98 results about "Coordinate descent" patented technology

The invention relates to the technical field of marketing operation based on big data analysis, and discloses a marketing operation method based on big data analysis. Preprocessing the user-commodity interaction data to construct an initial interaction matrix; constructing a co-occurrence counting matrix; automatically calculating a sparse penalty coefficient and a convergence threshold; setting an optimization objective function and a soft threshold function; initializing a projection matrix and iteratively solving a sparse projection matrix through a coordinate descent algorithm; constructing a commodity sparse co-occurrence adjacency set; and generating a personalized recommendation result in combination with user historical behaviors. The analysis quality is improved through data preprocessing and denoising, commodity potential correlation is mined through co-occurrence modeling, adaptability is enhanced through automatic parameter adjustment, a projection matrix optimization result is sparse and interpretable, the structure sensing capacity of recommendation is improved through an adjacent structure, and finally high-precision and high-correlation recommendation is achieved. The structure definition, the calculation efficiency and the model generalization ability are excellent, and an efficient and stable personalized marketing recommendation system is constructed.

The invention provides a communication auxiliary sensing system and method based on an intelligent super surface (RIS). The communication auxiliary sensing system comprises a multi-antenna base station, a multi-user terminal and an active RIS. According to the system, an uplink pilot frequency and a data signal are jointly utilized, an expectation maximization (EM) algorithm is adopted to iteratively recover equivalent channel state information (CSI), and a Cramer-Rao bound (CRB) of sensing performance is deduced. Combined optimization of user precoding and RIS reflection coefficients is innovatively proposed under the uncertainty of CSI, so as to minimize CRB and guarantee user communication rate at the same time. An original problem is converted into a processable form by establishing a traversal MIMO spectrum efficiency compact lower bound, and efficient optimization is realized by combining a WMMSE method with a block coordinate descent (BCD) frame. According to the scheme, the sensing precision and the spectrum efficiency are remarkably improved, the method is suitable for an active RIS-assisted MU-MIMO network, and the technology can also be expanded to a traditional RIS-free system.

The invention provides an unmanned aerial vehicle covert communication method and system supported by a movable antenna, and the method comprises the steps: calculating the maximum communication rate of a legal user under the support of the movable antenna according to a communication scene of the movable antenna; according to a movable antenna communication scene, calculating a minimum detection error rate under multi-illegal-guard cooperative detection; based on the maximum communication rate and the minimum detection error rate, a joint optimization problem of time slot segmentation, beam forming, unmanned aerial vehicle trajectory and movable antenna position is established, and the optimization objective is to maximize the minimum throughput of legal users under the condition of satisfying covert communication constraints; an iterative algorithm based on block coordinate descent and continuous convex approximation is adopted to obtain optimized time slot segmentation, beam forming, unmanned aerial vehicle trajectory and movable antenna position parameters, and the parameters are applied to covert communication of the unmanned aerial vehicle. According to the method provided by the invention, the concealment requirement of unmanned aerial vehicle communication can be well met, and the network security is improved.

The invention provides a single-bit digital beam forming method and device, a terminal and a medium, and relates to the technical field of radar detection and wireless communication, and the method comprises the steps: constructing a target single-bit low-rank matrix recovery model of a uniform linear array comprising a plurality of sensors, and an optimization problem of the target single-bit low-rank matrix recovery model, and converting the target single-bit low-rank matrix recovery model into a processable problem, solving and determining a low-rank matrix by using a near-end block coordinate descent algorithm and a gradient descent algorithm, calculating an estimated covariance matrix of a target and an interference signal based on the matrix, performing eigendecomposition to calculate an interference subspace, and modeling a single-bit digital beam forming problem according to a base vector of the interference subspace to obtain a single-bit digital beam forming matrix. And calculating a closed-form solution based on the principle that the optimal solution is located in the orthogonal complement space of the interference subspace. According to the method, the resolution requirement of the analog-to-digital converter can be reduced by fully utilizing the single-bit quantization characteristic, and the beamforming performance of single-bit digital beamforming is close to that of high-bit digital beamforming when the hardware complexity and the power consumption are reduced.

The application discloses a sensing-integrated waveform and unmanned aerial vehicle (UAV) trajectory design method for realizing secure mobile edge computing, and aims at the case that potential eavesdroppers exist in a mobile edge computing network architecture in which multiple ground users perform uplink information transmission to a UAV, optimizes and designs a sensing-integrated waveform and a UAV trajectory with the minimum total user energy consumption as the target. First, an optimization problem conforming to the scene is constructed, then the original optimization problem is decoupled into sub-problems about the sensing-integrated waveform and the UAV flight trajectory based on a Block Coordinate Descent (BCD) method, and a convex approximation fitting is performed on each non-convex sub-problem, finally, an iterative algorithm is adopted to solve the problem, so that a global suboptimal solution of the whole optimization problem and optimal sensing-integrated waveform and UAV flight trajectory are found.

The invention discloses an implementation method of a sub-connection active STAR-RIS assisted secure communication system. A base station, a sub-connection active STAR-RIS, a legal user and an eavesdropping user are included. According to the invention, M units of STAR-RIS are grouped into L sub-arrays, and an amplifier is shared to reduce power consumption and cost; with maximization of system secrecy energy efficiency as a target, base station beam forming, STAR-RIS reflection / transmission coefficients and amplifier amplification coefficients are jointly optimized; and solving the non-convex optimization problem by adopting an efficient algorithm based on block coordinate descent. According to the invention, the hardware overhead and the system power consumption are effectively reduced through the sub-connection structure, meanwhile, the secure transmission efficiency of the system is remarkably improved through joint optimization, multiplicative fading is overcome, and an efficient and reliable solution can be provided for 6G secure communication.

The present invention discloses a single-bit image restoration method, device, terminal, and medium, relating to the field of image processing. The method employs a non-zero quantization threshold to single-bit quantize an image to be restored, determining an initial image quantization model; employs a smooth hyperbolic tangent function to replace the sign function in the initial image quantization model, and performs low-rank matrix decomposition on the image to be restored, determining a target image quantization model; determines an initial optimization model based on a truncated least squares function and the target image quantization model; employs a semi-quadratic minimization method to transform the initial optimization model and determine a target optimization model; employs a proximal block coordinate descent algorithm to solve the target optimization model to obtain low-dimensional submatrices and a noise matrix; and utilizes the low-dimensional submatrices to determine a reconstructed image. This method effectively addresses the problem that prior art cannot effectively suppress quantization noise, thereby affecting image restoration accuracy.

The invention relates to a dynamic self-adaptive communication, sensing and calculation integrated method for an air-space-ground integrated network. The method comprises the following steps: S1, constructing an air-space-ground integrated network model fusing a low earth orbit satellite, an unmanned aerial vehicle and ground users / edge nodes; s2, establishing a cross-layer communication-perception-calculation integrated joint optimization model, and respectively forming two modes of binary unloading and partial unloading according to whether a task can be segmented or not; s3, for a binary unloading mode, realizing distributed solution and generating an unloading decision and a beam / power / bandwidth / computing resource scheme by using a dynamic adaptive prediction alternating direction multiplier (DAP-ADMM); and S4, aiming at a partial unloading mode, adopting a weighted minimum mean square error alternative optimization method, introducing a receiving equalizer, error weight and other auxiliary variables, executing block coordinate descent iteration, and outputting an unloading proportion, power distribution and a beam forming result. According to the DA-ISCC architecture and the optimization algorithm thereof provided by the invention, cross-domain calculation unloading with low time delay, high throughput and low overhead can be realized in a high-dynamic space-air-ground integrated network environment, and the DA-ISCC architecture and the optimization algorithm thereof can be widely applied to emergency communication, low-altitude logistics, ocean monitoring and other scenes.

The application discloses a kind of computing unloading and resource allocation method under relay auxiliary MEC architecture, belong to wireless communication and computing resource management technical field.The application provides multiple task processing modes by introducing relay device, including local execution, relay unloading and edge computing, in order to minimize execution cost from user angle, joint optimization method of task division strategy, computing resource allocation strategy, relay selection strategy and uplink spectrum allocation strategy is proposed, can reduce network congestion while guaranteeing quality of service, improve user experience;In order to solve joint optimization problem, using block coordinate descent, reconstruction linearization technology and convex optimization method, with fast convergence speed and lower computational complexity, and relay auxiliary MEC architecture and optimization method can adapt to different types of intelligent devices and application scenarios, with wide applicability and flexibility, meet the needs of future mobile edge computing development.

The invention discloses a block coordinate descent and matching theory-based low-orbit satellite hopping beam calculation unloading method, and belongs to the field of low-orbit satellite communication. The method comprises the following steps: firstly, a core network counts the reported service demand of each user, considers the lower limit of time delay satisfaction, and constructs a hopping beam and resource scheduling initial model; and then, based on a matching theory, carrying out hopping beam pattern initialization, and obtaining an optimal solution of unloading-resource scheduling by using a block coordinate descent algorithm, thereby obtaining the delay satisfaction degree of each sink node corresponding to the hopping beam pattern. And then, the core network randomly selects satellites and two different time slots to execute exchange matching, updates a beam hopping pattern and unloading-resource scheduling, selects and rejects the updated beam hopping pattern according to the time delay satisfaction degree, and iteratively updates repeatedly until the number of times is reached. And sending the finally reserved hopping beam pattern and the corresponding optimal resource scheduling strategy to each user / satellite node, and carrying out beam lightening and unloading strategy execution. According to the invention, the management complexity is effectively reduced.

This invention discloses a task offloading and resource allocation method under D2D-assisted MEC, belonging to the field of wireless communication and computing resource management technology. The method introduces service-oriented devices assisted by terminal direct transmission communication technology and rationally selects task processing modes, achieving efficient resource utilization and avoiding resource waste. By jointly optimizing computation offloading, D2D selection, computing resource allocation, and spectrum resource allocation, it can both guarantee service quality and reduce network congestion, thereby improving user experience. The two-stage iterative algorithm of this invention combines block coordinate descent, reconstruction linearization technology, and convex optimization methods, effectively solving non-convex optimization problems, and has a fast convergence speed and low computational complexity. Furthermore, the D2D-assisted MEC architecture and optimization method are applicable to different types of intelligent devices and diverse application scenarios, possessing broad applicability and flexibility, and can meet the needs of future mobile edge computing development.

The present invention proposes a control method, device, electronic device, and storage medium for a piezoelectric nanopositioning system based on rolling optimization, belonging to the field of precision electromechanical control technology. The method comprises: establishing a high-order transfer function model of a piezoelectric nanopositioning system and identifying the model parameters through a frequency sweep method; secondly, converting the model into a high-dimensional state space model, designing optimal control indicators, constructing a finite-time rolling optimization control problem, and improving the tracking capability of the piezoelectric nanopositioning system; finally, converting the rolling optimization control problem into a quadratic programming problem, and designing a coordinate descent method to accelerate the online calculation of the optimal control and ensure that the optimal control input satisfies the state and input constraints of the system. The method of the present invention can solve the problems of existing piezoelectric nanopositioning systems, such as the low order and limited expression capabilities of mathematical models, the narrow bandwidth of control methods, and the difficulty of digital implementation, thereby improving the ability of piezoelectric nanopositioning systems to track high-frequency reference instructions and being suitable for engineering applications.

The invention provides a mobile cell resource minimization slice resource configuration method and system, and belongs to the technical field of train communication, and the method comprises the steps: building a train mobile network model based on a relay system, and constructing a resource slice framework; establishing a communication and service quality assurance model for a passenger on-demand service and a beyond-visual-range service by considering a downlink bearing demand of a train intelligent service; establishing an optimization problem taking bandwidth allocation and terminal preemptive grouping as target variables; simplifying an original problem by using a convex optimization theory, and decomposing the original problem into a resource block allocation sub-problem and a mini time slot preemption sub-problem; and iteratively solving the two sub-problems by using a resource block allocation and preemption efficient algorithm based on block coordinate descent to obtain an optimal resource allocation strategy. Under the condition of non-ideal train-ground link transmission caused by the high-speed movement characteristic of a train, the deterministic transmission requirement of heterogeneous services is ensured, and the joint design of a preemption-based slice deployment strategy and a resource allocation algorithm is realized.

The application belongs to the technical field of unmanned aerial vehicle communication sensing, and specifically discloses a method for jointly scheduling communication and sensing resources of an unmanned aerial vehicle in a covert communication scenario, which comprises the following steps: establishing a communication performance model and a radar sensing performance model; combining covert communication constraints and no-fly zone avoidance constraints to build a joint optimization problem of communication and sensing resources; solving the closed-form optimal solution of sensing power and communication power, and reducing variables of the joint optimization problem; introducing a segmented hovering flight structure to discretize the continuous flight trajectory of the unmanned aerial vehicle, and combining the closed-form optimal solution to convert the joint optimization problem into a discrete optimization problem; using a block coordinate descent algorithm to alternately and iteratively solve the discrete optimization problem, and outputting an optimal user association strategy, an unmanned aerial vehicle hovering time and a continuous flight trajectory of the unmanned aerial vehicle. The application can improve the covert communication capability and comprehensive sensing accuracy of the unmanned aerial vehicle under the premise of meeting the covert communication demand and physical no-fly zone constraints.

This invention discloses an energy efficiency optimization method for a hybrid RIS-assisted UAV mobile edge computing system, comprising: establishing a system model including a ground terminal, a fixed RIS, and a UAV-mounted RIS; configuring system parameters; defining offloading decisions; determining the total offloading workload of the ground terminal and the total system energy consumption, and defining system energy efficiency; constructing an optimization problem aimed at maximizing system energy efficiency, jointly optimizing the ground terminal offloading decisions, etc., and simplifying the optimization problem using the Tinkerbach method; comparing the offloading rates of the ground terminal offloading tasks via the fixed RIS and the UAV-mounted RIS, and solving the offloading decisions; using a semidefinite relaxation method and introducing a penalty function to solve the beamforming matrix of the fixed RIS and the UAV-mounted RIS, solving the time slot allocation, and using continuous relaxation to solve the UAV trajectory; and iterating the offloading decisions continuously using the block coordinate descent method until the optimal solution and the maximum system energy efficiency are obtained.

This application discloses a block coordinate descent joint spatiotemporal calibration method and system, belonging to the field of sensor fusion and calibration technology. The method involves acquiring camera image sequences and inertial measurement unit (IMU) data; performing a first-stage coarse temporal alignment, estimating the temporal mapping parameters between the camera and IMU based on the camera image sequences and IMU data; performing a second-stage spatiotemporal joint optimization, jointly optimizing spatial extrinsic parameters, camera intrinsic parameters, and IMU data under the constraints of the temporal mapping parameters; iterating alternately between the first-stage coarse temporal alignment and the second-stage spatiotemporal joint optimization until a preset convergence condition is met; and outputting calibration results, including spatial extrinsic parameters, temporal mapping parameters, a noise model, and a calibration quality report. The method employs an alternating strategy of first performing coarse temporal alignment and then optimizing spatial extrinsic parameters, significantly improving the algorithm's convergence stability and calibration reliability.

The invention relates to a method for maximizing the average secrecy throughput in a short packet communication system assisted by an unmanned aerial vehicle (UAV). The method comprises the following steps of: firstly, selecting a short packet communication system; the method maximizes the average confidential throughput of all users by jointly optimizing user scheduling, transmit power, transport block length, and UAV trajectory. Firstly, an original problem is converted into four sub-problems of user scheduling optimization, power distribution optimization, block length selection optimization and unmanned aerial vehicle trajectory optimization. In order to solve the non-convex subproblem of unmanned aerial vehicle trajectory optimization, an improved particle swarm optimization (PSO) algorithm is adopted to search for an optimal or suboptimal UAV flight path. According to the method, under the optimization framework of overall block coordinate descent (BCD), other sub-problems are solved by means of methods such as continuous convex approximation (SCA), and the like, and therefore, the method can be used for solving the sub-problems of the block coordinate descent (BCD) of the block coordinate descent (BCD) of the block coordinate descent (BCD) of the block coordinate descent (BCD) of the block coordinate descent (BCD) of the block coordinate descent (BCD) of the block coordinate descent (BCD) of the block coordinate descent (BCD) of the block coordinate descent (BCD). According to the method provided by the invention, the communication safety performance of all users in the UAV-assisted short packet communication system can be improved, and a new design thought and optimization method are provided for the application of the UAV in a time delay sensitive wireless communication scene.

The invention discloses a trajectory and resource allocation optimization method and system in an unmanned aerial vehicle auxiliary cache system, and belongs to the technical field of wireless communication. The trajectory and resource allocation optimization method in the unmanned aerial vehicle auxiliary cache system comprises the steps of constructing the unmanned aerial vehicle auxiliary cache system, deriving a relationship between a user request time delay and an unmanned aerial vehicle hovering position, cache and trajectory based on unmanned aerial vehicle coverage radius and cache capacity constraints, establishing a user average request time delay minimization optimization problem, and optimizing the trajectory and resource allocation in the unmanned aerial vehicle auxiliary cache system. The optimization problem is decomposed into a user clustering sub-problem, a caching strategy sub-problem, a trajectory optimization sub-problem and a bandwidth power joint optimization sub-problem; and a neighbor clustering algorithm, a dynamic programming algorithm, an ant colony algorithm and a block coordinate descent algorithm are utilized to solve a user clustering sub-problem, a cache strategy sub-problem, a trajectory optimization sub-problem and a bandwidth power joint optimization sub-problem respectively, and finally an optimal user clustering, a cache placement strategy, an unmanned aerial vehicle flight trajectory and a bandwidth power distribution result are determined.

The present invention relates to the field of wireless power supply edge computing and task scheduling technology, and specifically to a resource allocation method for a wireless power supply edge computing network. First, heterogeneous access points, mobile terminals, time-varying channels, and heterogeneous tasks are comprehensively considered, and corresponding system models, equipment and service models, and task model calculation models are established; then, a selection algorithm based on hybrid delay tasks is designed by comprehensively considering various factors affecting computing performance; finally, considering the network model characteristics of the wireless power supply edge computing network, a coordinate descent algorithm based on one-dimensional time variable binary search is used to solve the mobile terminal's offloading decision and system time resource allocation problems. The present invention can effectively make heterogeneous access point selection, offloading decision-making, and time resource allocation schemes under time-varying channels, thereby improving the overall computing efficiency of the system.

This invention discloses a resource optimization method, system, and storage medium for a STAR-RIS-assisted UAV communication system. The method includes establishing a STAR-RIS-assisted UAV communication system model; constructing an objective optimization problem, aiming to maximize the system's total transmission rate under constraints such as UAV transmit power, spatial multiplexing, user scheduling binary constraints, STAR-RIS energy conservation constraints, and phase shift orthogonality constraints, and optimizing user scheduling, active beamforming, and STAR-RIS transmission and reflection coefficients; employing an alternating optimization framework to decompose the objective optimization problem into user scheduling sub-problems and active beamforming and STAR-RIS configuration sub-problems; using a penalized successive convex approximation method to solve the user scheduling sub-problem; and using a penalized dual decomposition method combined with block coordinate descent to solve the active beamforming and STAR-RIS configuration sub-problems. The method of this invention can significantly improve the overall system transmission rate while suppressing multi-user interference, meeting the high-bandwidth service requirements of surveillance video and other applications.

The invention belongs to the technical field of array signal high-precision positioning, and particularly relates to a rapid high-precision near-field positioning method suitable for non-uniform noise. The invention provides a near-field positioning method based on joint symmetric matrix decomposition and noise power estimation aiming at the problems that the positioning precision is reduced, the calculation complexity is high and the like in a non-uniform noise environment in the existing near-field positioning method, and the method adopts rapid inexact block coordinate descent to carry out alternate optimization. A closed-form solution exists in each iteration, analysis and solution can be carried out without line search, and the calculation complexity is reduced. The method breaks through the performance bottleneck of a traditional near-field source positioning method under non-uniform noise, has the advantages of being high in positioning precision, low in calculation complexity, high in environmental adaptability and the like, is more excellent in performance in a large-size antenna array scene, and can be applied to near-field positioning systems of radar, sonar, communication and the like.

The application relates to the technical field of communication, and specifically provides a reconfigurable intelligent surface (RIS) assisted ocean communication method and system based on LR-BCD, which comprises the following steps: constructing an RIS assisted ocean communication system model; constructing an ocean double-path channel model based on the ocean double-path propagation theory and in combination with a sea surface reflection coefficient; extracting low-rank characteristics of the channel by using singular value decomposition (SVD), projecting high-dimensional beamforming and phase shift optimization problems into a low-dimensional subspace; constructing a low-dimensional and rate maximization problem, and converting the problem into a convex subproblem by using a weighted minimum mean square error (WMMSE) criterion; alternately updating a low-dimensional precoding matrix and a phase shift vector by using a block coordinate descent (BCD) algorithm; and finally reconstructing the optimized low-dimensional variables back to a high-dimensional space and performing discretization and quantization. The application utilizes the sparsity of the ocean channel, significantly reduces the calculation complexity of large-scale RIS optimization through dimension reduction processing, and meanwhile, maintains the system and rate performance equivalent to that of a full-rank algorithm.

This invention discloses an active user detection method based on covariance-based nonorthogonal pilots for non-cellular massive MIMO systems, applicable to the communications field. A mathematical model for nonorthogonal random access based on non-cellular massive MIMO is established. By utilizing the block diagonal characteristics of the received signal covariance matrix, the active user detection problem is reconstructed into a maximum likelihood estimation problem. The coordinate descent algorithm is used to solve the reconstructed maximum likelihood estimation problem, and a parallel estimation algorithm is proposed to reduce computation time. This invention achieves a good trade-off between performance and complexity in active user detection, meeting the needs of scenarios with massive terminal random access and showing great application potential.

The application provides a distributed pose graph optimization method and device based on multi-level graph segmentation, which is used for communication between multiple robots, and the method comprises the following steps: performing a multi-level graph segmentation operation on an initial pose graph and constructing a distributed optimization problem; updating selected variable blocks by using a Riemannian gradient coordinate descent algorithm until an optimal solution meeting a condition is searched; and projecting the optimal solution to a feasible solution of the pose graph optimization by using singular value decomposition. Through the design of the distributed pose graph optimization method based on multi-level graph segmentation, the application constructs more balanced optimization sub-problems which are easier to process, reduces the communication cost in the multi-robot network, and improves the overall optimization performance of the multi-robot system.

This invention relates to a satellite-ground collaborative edge computing method for 6G space-ground integrated networks, comprising: constructing a system model for a mobile edge computing scenario; based on the system model, constructing a computation offloading model, a low Earth orbit satellite communication time model, and a channel model; based on the computation offloading model, the low Earth orbit satellite communication time model, and the channel model, constructing an offloading optimization problem with the goal of minimizing the weighted sum of total system latency and energy consumption; and iteratively solving the offloading optimization problem using a block coordinate descent algorithm, wherein the offloading optimization problem is decomposed into two sub-problems: task offloading optimization and resource allocation, which are solved separately. This invention solves the task allocation problem between satellites and base stations for multiple users by establishing a refined satellite-ground geometric model and resource scheduling model, minimizing the total system overhead while satisfying task deadlines and satellite service window constraints.

The present application belongs to the field of unmanned aerial vehicle communication technology, considering that when wireless sensors communicate with unmanned aerial vehicles, a mobile malicious node on the ground interferes with the communication of unmanned aerial vehicles, assuming that each sensor and unmanned aerial vehicle adopts frequency division multiplexing for communication, so there is no mutual interference between each sensor, and thus a communication model under malicious interference is obtained. Under this model, in order to maximize the throughput of unmanned aerial vehicles under malicious interference, an optimization problem is obtained. Since the optimization problem is a non-convex problem and has a large variable space, it is difficult to obtain the optimal solution. In order to solve this problem, the path discretization method is used to transform the original problem into a discrete equivalent problem, then the successive convex approximation and block coordinate descent techniques are applied to obtain a local optimal solution, and it is proved by the convex optimization principle that the local optimal solution can be approximately replaced by the global optimal solution, and finally the convergence and effectiveness of the algorithm are verified and shown by simulation results.

The present application relates to a kind of resource optimization method based on RIS auxiliary wireless energy-carrying backscattering communication system, belong to low-power Internet of Things technical field, the present application is aimed at the problem that transmission performance is poor caused by obstruction obstruction and channel perturbation, consider transmission time constraint, energy collection constraint, the maximum interference power constraint allowed by main receiver, channel uncertainty constraint and intelligent metasurface phase shift constraint, construct energy efficiency maximization resource management model based on bounded channel uncertainty, utilize worst criterion method and Cauchy inequality to convert original non-convex optimization problem containing parameter uncertainty into deterministic problem;By variable substitution method, continuous convex approximation method, block coordinate descent method and semi-positive definite programming method, the deterministic problem is converted into convex problem solution, the present application can effectively improve system total energy efficiency, while reducing main user interruption probability, improve signal propagation path.

The present application relates to an energy consumption minimization method and device for a multi-antenna unmanned aerial vehicles (UAVs) video transmission system assisted by intelligent reflecting surfaces (IRS), which utilizes a network model, a communication model, and an energy consumption model constructed by an aerial video surveillance system based on a UAV; a reconfigurable UAV-IRS-GU link is constructed by deploying an IRS to enhance the quality of air-to-ground communication; a model (P1) for minimizing the total energy consumption of the UAV is constructed based on the joint optimization of the UAV trajectory, flight time, beamforming vector, and IRS reflection angle; the model (P1) for minimizing the total energy consumption of the UAV is optimized through a first-stage algorithm and a second-stage algorithm to obtain a suboptimal solution, wherein the first-stage algorithm adopts a path discretization method, an alternating optimization method, and a continuous convex approximation algorithm (SCA) to minimize the propulsion energy consumption of the UAV; and the second-stage algorithm adopts a double-loop iterative algorithm based on a penalty-based block coordinate descent technique (P-BCD) to minimize the communication energy consumption of the UAV.

The application discloses a method for anti-clutter of airborne radar, which comprises the following steps: firstly, modeling the pulse-amplitude-phase time sequence hopping signal, establishing the airborne radar echo model, constructing the pulse-amplitude-phase agile waveform and the joint design of the anti-clutter optimization problem model of the receiving filter bank, and finally, quickly solving the pulse-amplitude-phase agile waveform and the receiving filter bank to realize the robust detection of multiple targets in the clutter background. The method can break through the technical bottleneck that the existing low PRF and medium-high PRF cannot simultaneously consider the range ambiguity and velocity ambiguity by modulating the pulse-amplitude-phase time sequence hopping signal, and based on the block coordinate descent and the efficient optimization method of quadratic transformation, the pulse-amplitude-phase agile waveform and the receiving filter bank are jointly designed to realize the robust detection of multiple targets in the clutter background. Compared with the existing pulse-amplitude-phase agile waveform technology, the method can realize the suppression of the range-velocity clutter and has the multiple target detection capability.

The present invention discloses a dual decomposition method for the sparse adversarial attack problem of image classification. First, the sparse adversarial attack problem of image classification is modeled as a large-scale multi-objective optimization problem, with each pixel of the image as a decision variable, and then a dual decomposition strategy is executed. The outer decomposition uses a sliding window to divide the large-scale decision variables into overlapping subsets of small-scale decision variables. Each time the sliding window slides, a small-scale multi-objective optimization problem is generated. Once the small-scale multi-objective optimization problem is generated, the inner decomposition immediately creates a set of global direction vectors and converts them into a set of single-objective optimization problems. Finally, a block coordinate descent strategy is used to optimize all single-objective problems, thereby enhancing the integrity and optimality of the solution during the optimization process. Experiments show that the proposed method has better results than other algorithms in solving large-scale multi-objective problems, and can find the best perturbation compared to other algorithms in sparse adversarial attack problems.