Cooperative detection method and system in unmanned aerial vehicle communication and sensing integrated network

Abstract

The invention belongs to but not limited to the technical field of unmanned aerial vehicle communication, and discloses a cooperative detection method and system in an unmanned aerial vehicle communication and sensing integrated network, an unmanned aerial vehicle communication and sensing integrated system is constructed, and a base station and a detection unmanned aerial vehicle jointly execute a detection task; in a cooperative detection scene, establishing an unmanned aerial vehicle communication and sensing signal transmitting and receiving signal model; according to a maximum likelihood criterion, sensing data fusion is carried out on the detection sets of the base station and the unmanned aerial vehicle by adopting an EKF algorithm; mathematical modeling is carried out on the maximization problem of the reachable communication rate; the communication performance is enhanced by jointly optimizing the trajectory of the unmanned aerial vehicle and a beam forming algorithm, and the optimization problem of maximizing the communication rate is solved. According to the invention, the problems of high maneuverability, miniaturization and high possibility of being blocked by buildings of the black flight unmanned aerial vehicle can be solved, the sensing accuracy is effectively improved by using communication fusion sensing information, the cooperative sensing distance is increased, and the detection performance is enhanced.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of unmanned aerial vehicle communication technology, and in particular relates to a collaborative detection method and system in an unmanned aerial vehicle interawareness integrated network. Background Art

[0002] In recent years, with the development of drone technology, drones have shown great potential in many fields, such as logistics, agriculture, disaster relief and national defense. However, due to their low cost, simple operation and convenient carrying, some drones already have strong load capacity and are easily used by criminals, seriously disrupting air traffic control order. Especially in the increasingly severe situation of anti-terrorism, security and stability maintenance, if drones are used by criminals, they will bring serious hidden dangers and threats to social security and national defense construction. In complex scenes such as cities, drones are difficult to achieve accurate and rapid detection in environments with high-rise buildings due to their high maneuverability, miniaturization and susceptibility to obstruction by buildings. This not only limits the detection capability of high-power radars for long-distance targets, but also makes it difficult to meet modern countermeasures.

[0003] Wireless communication is used to coordinate the UAV with the mobile communication network, and the sensor data of multiple nodes in the network are integrated, so that the collaborative sensing area far exceeds the sensing area of ​​a single radar. However, in the process of integrating UAVs with cellular networks, there are many challenges, such as mobility, interference, and decreased communication quality. Based on this problem, cooperative detection between UAVs and base stations has been proven to be an effective method. Through the collaborative work of multiple UAVs, the overall performance of the system can be significantly improved. In addition, the inter-sensory integration technology achieves load saving and spectrum reuse by using a unified transceiver and spectrum resources, while providing efficient collaborative detection and perception data sharing capabilities, supporting positioning, ranging, speed measurement, imaging, detection, identification and other functions, which can effectively alleviate the problem of insufficient detection capabilities of single radar sensors and achieve the improvement of overall network performance and business capabilities.

[0004] At present, the research on UAV interawareness integrated network mainly considers the use of communication-assisted perception, or perception-assisted communication. This model mainly emphasizes one-way functional complementarity, and rarely involves the positive feedback mechanism between the two. Summary of the invention

[0005] In view of the problems that UAVs are difficult to detect due to their high maneuverability, miniaturization and susceptibility to obstruction by buildings, the present invention proposes a collaborative detection method and system in a UAV synaesthesia integrated network, and reasonably designs a perception data fusion algorithm and a joint UAV trajectory and beamforming design algorithm.

[0006] The present invention is implemented as follows: a collaborative detection method in a UAV synaesthesia integrated network comprises the following steps:

[0007] Step 1: Build a UAV interawareness integrated system, where the base station and the detection UAV jointly perform the detection task; in the collaborative detection scenario, establish a UAV communication and perception signal transmission and reception signal model;

[0008] Step 2: According to the maximum likelihood criterion, the detection set of the base station and the UAV is fused using the EKF algorithm for perception data;

[0009] Step 3, mathematically model the problem of maximizing the achievable communication rate;

[0010] Step 4: Enhance communication performance by jointly optimizing the UAV trajectory and beamforming algorithm to solve the optimization problem of maximizing the achievable communication rate.

[0011] Furthermore, the UAV adopts synaesthesia integrated multi-beam signaling, using fixed sub-beams to perform communication functions and scanning sub-beams to perform perception functions.

[0012] Furthermore, the detection drone is connected to the ground cellular network and flies from the initial point to the end point within a given time. During this period, the detection drone maintains reliable communication with the cellular network and monitors the surrounding K intrusion drones in real time. The detection drone always flies at a fixed altitude H, and its flight time is t∈[0,T], which is divided into N=T / Δt equal-length time slots, and the length of each time slot is Δt; n∈[1,2,…,N] represents the nth time slot; each time slot is set small enough, and the position of the detection drone remains unchanged during this time slot. The flight trajectory of the detection drone is q u,c [n] = [x u,c [n],y u,c [n],H] T ,1≤n≤N.

[0013] Furthermore, the transmission signal of the detection drone is where s i [n] indicates the information transmitted and w i [n] represents the corresponding beamforming vector; since the detection UAV carries limited energy, its transmission power is limited to

[0014] The received signal is where h c [n] represents the channel vector between the detection drone and the base station, z c [n] is the variance, The first term represents the desired received communication signal, and the second term represents the channel interference;

[0015] The signal-to-interference-plus-noise ratio (SINR) of the received communication signal is

[0016] The achievable communication rate for receiving communication signals is C com [n] = log2(1 + γ c [n]);

[0017] The transmit beam gain of the sensing function is expressed as

[0018]

[0019] in represents the steering vector, θ n represents the angle of arrival.

[0020] Furthermore, the detection results of the UAV and the base station are represented as S U With S B , where S = (Ω, V), represents the location information, and V = μ represents the movement state information. The normalized Euclidean distance between two detection result sets is expressed as in as well as They represent the maximum Euclidean distance between the location information and the movement status information respectively.

[0021] Furthermore, the specific operation steps of the EKF perception data fusion algorithm are as follows:

[0022] A1. Initialize the empty set S F ;

[0023] A2. Calculate the normalized Euclidean distance between two detection result sets;

[0024] A3. Traverse the two detection result sets, Γ d is the set threshold value, if Ψ i,j ≤Γ d , perform perception data fusion and put it into set S F ; Otherwise, put it directly into the set S F .

[0025] Furthermore, the specific steps of data fusion are as follows:

[0026] A31, state prediction: x n|n-1 =g(x n-1 )+ω n ;

[0027] A32. Forecast covariance matrix calculation:

[0028] A33, Kalman filter gain calculation:

[0029] A34, Status update: x n =x n|n-1 +K n (y n -h(x n|n-1 )).

[0030] Furthermore, the drone needs to move from the starting point q within the specified time. I Fly to the end point F , the maximum flight speed is V max , the maximum moving distance between two time slots is D m =V max Δt; flight limit is q u,c [1] = q I ,q u,c [N] = q F , and ||q u,c [n+1]-q u,c [n]||≤D m .

[0031] Furthermore, the UAV trajectory and beamforming algorithm are jointly optimized, which specifically includes the following steps:

[0032] A1. Initialize the beamforming vector matrix W and the UAV flight trajectory Q, and set the number of iterations l = 0;

[0033] A2. For a given UAV flight trajectory Q, the original optimization problem is transformed into an optimization problem with only the beamforming vector matrix W as the optimization variable. Using the semi-positive relaxation technique, the non-convex problem is transformed into a convex optimization problem, and the convex optimization toolkit can be used to solve the optimal beamforming vector matrix W. l+1 ;

[0034] A3. For a given beamforming vector matrix W l+1 The original optimization problem is transformed into an optimization problem with only the UAV flight trajectory Q as the optimization variable. This is a typical non-convex optimization problem. By using the continuous convex approximation technology, the non-convex problem is transformed into a convex optimization problem, and the convex optimization toolkit can be used to solve the optimal power allocation matrix Q. l+1 ;

[0035] A4. Using the already obtained beamforming vector matrix W l+1 and the drone flight trajectory Q l+1 Obtain the maximum communication rate achievable in the current iteration make γ0 is the set threshold value. If γ≥γ0, update the number of iterations l=l+1 and return to step A2; otherwise, end directly.

[0036] Another object of the present invention is to provide a cooperative detection method in a UAV synergy integrated network and a cooperative detection system in a UAV synergy integrated network, comprising a detection UAV and a base station;

[0037] The base station and the detection drone jointly perform the detection task; in the collaborative detection scenario, a model for transmitting and receiving drone communication and perception signals is established;

[0038] According to the maximum likelihood criterion, the detection sets of the base station and the UAV are fused with the EKF algorithm for perception data;

[0039] Mathematically model the problem of maximizing the achievable communication rate;

[0040] The communication performance is enhanced by jointly optimizing the UAV trajectory and the beamforming algorithm, solving the optimization problem of maximizing the achievable communication rate.

[0041] Through data simulation, the effectiveness of the algorithm proposed in this invention in the integrated design of UAV communication and perception is verified. The simulation results show that compared with the traditional synaesthesia integration focusing on one-way functional complementation, the present invention can significantly improve the perception ability while ensuring the performance of the communication system. Communication and perception complement each other, providing strong support for the safe flight of UAVs in complex urban environments. In addition, the implementation of the algorithm will help promote the development of UAV communication technology and lay the foundation for the construction of future smart cities.

[0042] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0043] First, in traditional UAV communication systems, communication and perception are usually completed by independent modules or devices, resulting in low data transmission efficiency, slow system response speed, and difficulty in coping with multi-node network topology, resulting in untimely information sharing and delayed decision-making. How to design a method suitable for multi-node networking collaborative detection and realize dynamic optimization and scheduling of communication, computing and power resources in UAV interawareness collaborative systems is the focus of technical research.

[0044] The present invention proposes a cooperative detection method and system in a UAV synaesthesia integrated network, taking into account the two-way dynamic cooperation between synaesthesia. Under this mechanism, communication and perception complement each other, and the perception accuracy is effectively improved by using communication to fuse perception information, and the cooperative perception distance is increased. The enhancement of detection performance can be further enhanced by jointly designing UAV trajectories and beamforming design to enhance communication performance, filling the existing research gap.

[0045] The collaborative detection method and system in the integrated network of unmanned aerial vehicle synaesthesia of the present invention can solve the problem that the high maneuverability, miniaturization and susceptibility to obstruction by buildings of illegal unmanned aerial vehicles make it difficult for traditional radar-based detection methods to achieve accurate and rapid detection in an environment with tall buildings. At the same time, the two-way dynamic collaboration between synaesthesia is considered, and communication and perception complement each other. Communication is used to fuse perception information to effectively improve perception accuracy and enhance collaborative perception distance. The enhancement of detection performance can be further enhanced by jointly designing unmanned aerial vehicle trajectories and beamforming design to enhance communication performance. The method of the present invention has the following advantages:

[0046] 1. The drone uses a multi-beam signal with integrated interoception, using fixed sub-beams to perform communication functions and scanning sub-beams to perform perception functions. This improves the flexibility of the system, allowing the drone to flexibly adjust communication and perception strategies in different mission scenarios, thereby improving the efficiency of mission completion.

[0047] 2. Designing an EKF algorithm to integrate detection information from detection drones and base stations can effectively improve detection accuracy and increase detection distance.

[0048] 3. The joint design of drone trajectory and beamforming design further enhances the communication performance. An iterative algorithm is proposed to solve the optimization problem of maximizing the achievable communication rate. This not only improves the stability of the communication link, but also maximizes the communication rate, thereby improving the communication efficiency of the entire network.

[0049] 4. The proposed algorithm is suitable for UAV communication integrated networks with different performance requirements and different flight times, ensuring the stability and reliability of the system and significantly expanding the application field of UAV communications.

[0050] Second, the expected benefits and commercial value of the technical solution of the present invention after transformation are:

[0051] By adopting the technical solution of the present invention, accurate perception and real-time tracking of low-altitude drones can be achieved, effectively solving the problem of safety monitoring of low-altitude illegal drones, and providing strong technical support for government regulatory departments. At the same time, by providing efficient technical monitoring means, the safety and reliability of low-altitude flight missions are guaranteed, thereby promoting the widespread application of drones in express logistics, geographic surveying and mapping, urban management, emergency rescue and other fields, and promoting the rapid development of the low-altitude economy. Further, for telecom operators, the application of synaesthesia integration technology is expected to provide them with new business growth points and open up market space in the field of low-altitude economy.

[0052] (2) The technical solution of the present invention fills the technical gap in the industry at home and abroad:

[0053] The present invention utilizes 5G-A synaesthesia technology, and through the continuous networking and collaborative detection capabilities of synaesthesia base stations and detection drones, realizes functions such as multi-target detection, continuous trajectory tracking, and electronic fence intrusion warning for low-altitude drones. The application of this technology not only meets the application needs of low-altitude security, traffic management, etc., but also provides technical support for the steady development of the low-altitude economy. Previously, the technology was verified by simulation data, proving its global and high-precision low-altitude perception capabilities. The breakthrough of this technical solution promotes and guides the further development of synaesthesia integration technology in the field of low-altitude economy, fills this technical gap, and lays a solid foundation for the rapid development of the low-altitude economy.

[0054] (3) The technical solution of the present invention solves the technical problems that people have been eager to solve but have never been able to solve successfully:

[0055] In complex scenes such as cities, drones are difficult to detect accurately and quickly with traditional radar-based detection methods in environments with tall buildings due to their high maneuverability, miniaturization, and susceptibility to obstruction by buildings. This not only limits the detection capability of high-power radars for long-distance targets, but also makes it difficult to meet the needs of modern countermeasures. Therefore, more advanced technical means are urgently needed to meet this challenge. At present, 5G networks have been widely deployed. With their large bandwidth and large antenna array technology, they can not only provide high-precision perception capabilities, but also achieve low-cost, all-weather ubiquitous perception by reusing base station sites, equipment, and spectrum resources. This provides a new solution for drone detection in complex urban environments. The integrated system of synaptic computing can sense and track drones in real time, and then establish electronic fences to prevent drones from invading specific areas. This systematic networked perception capability not only improves the safety and reliability of drone flights, but also provides strong technical support for public safety in urban environments.

[0056] (4) The technical solution of the present invention overcomes technical prejudice:

[0057] The technical solution of the present invention overcomes technical prejudice to a certain extent. Traditional radar detection technology has poor recognition ability when facing "low, slow and small" targets, while the synaesthesia integration technology realizes accurate perception and real-time tracking of low-altitude UAVs by integrating wireless communication and perception functions. This technical solution not only improves the detection accuracy, but also further enhances the reliability and accuracy of the system through the integrated application of technologies such as multi-node collaboration, data fusion and multi-dimensional resource optimization, providing strong support for low-altitude security.

[0058] Third, the present invention proposes a collaborative detection method in a UAV synaesthesia integrated network, which mainly realizes the integration of the UAV's communication and perception functions through the collaborative work of the base station and the detection UAV. In this system, by establishing a transmission and reception model of UAV communication and perception signals, it is possible to perform communication and perception tasks simultaneously, and use the extended Kalman filter (EKF) algorithm to perform perception data fusion to improve detection accuracy. By jointly optimizing the UAV trajectory and beamforming algorithm, the communication performance is further enhanced and the achievable communication rate is improved.

[0059] The present invention uses a multi-beam signal design for the communication and perception functions of the drone, uses fixed sub-beams to achieve communication functions, and uses scanning sub-beams to perform perception functions, ensuring that the detection drone always maintains reliable communication capabilities during flight and monitors the surrounding environment in real time. This multi-beam signal design not only ensures the reliability of communication, but also greatly improves the perception accuracy, and is particularly suitable for complex and changeable collaborative detection scenarios.

[0060] In terms of technical implementation, the EKF perception data fusion algorithm, semi-positive relaxation, and continuous convex approximation technology are used to jointly optimize the trajectory and beamforming algorithm of the UAV, solving the non-convex optimization problem of maximizing the achievable communication rate. This method can not only ensure the optimal communication rate of the UAV under limited energy conditions, but also effectively improve the perception accuracy and reliability of the system.

[0061] This method has made significant technological progress based on existing technologies. It has not only achieved innovative breakthroughs in the coordination of drone communication and perception functions, but also demonstrated high practicality and broad application prospects in actual industrial applications.

[0062] Fourth, the present invention solves several key technical problems existing in the prior art in the UAV synaesthesia integrated network by introducing main parameters, algorithms and mathematical models, and has achieved significant technical progress.

[0063] First, the communication and perception signal transmission and reception model of the UAV in the present invention combines the maximum likelihood criterion with the extended Kalman filter (EKF) algorithm to achieve the perception data fusion of multiple detection results. The EKF algorithm can effectively handle the state estimation problem of nonlinear systems, so that the perception accuracy of UAVs in complex environments has been greatly improved. By calculating the normalized Euclidean distance, the detection results are fused and filtered, which solves the problem of difficulty in fusion of multi-source perception data and insufficient accuracy in the prior art.

[0064] Secondly, the present invention adopts a mathematical modeling method to maximize the achievable communication rate. By modeling the communication channel of the UAV and combining it with the beamforming algorithm, the communication performance is optimized. In the prior art, the communication rate is often restricted by the limited energy of the UAV and the complex communication environment. The present invention jointly optimizes the trajectory and beamforming algorithm of the UAV, applies semi-definite relaxation technology and continuous convex approximation technology, and transforms the original non-convex optimization problem into a convex optimization problem, thereby effectively solving the optimal beamforming matrix and flight trajectory, and significantly improving the communication performance of the UAV.

[0065] Third, the present invention designs a joint optimization of drone trajectory and beamforming algorithm. By optimizing the beamforming vector matrix and drone flight trajectory through multiple iterations, it not only improves the communication rate, but also greatly extends the drone's continuous working ability under limited energy conditions. This effectively solves the energy consumption bottleneck problem that is common in the prior art.

[0066] In summary, the present invention achieves an effective solution to the technical problems of the prior art by optimizing the perception and communication functions in the UAV interawareness integrated network, including technical breakthroughs in multi-source data fusion, communication rate optimization, and energy consumption management, which significantly improves the overall performance of the system and promotes the application and development of UAV collaborative detection technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A flow chart of a collaborative detection method in a UAV synergy integrated network provided by an embodiment of the present invention;

[0068] Figure 2 A schematic diagram of the working principle of the synaesthesia integrated multi-beam provided in an embodiment of the present invention;

[0069] Figure 3 A flow chart of the EKF perception data fusion algorithm provided by an embodiment of the present invention;

[0070] Figure 4 A flow chart of a joint optimization UAV trajectory and beamforming algorithm provided in an embodiment of the present invention;

[0071] Figure 5 A structural diagram of a collaborative detection system in a UAV synergy integrated network provided by an embodiment of the present invention;

[0072] Figure 6 A simulation diagram showing the performance comparison of the algorithm proposed under different sensing beam gain constraints provided by an embodiment of the present invention with other basic algorithms in terms of achievable communication rates;

[0073] Figure 7 A simulation diagram comparing the performance of the algorithm proposed under different flight times at achievable communication rates provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0075] The embodiment of the present invention provides a collaborative detection method in a UAV synaesthesia integrated network, and constructs a UAV synaesthesia integrated system. The system has dual functions of communication and perception, and the base station and the detection UAV jointly perform the detection task. Information sharing is achieved through the communication function between the base station and the detection UAV, and the perception data is integrated, thereby effectively improving the detection accuracy and increasing the detection distance; the enhancement of detection performance can be further enhanced by jointly designing the UAV trajectory and beamforming design to enhance the communication performance, thereby designing an iterative algorithm to further solve the optimization problem of maximizing the achievable communication rate; data simulation verifies the effectiveness of the proposed method.

[0076] like Figure 1 As shown, a collaborative detection method in a UAV synaesthesia integrated network provided by an embodiment of the present invention includes the following steps:

[0077] Step 1: Build a UAV interawareness integrated system, where the base station and the detection UAV jointly perform the detection task; in the collaborative detection scenario, establish a UAV communication and perception signal transmission and reception signal model;

[0078] Step 2: According to the maximum likelihood criterion, the detection set of the base station and the UAV is fused using the EKF algorithm for perception data;

[0079] Step 3, mathematically model the problem of maximizing the achievable communication rate;

[0080] Step 4: Enhance communication performance by jointly optimizing the UAV trajectory and beamforming algorithm to solve the optimization problem of maximizing the achievable communication rate.

[0081] like Figure 2 As shown, the detection UAV transmitting antenna uses a uniform linear array to generate multiple beams, and its transmission signal can be expressed as where s i [n] represents the baseband signal, represents the transmit beamforming vector, represents the phase shift vector, β R represents the power allocation factor, and Represent the beamforming vectors of the sensing and communication sub-beams respectively. By flexibly adjusting and β R, which can meet the needs of communication and perception at the same time, with fixed sub-beams performing communication functions and scanning sub-beams performing perception functions.

[0082] The detection drone is connected to the ground cellular network and flies from the starting point to the end point within a given time. During this period, it needs to maintain reliable communication with the cellular network and monitor the surrounding K in real time. s The detection drone always flies at a fixed height H. Its flight time is t∈[0,T], which is divided into N=T / Δt equal-length time slots, and the length of each time slot is Δt. n∈[1,2,…,N] represents the nth time slot. Each time slot is set small enough that the position of the detection drone remains unchanged during this time slot. The flight trajectory of the detection drone is q u,c [n] = [x u,c [n],y u,c [n],H] T ,1≤n≤N.

[0083] Since the detection drone carries limited energy, its transmission power is limited to The received signal is Where f0[n] represents the corresponding received signal beamforming vector, h c [n] represents the channel vector between the detection drone and the base station, z c [n] is the variance, Additive white Gaussian noise is a good simulation of background noise in many real physical systems. In communication systems, the noise source appears as additive noise when it is superimposed on the signal at the receiver. The white noise property means that the noise power is evenly distributed across all frequencies. The first term in the received signal formula represents the desired received communication signal, and the second term represents the channel interference.

[0084] Therefore, the signal-to-interference-plus-noise ratio (SINR) of the received communication signal is The achievable communication rate for receiving communication signals is C com [n] = log2(1 + γ c [n]). The transmit beam gain of the sensing function is thus expressed as in represents the steering vector, θ n represents the angle of arrival.

[0085] The detection results of the UAV and the base station are represented as S U With S B , where S = (Ω, V), Represents the target location information, r,θ, Respectively represent the distance, horizontal angle and pitch angle of the detected target, and V = μ represents the moving state information of the target. The normalized Euclidean distance of two detection result sets is expressed as in as well as They represent the maximum Euclidean distance between the location information and the movement status information respectively.

[0086] like Figure 3 As shown, the EKF algorithm is used for perception data fusion. The specific operation steps are as follows:

[0087] S31. Initialize the empty set S F ;

[0088] S32, calculating the normalized Euclidean distance between two detection result sets;

[0089] S33, traverse the two detection result sets, Γ d is the set threshold value, if Ψ i,j ≤Γ d , perform perception data fusion and put it into set S F ; Otherwise, put it directly into the set S F The specific steps of data fusion are as follows:

[0090] (1) State prediction: x n|n-1 =g(x n-1 )+ω n ;

[0091] (2) Calculation of prediction covariance matrix:

[0092] (3) Kalman filter gain calculation:

[0093] (4) Status update: x n =x n|n-1 +K n (y n -h(x n|n-1 )).

[0094] Among them, x n represents the state variable, y n represents the measurement variable, g(x) is the state transfer function, h(x) is the Jacobian matrix of the measurement function with respect to the state variable, as well as

[0095] The drone needs to travel from the starting point q within the specified time. I Fly to the end point F , the maximum flight speed is V max, the maximum moving distance between two time slots is D m =V max Δt. The flight limit is q u,c [1] = q I ,q u,c [N] = q F , and ||q u,c [n+1]-q u,c [n]||≤D m .

[0096] like Figure 4 As shown in the figure, the UAV trajectory and beamforming algorithm are jointly optimized. The specific steps are as follows:

[0097] S41, initialize the beamforming vector matrix W and the UAV flight trajectory Q, and set the number of iterations l = 0;

[0098] S42. For a given UAV flight trajectory Q, the original optimization problem is transformed into an optimization problem with only the beamforming vector matrix W as the optimization variable, which can be expressed as:

[0099] Optimization problem:

[0100] Constraints: P s,j (q u,c [n])≥Γ s

[0101]

[0102] At this time, the constraint objective function is a non-convex function. First, use the semi-positive definite relaxation technique to convert the non-convex problem into a convex optimization problem. The specific conversion steps are as follows:

[0103] S421, Order and at this time, Using the first-order Taylor formula to expand C com The lower bound of [n] is

[0104] in, represents the local value at the mth iteration. At this point, the optimization objective function has been transformed into a convex function;

[0105] S42, Order The original optimization problem is transformed into:

[0106] Optimization problem:

[0107] Constraints: tr(a H (q u,c [n])W k a(qu,c [n]))≥Γ s

[0108]

[0109] rank(W k )≤1

[0110] At this point, the optimization problem is a standard semi-positive definite relaxed optimization problem, and the optimal beamforming vector matrix W can be solved by the convex optimization toolkit. l+1 ;

[0111] S43. For a given beamforming vector matrix W l+1 , the original optimization problem is transformed into an optimization problem with only the UAV flight trajectory Q as the optimization variable, which can be expressed as

[0112] Optimization problem:

[0113] Constraints: P s,j (q u,c [n])≥Γ s

[0114] q u,c [1] = q I ,q u,c [N] = q F

[0115] ||q u,c [n+1]-q u,c [n]||≤D m

[0116] This is a typical non-convex optimization problem. By using continuous convex approximation technology, the non-convex problem can be converted into a convex optimization problem. The specific conversion steps are as follows:

[0117] S431, Order

[0118]

[0119] and Where F0[n] p,q represents the p-th row and q-th column of the matrix, d c,b Represents the distance between the drone and the base station, and the first-order Taylor formula is used to expand C com The lower bound of [n] is

[0120] in represents the local value at the mth iteration. At this point, the optimization objective function has been transformed into a convex function;

[0121] S432, Order and According to the first-order Taylor formula, P s,j (q u,c The lower bound of [n]) is At this point, the optimization objective constraints have been transformed into convex functions;

[0122] S433. In order to enhance the approximate accuracy, the confidence interval is set as follows: ||q (m+1) [n]-q (m) [n]||≤r (m) , r (m) represents the confidence region of the mth iteration;

[0123] S434. At this time, the original optimization problem is transformed into:

[0124] Optimization problem:

[0125] Constraints:

[0126] ||q (m+1) [n]-q (m) [n]||≤r (m)

[0127] This optimization problem is a convex optimization problem, so the convex optimization toolkit can be used to solve the optimal power allocation matrix Q l+1 ;

[0128] S44, using the beamforming vector matrix W that has been obtained l+1 and the drone flight trajectory Q l+1 Obtain the maximum communication rate achievable in the current iteration make γ0 is the set threshold value. If γ≥γ0, update the number of iterations l=l+1 and return to step A2; otherwise, end directly.

[0129] like Figure 5 As shown, an embodiment of the present invention provides a collaborative detection method in a UAV synergy integrated network and a collaborative detection system in a UAV synergy integrated network, including a detection UAV and a base station;

[0130] The base station and the detection drone jointly perform the detection task; in the collaborative detection scenario, a model for transmitting and receiving drone communication and perception signals is established;

[0131] According to the maximum likelihood criterion, the detection sets of the base station and the UAV are fused with the EKF algorithm for perception data;

[0132] Mathematically model the problem of maximizing the achievable communication rate;

[0133] The communication performance is enhanced by jointly optimizing the UAV trajectory and the beamforming algorithm, solving the optimization problem of maximizing the achievable communication rate.

[0134] The collaborative detection method and system in the UAV synergy integrated network of the present invention has a wide range of application fields and potential related products, specifically including the following aspects:

[0135] 1. Urban airspace management and safety monitoring

[0136] Application field: This invention can be applied to urban airspace management, especially the monitoring and management of illegal drones in urban high-rise areas. Through collaborative detection technology, the system can effectively detect and locate illegal drones in complex urban environments, providing technical support for the safety of urban airspace.

[0137] Related products: UAV airspace monitoring system, urban safety management platform, illegal UAV detection and jamming equipment.

[0138] 2. Smart city and intelligent traffic management

[0139] Application fields: The present invention can be applied to smart cities and intelligent traffic management systems to monitor and manage drone traffic in real time and ensure the safe operation of drones. At the same time, the breadth and accuracy of urban monitoring can be improved through the coordinated detection of drones.

[0140] Related products: Intelligent traffic management system, drone traffic monitoring platform, urban air traffic management system.

[0141] 3. Public safety and emergency response

[0142] Application fields: The present invention can be applied to the public safety field, such as security monitoring of large-scale events, disaster emergency response and other scenarios. Through the coordinated detection of drones, it is possible to quickly monitor and evaluate the on-site situation and provide support for emergency decision-making.

[0143] Related products: Emergency drone monitoring system, disaster site real-time monitoring platform, public safety management system.

[0144] 4. Drone logistics and delivery network

[0145] Application areas: In drone logistics and delivery networks, the technology of the present invention can be used to optimize the communication and perception functions of drones, improve the coordinated scheduling capabilities of drones during the delivery process, and ensure the safe flight of drones in complex environments.

[0146] Related products: UAV logistics distribution system, intelligent UAV dispatching platform, UAV communication and navigation system.

[0147] Relevant evidence of the technical effects achieved by the embodiments of the present invention.

[0148] According to the proposed algorithm, data simulation is carried out. Under different perception beam gain limits and different flight times, the designed algorithm is analyzed and verified to be effective. At the same time, it is compared with other basic algorithms to reflect the advantages of the algorithm in improving communication and perception performance.

[0149] In the simulation process of the present invention, it is assumed that the flying altitude of the UAV remains unchanged during the entire flight cycle, and the initial position of the UAV is q I =[20,20,100], the final position is q I =[300,300,100]. The path loss value is set to β0 = -60dB. The noise power is set to The maximum speed of the drone is set to 0.5m / s, our detection area is 600×600, and there are four target drones in the target area. The specific simulation process is as follows:

[0150] By means of software simulation, the performance of the present invention and the traditional method in different scenarios are simulated, and the technical effects are compared and analyzed. Figure 6 The simulation diagram shows the performance comparison of the proposed algorithm with other basic algorithms in terms of achievable communication rate under different sensing beam gain constraints. The implementation of other basic algorithms is as follows: the beamforming algorithm indicates that the trajectory of the drone is not optimized during the entire iteration process, and it always keeps flying in a straight line, and only the beamforming vector is optimized; the joint optimization algorithm jointly optimizes the trajectory of the drone and the beamforming vector. Figure 6 In the figure, we can see that the proposed algorithm is superior to other algorithms in terms of achievable communication rate. As the perception beam gain increases, the achievable communication rate continues to decrease, which shows that there is a trade-off between perception and communication functions. By jointly optimizing the trajectory and beamforming vector of the drone, and based on the gain brought by the EKF perception data fusion algorithm, the communication and perception performance of the system can be improved.

[0151] Figure 7 The simulation diagram shows the performance comparison of the proposed algorithm in terms of achievable communication rate under different flight times. Figure 7 It can be seen that for the same flight altitude, the longer the flight time, the higher the achievable communication rate. In addition, when the UAV's flight time is longer, its flight trajectory is closer to the communication user, which can improve the communication performance of the system while keeping the perception performance unchanged.

[0152] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0153] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A collaborative detection method in a UAV synaesthesia integrated network, It is characterized in that The following steps are involved: Step 1: Build a drone interawareness integrated system, where the base station and the detection drone jointly perform the detection task; In the collaborative detection scenario, a model for transmitting and receiving signals for UAV communication and perception is established; Step 2: According to the maximum likelihood criterion, the detection set of the base station and the UAV is fused using the EKF algorithm for perception data; Step 3, mathematically model the problem of maximizing the achievable communication rate; Step 4: Enhance communication performance by jointly optimizing the UAV trajectory and beamforming algorithm to solve the optimization problem of maximizing the achievable communication rate.

2. The collaborative detection method in the UAV synaesthesia integrated network as claimed in claim 1, It is characterized in that The UAV adopts synaesthesia integrated multi-beam signal, using fixed sub-beams to perform communication functions and scanning sub-beams to perform perception functions.

3. The collaborative detection method in the UAV synaesthesia integrated network as claimed in claim 1, It is characterized in that The detection drone is connected to the ground cellular network and flies from the starting point to the end point within a given time. During this period, the detection drone maintains reliable communication with the cellular network and monitors the surrounding K intrusion drones in real time. The detection drone always flies at a fixed altitude H. Its flight time is t∈[0,T], which is divided into N=T / Δt equal-length time slots, and the length of each time slot is Δt. n∈[1,2,…,N] represents the nth time slot. Each time slot is set small enough so that the position of the detection drone remains unchanged during this time slot. The flight trajectory of the detection drone is q u,c [n] = [x u,c [n],y u,c [n],H] T ,1≤n≤N.

4. The collaborative detection method in the UAV synaesthesia integrated network according to claim 1, It is characterized in that The transmission signal of the detection drone is where s i [n] indicates the information transmitted and w i [n] represents the corresponding beamforming vector; since the detection UAV carries limited energy, its transmission power is limited to The received signal is where h c [n] represents the channel vector between the detection drone and the base station, z c [n] is the variance, The first term represents the desired received communication signal, and the second term represents the channel interference; The signal-to-interference-plus-noise ratio (SINR) of the received communication signal is The achievable communication rate for receiving communication signals is C com [n] = log 2 (1+γ c [n]); The transmit beam gain of the sensing function is expressed as: in represents the steering vector, θ n represents the angle of arrival.

5. The collaborative detection method in the UAV synaesthesia integrated network as claimed in claim 1, It is characterized in that The detection results of the UAV and the base station are represented as S U With S B , where S = (Ω, V), represents the location information, and V = μ represents the mobile state information. The normalized Euclidean distance between the two detection result sets is expressed as in as well as They represent the maximum Euclidean distance between the location information and the movement status information respectively.

6. The collaborative detection method in the UAV synaesthesia integrated network as claimed in claim 1, It is characterized in that The specific operation steps of the EKF perception data fusion algorithm are as follows: A1. Initialize the empty set S F ; A2. Calculate the normalized Euclidean distance between two detection result sets; A3. Traverse the two detection result sets, Γ d is the set threshold value, if Ψ i,j ≤Γ d , perform perception data fusion and put it into set S F ; Otherwise, put it directly into the set S F .

7. The collaborative detection method in the UAV synaesthesia integrated network as claimed in claim 6, It is characterized in that The specific steps of data fusion are as follows: A31, state prediction: x n|n-1 =g(x n-1 )+ω n ; A32. Forecast covariance matrix calculation: A33, Kalman filter gain calculation: A34, Status update: x n =x n|n-1 +K n (y n -h(x n|n-1 )).

8. The collaborative detection method in the UAV synaesthesia integrated network as claimed in claim 1, It is characterized in that The drone needs to travel from the starting point q within the specified time. I Fly to the end point F , the maximum flight speed is V max , the maximum moving distance between two time slots is D m =V max Δt; flight limit is q u,c [1] = q I ,q u,c [N] = q F , and ||q u,c [n+1]-q u,c [n]||≤D m .

9. The collaborative detection method in the UAV synaesthesia integrated network as claimed in claim 1, It is characterized in that Joint optimization of the drone trajectory and beamforming algorithm includes the following steps: A1. Initialize the beamforming vector matrix W and the UAV flight trajectory Q, and set the number of iterations l = 0; A2. For a given UAV flight trajectory Q, the original optimization problem is transformed into an optimization problem with only the beamforming vector matrix W as the optimization variable. Using the semi-positive relaxation technique, the non-convex problem is transformed into a convex optimization problem, and the convex optimization toolkit can be used to solve the optimal beamforming vector matrix W. l+1 ; A3. For a given beamforming vector matrix W l+1 The original optimization problem is transformed into an optimization problem with only the UAV flight trajectory Q as the optimization variable. The continuous convex approximation technology is used to transform the non-convex problem into a convex optimization problem, so that the optimal power allocation matrix Q can be solved using the convex optimization toolkit. l+1 ; A4. Using the already obtained beamforming vector matrix W l+1 and the drone flight trajectory Q l+1 Obtain the maximum communication rate achievable in the current iteration make γ 0 is the set threshold value, if γ≥γ 0 , update the number of iterations l=l+1, and return to step A2; otherwise, end directly.

10. A cooperative detection system in a UAV synergy integrated network using the cooperative detection method in a UAV synergy integrated network according to any one of claims 1 to 9, It is characterized in that Including detection drones and base stations; The base station and the detection drone jointly perform the detection task; In the collaborative detection scenario, a model for transmitting and receiving signals for UAV communication and perception is established; According to the maximum likelihood criterion, the detection sets of the base station and the UAV are fused with the EKF algorithm for perception data; Mathematically model the problem of maximizing the achievable communication rate; The communication performance is enhanced by jointly optimizing the UAV trajectory and the beamforming algorithm, solving the optimization problem of maximizing the achievable communication rate.

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