Cooperative Target Research Articles

Ultra-deep channel sand body target recognition method based on improved deep learning under UAV cluster

Abstract River sand bodies have complex and changeable characteristics and distribution. In order to improve the accuracy and efficiency of target recognition, this study proposes a target recognition method of ultra-deep river sand bodies with improved deep learning under unmanned aerial vehicle (UAV) cluster. By constructing the cooperative target allocation model of UAV group, it is ensured that the targets of ultra-deep and large-area river sand bodies are collected. The gradient histogram is used to extract the image characteristics of ultra-deep river sand body and enhance the target image of ultra-deep river sand body. Bi-directional long short-term memory (Bi-LSTM) network model is constructed by introducing bidirectional recurrent neural network (RNN) to improve deep learning. Bi-LSTM neural network is used to construct the target recognition model of ultra-deep river sand body and complete the target recognition. The experimental results show that this method can extract the target edge completely and recognize the image edge accurately, and the average recognition accuracy under different ambiguities is higher than 95. It is proved that this method has high accuracy in sand body feature extraction and classification and has great application potential in river sand body target recognition.

A Game Theory-Based Approach for Multiple UAVs Cooperative Target Defense

A Game Theory-Based Approach for Multiple UAVs Cooperative Target Defense

BET inhibitors drive Natural Killer activation in non-small cell lung cancer via BRD4 and SMAD3

Non-small-cell lung carcinoma (NSCLC) is the most common lung cancer and one of the pioneer tumors in which immunotherapy has radically changed patients’ outcomes. However, several issues are emerging and their implementation is required to optimize immunotherapy-based protocols. In this work, we investigate the ability of the Bromodomain and Extra-Terminal protein inhibitors (BETi) to stimulate a proficient anti-tumor immune response toward NSCLC. By using in vitro, ex-vivo, and in vivo models, we demonstrate that these epigenetic drugs specifically enhance Natural Killer (NK) cell cytotoxicity. BETi down-regulate a large set of NK inhibitory receptors, including several immune checkpoints (ICs), that are direct targets of the transcriptional cooperation between the BET protein BRD4 and the transcription factor SMAD3. Overall, BETi orchestrate an epigenetic reprogramming that leads to increased recognition of tumor cells and the killing ability of NK cells. Our results unveil the opportunity to exploit and repurpose these drugs in combination with immunotherapy.

Highly sensitive mid-infrared methane remote sensor using a deep neural network filter.

A novel mid-infrared methane remote sensor integrated on a movable platform based on a 3.291-µm interband cascade laser (ICL) and wavelength modulation spectroscopy (WMS) is proposed. A transmitting-receiving coaxial, visualized optical layout is employed to minimize laser energy loss. Using a hollow retro-reflector remotely deployed as a cooperative target, the atmospheric average methane concentration over a 100-meter optical range is measured with high sensitivity. A deep neural network (DNN) filter is used for second harmonic (2f) signal denoising to compensate for the performance shortcomings of conventional filtering. Allan deviation analysis indicated that after applying the DNN filter, the limit of detection (LOD) of methane was 86.62 ppb with an average time of 1 s, decreasing to 12.03 ppb with an average time of 229 s, which is a significant promotion compared to similar work reported. The high sensitivity and stability of the proposed sensor are shown through a 24-hour continuous monitoring experiment of atmospheric methane conducted outdoors, providing a new solution for high-sensitivity remote sensing of atmospheric methane.

Safety-Critical Cooperative Target Enclosing Control of Autonomous Surface Vehicles Based on Finite-Time Fuzzy Predictors and Input-to-State Safe High-Order Control Barrier Functions

Safety-Critical Cooperative Target Enclosing Control of Autonomous Surface Vehicles Based on Finite-Time Fuzzy Predictors and Input-to-State Safe High-Order Control Barrier Functions

Pose Measurement Method Based on Machine Vision and Novel Directional Target

Aiming at the shortcomings of the existing machine vision pose measurement technology, a pose measurement method based on monocular vision and a cooperative target is proposed. A planar target designed with circles and rings as the main body is dedicated to object pose measurement, and a feature point coordinate extraction and sorting algorithm is designed for this target to effectively extract image features on the target. The RANSAC algorithm and topology-based fitting for the intersection method are used to optimise data processing, further improving the accuracy of feature point coordinate extraction and ultimately achieving high-precision measurement of object poses. The experimental results show that the measurement accuracy of the roll angle perpendicular to the optical axis can reach 0.02°, and the repeatability can reach 0.0004° after removing the systematic error; the measurement accuracy of the pitch angle can reach 0.03°, and the repeatability can go to 0.002° after removing the systematic error. The measurement range of the pitch angle is [−30°, +30°]; the measurement range of the roll angle is [−179°, +179°]. The experimental results show that the system has high measurement accuracy and meets the requirements of high-precision measurement.

Cooperative target estimation of strap-down missiles considering random measurement delay

Aiming at the problem of cooperative target estimation for multiple strap-down missiles, this paper is concerned with developing a distributed cooperative estimation fusion algorithm considering the random transmission delay of observation data. As a stepping stone, the random measurement delay system with multiple strap-down missiles is transformed into a delay-free system with correlation noise in finite time intervals using the random observation delay model for strap-down missiles. Then, the distributed fusion technique without feedback is proposed based on the design of the best local filter for each strap-down sampling subsystem; To further enhance the robustness and stability of the cooperative target estimation system, the distributed fusion algorithm with feedback is suggested. Simulation in various cases and comparison studies are conducted to verify the effectiveness and robustness of the proposed scheme.

Configuration optimization method of cooperative target for pose estimation with monocular vision

Configuration optimization method of cooperative target for pose estimation with monocular vision

Attitude measurement of ultraclose-range spacecraft based on improved YOLOv5s and adaptive Hough circle extraction.

In order to fulfill the requirements for various operations in space, such as rendezvous, docking, and capturing, there is a pressing need to achieve ultraclose-range spacecraft pose measurement. This paper addresses the challenges of pose measurement under low-light conditions at ultraclose range by introducing a stereovision solution based on target detection and adaptive circle extraction. Initially, an improved target detection algorithm is employed to expedite feature object detection. Subsequently, an adaptive circle extraction algorithm is developed through analysis of camera imaging to surmount challenges related to feature extraction and potential feature loss in the space environment. This approach facilitates swift and accurate measurement of spacecraft at ultraclose range. The results showcase a 66.36% reduction in parameter count for the enhanced target detection algorithm compared with the prevalent YOLOv7_tiny algorithm. Additionally, the adaptive circle extraction algorithm demonstrates an 11.4% increase in cooperative target feature extraction precision compared with existing methods while maintaining requisite detection speed. Simulation experiments indicate that the real-time position measurement error for spacecraft at ultraclose range is less than 0.18mm, and angle measurement error is less than 0.05°. This presents a viable visual solution for spacecraft pose measurement at ultraclose range in low-light environments.

GNSS-Denied Joint Cooperative Terrain Navigation and Target Tracking Using Factor Graph Geometric Average Fusion

GNSS-Denied Joint Cooperative Terrain Navigation and Target Tracking Using Factor Graph Geometric Average Fusion

Bird Flocking Inspired Methods for Multi-UAV Cooperative Target Search

Bird Flocking Inspired Methods for Multi-UAV Cooperative Target Search

Retracted: Research on Mapping Error Control of Underground Space Mobile LiDAR Constrained by Cooperative Targets

Retracted: Research on Mapping Error Control of Underground Space Mobile LiDAR Constrained by Cooperative Targets

Deception Velocity-Based Method to Discriminate Physical Targets and Active False Targets in a Multistatic Radar System

Due to the silent operation of the receiver station in a multistatic radar system, it is difficult for the jammer to generate the cooperative active deception target for the multistatic radar system. Making use of the spatial diversity property, a data level fusion method is proposed to counter the active deception jamming in this paper. According to the spatial correlation difference in physical target and active false target motion states, the deception velocity of the physical target, which is obtained by the radial velocity of each receiver, obeys the Gaussian distribution with zero mean, and the one of the active false target obeys the Gaussian distribution with the mean being its true deception velocity. Based on this fact, the active false target and physical target are discriminated by the deception velocity testing. The proposed deception velocity-based (DVB) method can keep a constant misjudgment probability for physical targets and discriminate active false targets effectively, especially in large deception velocity cases. The simulation verifies the feasibility and validity of the proposed discrimination method. Moreover, the proposed method can be combined with the location information association method to enhance the ability to discriminate the range–velocity joint deception of false targets.

Cooperative target allocation for air-sea heterogeneous unmanned vehicles against saturation attacks

This paper proposes a cooperative target allocation method for efficiently utilizing air-sea heterogeneous unmanned vehicles to counter saturation attacks by low-cost UAVs. Specifically, a bilevel model that combines the advantages of heuristic algorithms and the coalition formation game (CFG) is constructed by integrating unmanned vehicles' motion and attack attributes. The upper layer model aims to utilize the advantages of the heuristic algorithm to generate a feasible target allocation scheme in a shorter time. A genetic algorithm constrained by inheritance conditions (GAC) is proposed to solve the problem of the low efficiency of traditional genetic algorithms in solving the upper model. The lower layer model is established based on a coalition formation game, which aims to continuously and stably optimize the target allocation scheme. A search method based on mobile players is designed to form a stable coalition structure faster when the number of unmanned vehicles is enormous. Simulation results show the feasibility and superiority of the algorithm proposed in this paper in dealing with allocation tasks of different complexity.

Cooperative Localization and Target Tracking With Data Exception Based on Classification

Cooperative Localization and Target Tracking With Data Exception Based on Classification

Reinforcement Learning-Based Cooperative Target Tracking Control of Unmanned Surface Vehicles Under Data Falsification Attacks

Reinforcement Learning-Based Cooperative Target Tracking Control of Unmanned Surface Vehicles Under Data Falsification Attacks

UAV Swarm Cooperative Target Search: A Multi-Agent Reinforcement Learning Approach

UAV Swarm Cooperative Target Search: A Multi-Agent Reinforcement Learning Approach

Multi-Robot Cooperative Target Search Based on Distributed Reinforcement Learning Method in 3D Dynamic Environments

Multi-Robot Cooperative Target Search Based on Distributed Reinforcement Learning Method in 3D Dynamic Environments

Cooperative Target Enclosing Control for Multiple Unmanned Surface Vehicles with Unknown Dynamics and Safety Assurance

Cooperative Target Enclosing Control for Multiple Unmanned Surface Vehicles with Unknown Dynamics and Safety Assurance

Scalable-MADDPG-Based Cooperative Target Invasion for a Multi-USV System.

This article concentrates on proposing a scalable deep reinforcement learning (DRL) method for a multiple unmanned surface vehicle (multi-USV) system to operate cooperative target invasion. The multi-USV system, which is made up of multiple invaders, needs to invade target areas in a specified time. A novel scalable reinforcement learning (RL) method called Scalable-MADDPG is proposed for the first time. In this method, the scale of the multi-USV system can be changed at any time without interrupting the training process. Then, to mitigate the policy oscillation after applying Scalable-MADDPG, a bi-directional long-short-term memory (Bi-LSTM) network is constructed. Moreover, an improved ϵ -greedy strategy is proposed to help balance the exploration and exploitation in RL. Furthermore, to enhance the robustness of the optimal policy, Ornstein-Uhlenbeck (OU) noise is added in this improved ϵ -greedy strategy during the training process. Finally, the scalable RL method is used to help the multi-USV system perform cooperative target invasion under complex marine environments. The effectiveness of Scalable-MADDPG is demonstrated through three experiments.