making it difficult to solve with traditional optimization methods. In summary, in existing drone communication networks, the integration of drone communication and perception is still in its infancy, requiring significant power and hardware implementation. Furthermore, drones primarily focus on pure communication scenarios. Key challenges in the development of drone synaesthesia integration include achieving shared spectrum and hardware integration, ensuring user quality while enhancing perception performance within communication networks, jointly designing drone trajectories and beamforming, and solving these non-convex optimization problems.

[0025] In view of this, this application proposes a design method for joint trajectory and beamforming in a synaesthesia-integrated UAV network. This method plans the flight trajectory of the UAV so that the UAV can perceive the detection target while communicating with the ground user during the flight, thereby achieving synaesthesia integration. First, the UAV network scenario is modeled, which includes the start and end points of the flight mission and multiple ground users and ground detection targets on the ground with known positions. The communication channel is considered to be a line-of-sight channel, and the number of antennas carried by the UAV is M = M x *M yA uniform planar array is constructed, and the height and speed of the drone are considered as constants, and the entire flight process is divided into N time slots; secondly, based on the traditional radar beam pattern design problem, the communication and perception integrated beamforming problem is obtained, and then combined with the drone trajectory constraints, the optimization problem of joint trajectory and beamforming in the synaesthesia integrated network is proposed. This optimization problem is a multivariate non-convex optimization problem, which is solved by an alternating optimization algorithm based on the Riemann conjugate gradient algorithm. The optimal movement route is selected for the drone and it senses and detects targets while communicating with ground users. This application realizes the synaesthesia integration of the drone network, maximizes the perception performance while meeting the communication performance under the condition of drone power limitation, and plans the drone flight path. The proposed optimization algorithm has the advantages of high reliability, high real-time performance, low complexity, etc., which meets the requirements of the drone network for latency and accuracy.

[0026] See Figure 1 , Figure 1 This is a flow chart of a design method for joint trajectory and beamforming in a synaesthesia integrated UAV network provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of a multi-user communication and perception integrated drone network scenario provided by an embodiment of the present invention. This application provides a design method for joint trajectory and beamforming in a synaesthesia integrated drone network, including:

[0027] S101. Construct a network scenario including a UAV, a ground user, and a ground detection target. The UAV completes a flight from a starting point to a destination, communicates with the ground user during the flight, and senses the ground detection target.

[0028] S102: Build a model of the communication process between the UAV and the ground user, and build a model of the perception process between the UAV and the ground detection target;

[0029] S103, constructing constraint conditions for a model of the communication process between the UAV and the ground user and a model of the perception process between the UAV and the ground detection target;

[0030] S104. Optimizing the radar beam pattern in constructing drone perception functions;

[0031] S105. Based on the constraints and the optimization problem of radar beam pattern, the UAV trajectory and beamforming optimization problem in the communication and perception integration scenario is constructed;

[0032] S106, simplifying the UAV trajectory and beamforming optimization problem;

[0033] S107. Solve the UAV trajectory and beamforming optimization problem based on the Riemann conjugate gradient algorithm to obtain the UAV flight trajectory and beamforming matrix.

[0034] For more details, please see Figure 1 As shown, this embodiment provides a design method for joint trajectory and beamforming in a synaesthesia integrated UAV network. Based on the MIMO radar beam pattern optimization problem, a communication perception integrated beamforming problem is proposed. According to the UAV trajectory constraints and communication, power and other constraints, a joint trajectory and synaesthesia integrated beamforming optimization problem is proposed; given that the optimization problem is a multivariate non-convex optimization problem, the penalty function method and matrix change are used to simplify the optimization problem, and then the alternating optimization method based on the Riemann conjugate gradient algorithm is used to solve it; it can be understood that the search method is first used to convert the original problem into an unconstrained optimization problem on the Riemann manifold, and then the Riemann conjugate gradient algorithm is used to solve it. Compared with the traditional method, the semi-positive definite optimization algorithm of this method has lower complexity, faster calculation speed, and approximate reliability; thus, a low-latency, high-reliability algorithm is provided for the UAV to plan the path and beam, so that the UAV can provide reliable communication and perception services while being power-constrained.

[0035] This embodiment also addresses the design of joint trajectories and beamforming in a synaesthesia-integrated UAV network. Considering this non-convex optimization problem, an alternating optimization algorithm based on the Riemann conjugate gradient algorithm is employed to solve the optimization problem. This algorithm can simultaneously plan trajectories for drones and design beams, achieving integrated communication and perception within the UAV network. The model considers the characteristics of drone motion, MIMO radar beam patterns, integrated communication and perception beam characteristics, and interference in multi-user scenarios. Simulation results demonstrate that the method proposed in this embodiment enables drones to provide integrated communication services while maximizing perception performance while ensuring communication performance.

[0036] In summary, this embodiment achieves a highly integrated communication and perception system, sharing frequency bands and hardware infrastructure. This addresses issues such as spectrum shortages and hardware complexity. This integration enables greater efficiency in the combined work of communication and perception, resulting in a mutually reinforcing effect between perception and communication. Using this integrated communication and perception technology in drone networks can reduce spectrum waste and hardware complexity while assisting communication through environmental perception. This technology fills a gap in the drone industry both domestically and internationally and promotes the development of integrated communication and perception research.

[0037] In an optional embodiment of the present application, see Figure 3 As shown, Figure 3 This is a schematic diagram of the motion trajectory of the drone and the location of the ground user during the entire flight process provided by the embodiment of the present invention. The network scenario is constructed. The drone starts from a designated location and passes through the area of ​​the ground user. During the entire flight, the drone communicates with the ground user while also sensing the ground detection target. Among them, the drone is equipped with M=M antennas. x *M yuniform planar array, M x is the number of antennas in the row direction of the antenna array, M y is the number of antennas in the antenna array column direction. All antennas are shared for downlink communication and radar detection and perception. Consider K single-antenna ground users with known positions, which are used as downlink communication targets and perception and detection targets at the same time. The UAV flight process is divided into N time periods. The UAV is set to fly at a constant altitude and a constant speed.

[0038] Then the position of the UAV in the nth time slot is expressed as:

[0039] r(n)=[x(n),y(n),H];

[0040] Where n is the time slot number, x(n) and y(n) are the horizontal coordinates and vertical coordinates of the UAV respectively, and H is the flight altitude of the UAV.

[0041] Then the ground user position is expressed as:

[0042] r i d =[x i ,y i ,0];

[0043] Among them, i is the ground user serial number, and d is the UAV flight speed, that is, the flight distance in each time slot.

[0044] Knowledge that needs explanation, Figure 3 The illustrated embodiment illustrates different ground users and the drone trajectories obtained using the method of the present application, wherein the "five-pointed star" represents the position of the ground user and the "dotted line" represents the movement trajectory of the drone; it can be seen from the figure that although the positions of the ground users vary greatly, the drone can always fly from the designated starting point to near the end point, reflecting the stability and effectiveness of the method provided by this embodiment.

[0045] In an optional embodiment of the present application, the model of the communication process between the drone and the ground user and the model of the perception process between the drone and the ground detection target include:

[0046] Communication channel h between UAV and ground users i (n), its expression is:

[0047]

[0048]

[0049] Where ρ is the channel gain per unit distance, λ c is the wavelength, a i(n) is the steering vector of the transmitting antenna, x(n) is the horizontal coordinate of the UAV, y(n) is the vertical coordinate of the UAV, n is the time slot number, x i is the horizontal coordinate of the ground user, y i is the vertical coordinate of the ground user, i is the ground user serial number, and H is the flight altitude of the UAV;

[0050] The signal-to-noise ratio received by the ground user is expressed as:

[0051]

[0052] Among them, a i (n) H for a i The conjugate transposed matrix of (n), r i d is the position of the ground user, d is the flight distance of the UAV in each time slot, r(n) is the position of the UAV in the nth time slot, w i (n) is the vector of the UAV’s transmit beamforming to the i-th ground user in the n-th time slot, σ is the noise power received by the ground user, and j is the summation variable, which has no practical meaning and is only used to distinguish i in the summation time slot;

[0053] The covariance matrix C of the precoded communication symbol is expressed as:

[0054]

[0055] Among them, w i is the transmit beamforming vector of ground user i, w i H w i The conjugate transposed matrix of , K is the total number of ground users; the ground users and the ground detection targets are both K single antennas with known positions.

[0056] In an optional embodiment of the present application, the steering vector a of the transmitting antenna i The expression for (n) is:

[0057]

[0058] in, is the Kronecker product, a T is the transposed matrix of the transmitting antenna’s steering vector matrix a, b is the transmitting antenna spacing, θ i (n) is the elevation angle of the line connecting the UAV and the i-th ground user relative to the horizontal plane, is the rotation angle of the line connecting the UAV and the i-th ground user relative to the y-axis; c is the wavelength, M xis the number of antenna array rows, M y is the number of antenna array column directions;

[0059] Among them, θ i (n) and is the function of the UAV position and the ground user position, that is:

[0060]

[0061]

[0062] Among them, x(n) is the horizontal coordinate of the drone, y(n) is the vertical coordinate of the drone, n is the time slot number, x i is the horizontal coordinate of the ground user, y i is the vertical coordinate of the ground user, i is the ground user serial number, and H is the flight altitude of the UAV.

[0063] It should be noted that the communication signal h between the UAV and the ground user i (n) and the UAV’s transmit beamforming vector w for the i-th ground user in the n-th time slot i (n) satisfies the following formula, which is expressed as follows:

[0064]

[0065] Among them, g i (n) is the signal received by the i-th ground user in the n-th time slot, s i (n) is the information symbol transmitted to ground users, z i (n) is the noise received by ground users.

[0066] In an optional embodiment of the present application, the constraints of the model of the communication process between the drone and the ground user and the model of the perception process between the drone and the ground detection target include:

[0067] The signal-to-noise ratio γ received by ground users i (n) To be limited, namely:

[0068] γ i (n)≥Γ i ;

[0069] Among them, Γ i is the minimum value of the signal-to-noise ratio received by the i-th ground user;

[0070] The total power P0 of the UAV transmitting antenna is limited, considering that each transmitting antenna consumes the same power, that is:

[0071]

[0072] Where I is a column vector whose elements are all 1, and M is the number of transmitting antennas;

[0073] Limit the trajectory of the drone, namely:

[0074] ||r(n)-r(n-1)||=d;

[0075] Among them, r(n) and r(n-1) are the positions of the drones in adjacent time slots, and d is the flight distance of the drone in each time slot.

[0076] It should be noted that the introduction of constraints is to ensure the reliability of UAV network communication.

[0077] In an optional embodiment of the present application, for a traditional MIMO radar, the optimization problem of its beam pattern is equivalent to the optimization problem of designing the covariance matrix of the radar transmit signal. The expression of the optimization problem of the radar beam pattern in the drone perception function is:

[0078]

[0079]

[0080] R f 0, R=R H ;

[0081] α≥0;

[0082] Among them, (θ m ,φ m ) is the direction of the mth ground detection target, M r is the number of ground detection targets, a(θ m ,φ m ) is the steering vector of the mth ground detection target, a is the steering vector of the transmitting antenna, P b (θ m ,φ m ) is the ideal beam gain for the mth ground detection target, and R is the covariance matrix of the radar transmit signal. By solving this problem, the transmit signal covariance matrix R corresponding to the ideal beam pattern can be obtained, which represents the perception performance in the communication perception integration.

[0083] In an optional embodiment of the present application, based on the constraints and the radar beam pattern optimization problem, the process of constructing the drone trajectory and beamforming optimization problem in the communication and perception integration scenario includes:

[0084] Under the ideal radar beam pattern, obtain The beam gain in the direction is:

[0085]

[0086] Among them, θ i is the elevation angle of the line connecting the UAV and the i-th ground user relative to the horizontal plane, φ i is the rotation angle of the line connecting the UAV and the i-th ground user relative to the y-axis;

[0087] Under the model of the communication process between the UAV and the ground user and the model of the perception process between the UAV and the ground detection target, obtain The beam gain in the direction is:

[0088]

[0089] The beam pattern in the communication and perception integration scenario satisfies the ideal radar beam pattern, and its expression is:

[0090]

[0091] Where β is a scaling factor such that the actual beam pattern matches the shape of the ideal beam pattern;

[0092] Construct the optimization problem of UAV trajectory and beamforming in the communication and perception integration scenario, and its expression is:

[0093]

[0094] Where R is the covariance matrix of the radar transmit signal. It should be noted that R is the result of the optimization problem in the above steps and represents the transmit covariance matrix corresponding to the ideal beam pattern. Since the entire process is divided into N time slots, and all constraints and optimization objectives are the same in each time slot, the optimization problem can be regarded as N independent problems. For the sake of notational simplicity, the time slot number n is omitted from the variables and they are defaulted to be functions of n. The optimization problem aims to fit the actual transmit covariance matrix with the ideal radar beam pattern covariance matrix so that the actual beam pattern matches the ideal beam pattern, thereby achieving the perception performance index.

[0095] The optimization problem of UAV trajectory and beamforming in the communication and perception integration scenario under constraints is constructed, and its expression is:

[0096]

[0097]

[0098]

[0099]

[0100] C4: ||r(n)-r(n-1)||=d;

[0101] in,

[0102] In an optional embodiment of the present application, see Figure 4 As shown, Figure 4 This is a schematic diagram of the relationship between beam gain and receive SINR threshold for the integrated communication and perception solution and the radar-only solution provided in an embodiment of the present invention. The process of simplifying the drone trajectory and beamforming optimization problem includes:

[0103] Based on the constraint condition C1, the mean square error penalty function λ(γ) is constructed, and its expression is:

[0104]

[0105] Where γ is the ground user signal-to-noise ratio threshold array, γ=[γ1,γ2,...,γ K ] T , γ i is the signal-to-noise ratio threshold of the i-th terrestrial user, and T is the transpose;

[0106] Based on the constraint C3, a i is the steering vector of the transmitting antenna, so that Q=[w1,w2,...,w K ], w is the vector of the ground user's transmit beamforming, and K is the total number of ground users; construct the first formula, which is expressed as follows:

[0107]

[0108] in, for a i The conjugate transposed matrix, w i is the transmit beamforming vector of ground user i, Q H is the conjugate transposed matrix of Q;

[0109] Substituting the first formula into the mean square error penalty function, we get:

[0110]

[0111] Among them, r i is the position of the UAV, Γ i is the minimum value of the signal-to-noise ratio received by the i-th ground user; r is the coordinate vector of the UAV;

[0112] The UAV trajectory and beamforming optimization problem is then expressed as:

[0113]

[0114]

[0115] C2:||r(n)-r(n-1)||=d;

[0116] Among them, ζ is the penalty coefficient, F is the matrix norm type, and R is the covariance matrix of the radar transmit signal; in this way, the optimization problem is transformed into an optimization problem with fewer constraints, lower complexity, and easier to solve.

[0117] In an optional embodiment of the present application, see Figure 5 As shown, Figure 5 This is a schematic diagram of the convergence speed of the Riemann conjugate gradient algorithm provided by an embodiment of the present invention. The process of solving the drone trajectory and beamforming optimization problem based on the Riemann conjugate gradient algorithm and obtaining the drone flight trajectory and beamforming matrix includes:

[0118] For the nth time slot, given Q(n) and r(n), solve for Q(n+1) and r(n+1);

[0119] Select several points from the drone's flight landing points, and record each selected point as r * (n+1), bring the selected points into the optimization problem of radar beam pattern in the UAV perception function, and get R * (n+1);

[0120] R * (n+1) and r * (n+1) is introduced into the UAV trajectory and beamforming optimization problem in the communication and perception integration scenario, and it is transformed into an optimization problem related only to Q, that is:

[0121]

[0122]

[0123] Based on the fact that the domain of the optimization problem is a complex hypersphere, the optimization problem is reformulated as follows:

[0124]

[0125] Where O is the radius The complex hypersphere is popular;

[0126] Use the Riemann conjugate gradient algorithm to solve Q * (n+1) and the minimum objective function value f(Q*(n+1));

[0127] Each selected point r * (n+1) corresponds to a Q *(n+1) and a minimum objective function value f(Q*(n+1)), select the r that minimizes the minimum objective function value f(Q*(n+1)) * (n+1), that is, Q(n+1) and r(n+1);

[0128] Get the Q and r corresponding to all time slots, that is, get the UAV flight trajectory and beamforming matrix.

[0129] It should be noted that Figure 5 The illustrated embodiment illustrates the running convergence speed of the "alternating optimization method based on Riemann conjugate gradient", where the horizontal axis is the number of iterations of the running algorithm and the vertical axis is the optimization objective function value. The figure shows two cases where the number of drone antenna arrays is 9 and 20 respectively. When the design method provided in this application is adopted, after 5 iterations, the optimization objective function value drops to a stable value, which reflects the efficiency and speed of this method.

[0130] Based on the same inventive concept, the present application also provides a design system for joint trajectory and beamforming in a synergistic integrated UAV network, including a communication system and a perception system, and the communication system and the perception system share a hardware transmitter and also share spectrum resources; in the prior art, the communication-perception joint system relies on an additional "control center" to exchange communication and perception information to achieve dual-system integration, but this integration efficiency is not high and will generate additional resource consumption (hardware, power); while the communication system and the perception system in this embodiment, the two systems share a hardware transmitter and also share spectrum resources, saving resources while making the information interaction between the two systems faster and the system performance stronger.

[0131] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A design method for joint trajectory and beamforming in a synaesthesia-integrated UAV network, characterized by: include: Constructing a network scenario including a UAV, a ground user, and a ground detection target; wherein the UAV completes a flight from a starting point to an end point, communicates with the ground user during the flight, and simultaneously senses the ground detection target; Constructing a model of the communication process between the UAV and the ground user, and constructing a model of the perception process between the UAV and the ground detection target; Constructing constraint conditions for a model of a communication process between the UAV and the ground user and a model of a perception process between the UAV and the ground detection target; The optimization problem of constructing the radar beam pattern in the UAV perception function is expressed as: ； ； ； ； in, For the The direction of the ground detection target, is the number of ground detection targets, For the The guidance vector of the ground detection target, is the steering vector of the transmitting antenna, For the first The ideal beam gain for a ground detection target, is the covariance matrix of the radar transmission signal, is the flight altitude of the UAV, Indicates the total power of the drone’s transmitting antenna, is a column vector whose elements are all 1, is the number of transmitting antennas; Based on the constraints and radar beam pattern optimization problem, the UAV trajectory and beamforming optimization problem in the communication and perception integrated scenario is constructed, including: Under the ideal radar beam pattern, obtain Beam gain in direction ,Right now: ； in, For drones and The elevation angle of the ground user line relative to the horizontal plane, For drones and terrestrial user lines relative to The rotation angle of the axis; Under the model of the communication process between the UAV and the ground user and the model of the perception process between the UAV and the ground detection target, obtain Beam gain in direction ,Right now: ； in, is the covariance matrix of the precoded communication symbols; The beam pattern in the communication and perception integration scenario satisfies the ideal radar beam pattern, and its expression is: ； in, is the scale factor; Construct the optimization problem of UAV trajectory and beamforming in the communication and perception integration scenario, and its expression is: ； in, For ground users The transmit beamforming vector, for The conjugate transposed matrix of is the total number of ground users; The optimization problem of UAV trajectory and beamforming in the communication and perception integration scenario under constraints is constructed, and its expression is: ； ； ； ； ； in, , is the channel gain per unit distance, is the ground user location, For drones The position of the time slot, for The conjugate transposed matrix of is the steering vector of the transmitting antenna, For drones Time slot pair The transmit beamforming vector of the ground user is, is the noise power received by ground users, is the flight distance of the UAV in each time slot, For summing variables; Simplify UAV trajectory and beamforming optimization problems, including: Based on constraints , construct the mean square error penalty function , whose expression is: ； in, is the ground user signal-to-noise ratio threshold array, , For the The signal-to-noise ratio threshold for ground users, is transposed; Based on constraints , making , is the steering vector of the transmitting antenna, so that , is the vector of the transmit beam formed by the ground user; construct the first formula, which is expressed as follows: ； in, for The conjugate transposed matrix of for The conjugate transposed matrix of ; Substituting the first formula into the mean square error penalty function, we get: ； in, is the position of the drone, is the coordinate vector of the UAV; The UAV trajectory and beamforming optimization problem is then expressed as: ； ； ； in, is the penalty coefficient, is the matrix norm type, and is the position of the drone in the adjacent time slot; The Riemann conjugate gradient algorithm is used to solve the UAV trajectory and beamforming optimization problem, and the UAV flight trajectory and beamforming matrices are obtained, including: For the time slots, known and , solve and ; Select several points from the drone's flight landing points, and record the number of each selected point as , bringing the selected points into the optimization problem of radar beam pattern in the UAV perception function, we get ; Will and Introduced into the UAV trajectory and beamforming optimization problem in the communication perception integration scenario, it is transformed into The related optimization problem is: ； ； Based on the fact that the domain of the optimization problem is a complex hypersphere, the optimization problem is reformulated as follows: ； in, The radius is The complex hypersphere is popular; Solved using the Riemann conjugate gradient algorithm and the minimum objective function value ; Each selected point Each corresponds to one and a minimum objective function value , select the minimum objective function value Minimum corresponding , that is, we get and ; Get the corresponding time slots and , that is, obtaining the matrix of UAV flight trajectory and beamforming.

2. The design method of joint trajectory and beamforming in the synaesthesia integrated UAV network according to claim 1 is characterized in that: The model of the communication process between the UAV and the ground user and the model of the perception process between the UAV and the ground detection target include: The communication channel between the UAV and the ground user , whose expression is: ； ； in, is the channel gain per unit distance, is the wavelength, is the steering vector of the transmitting antenna, is the horizontal coordinate of the UAV on the horizontal plane, is the vertical coordinate of the UAV on the horizontal plane, is the time slot number, is the horizontal coordinate of the ground user, is the vertical coordinate of the ground user, is the ground user serial number, is the flight altitude of the UAV; The signal-to-noise ratio received by the ground user is expressed as: ； in, for The conjugate transposed matrix of is the location of the ground user, is the flight distance of the UAV in each time slot, For drones The location of the time slot, For drones Time slot pair The transmit beamforming vector of the ground user is, is the noise power received by ground users; Covariance matrix of precoded communication symbols , whose expression is: ； in, For ground users The transmit beamforming vector, for The conjugate transposed matrix of is the total number of ground users; the ground users and the ground detection targets are A single antenna with a known position.

3. The design method of joint trajectory and beamforming in the synaesthesia integrated UAV network according to claim 2 is characterized in that: The steering vector of the transmitting antenna The expression is: ； in, is the Kronecker product, is the steering vector matrix of the transmitting antenna The transposed matrix of is the transmitting antenna spacing, For drones and The elevation angle of the ground user line relative to the horizontal plane, For drones and terrestrial user lines relative to The rotation angle of the axis; is the wavelength, is the number of antenna array rows, is the number of antenna array column directions; in, and is the function of the UAV position and the ground user position, that is: ； ； in, is the horizontal coordinate of the UAV on the horizontal plane, is the vertical coordinate of the UAV on the horizontal plane, is the time slot number, is the horizontal coordinate of the ground user, is the vertical coordinate of the ground user, is the ground user serial number, is the flight altitude of the drone.

4. The design method of joint trajectory and beamforming in the synaesthesia integrated UAV network according to claim 1 is characterized in that: The constraints of the model of the communication process between the UAV and the ground user and the model of the perception process between the UAV and the ground detection target include: Signal-to-noise ratio received by ground users To limit, that is: ； in, For the The minimum signal-to-noise ratio received by a ground user; Total power of the UAV transmitting antenna To limit, that is: ； in, is a column vector whose elements are all 1, is the number of transmitting antennas; Limit the trajectory of the drone, namely: ； in, and is the position of the UAV in the adjacent time slot, is the flight distance of the UAV in each time slot.

Images