Non-cooperative Target Research Articles

Multi-UAV Escape Target Search: A Multi-Agent Reinforcement Learning Method

The multi-UAV target search problem is crucial in the field of autonomous Unmanned Aerial Vehicle (UAV) decision-making. The algorithm design of Multi-Agent Reinforcement Learning (MARL) methods has become integral to research on multi-UAV target search owing to its adaptability to the rapid online decision-making required by UAVs in complex, uncertain environments. In non-cooperative target search scenarios, targets may have the ability to escape. Target probability maps are used in many studies to characterize the likelihood of a target’s existence, guiding the UAV to efficiently explore the task area and locate the target more quickly. However, the escape behavior of the target causes the target probability map to deviate from the actual target’s position, thereby reducing its effectiveness in measuring the target’s probability of existence and diminishing the efficiency of the UAV search. This paper investigates the multi-UAV target search problem in scenarios involving static obstacles and dynamic escape targets, modeling the problem within the framework of decentralized partially observable Markov decision process. Based on this model, a spatio-temporal efficient exploration network and a global convolutional local ascent mechanism are proposed. Subsequently, we introduce a multi-UAV Escape Target Search algorithm based on MAPPO (ETS–MAPPO) for addressing the escape target search difficulty problem. Simulation results demonstrate that the ETS–MAPPO algorithm outperforms five classic MARL algorithms in terms of the number of target searches, area coverage rate, and other metrics.

Cross-Spectral Navigation with Sensor Handover for Enhanced Proximity Operations with Uncooperative Space Objects

Close-proximity operations play a crucial role in emerging mission concepts, such as Active Debris Removal or small celestial bodies exploration. When approaching a non-cooperative target, the increased risk of collisions and reduced reliance on ground intervention necessitate autonomous on-board relative pose (position and attitude) estimation. Although navigation strategies relying on monocular cameras which operate in the visible (VIS) spectrum have been extensively studied and tested in flight for navigation applications, their accuracy is heavily related to the target’s illumination conditions, thus limiting their applicability range. The novelty of the paper is the introduction of a thermal-infrared (TIR) camera to complement the VIS one to mitigate the aforementioned issues. The primary goal of this work is to evaluate the enhancement in navigation accuracy and robustness by performing VIS-TIR data fusion within an Extended Kalman Filter (EKF) and to assess the performance of such navigation strategy in challenging illumination scenarios. The proposed navigation architecture is tightly coupled, leveraging correspondences between a known uncooperative target and feature points extracted from multispectral images. Furthermore, handover from one camera to the other is introduced to enable seamlessly operations across both spectra while prioritizing the most significant measurement sources. The pipeline is tested on Tango spacecraft synthetically generated VIS and TIR images. A performance assessment is carried out through numerical simulations considering different illumination conditions. Our results demonstrate that a combined VIS-TIR navigation strategy effectively enhances operational robustness and flexibility compared to traditional VIS-only navigation chains.

Robust Truncated Statistics Constant False Alarm Rate Detection of UAVs Based on Neural Networks

With the rapid popularity of unmanned aerial vehicles (UAVs), airspace safety is facing tougher challenges, especially for the identification of non-cooperative target UAVs. As a vital approach for non-cooperative target identification, radar signal processing has attracted continuous and extensive attention and research. The constant false alarm rate (CFAR) detector is widely used in most current radar systems. However, the detection performance will sharply deteriorate in complex and dynamical environments. In this paper, a novel truncated statistics- and neural network-based CFAR (TSNN-CFAR) algorithm is developed. Specifically, we adopt a right truncated Rayleigh distribution model combined with the characteristics of pattern recognition using a neural network. In the simulation environments of four different backgrounds, the proposed algorithm does not need guard cells and outperforms the traditional mean level (ML) and ordered statistics (OS) CFAR algorithms. Especially in high-density target and clutter edge environments, since utilizing 19 statistics obtained from the numerical calculation of two reference windows as the input characteristics, the TSNN-CFAR algorithm has the best adaptive decision ability, accurate background clutter modeling, stable false alarm regulation property and superior detection performance.

A Systematic Review of Ship Wake Detection Methods in Satellite Imagery

The field of maritime surveillance is one of great strategical importance from the point of view of both civil and military applications. The growing availability of spaceborne imagery makes it a great tool for ship detection, especially when paired with information from the automatic identification system (AIS). However, small vessels can be challenging targets for spaceborne sensors without relatively high resolution. Moreover, when faced with non-cooperative targets, hull detection alone is insufficient for obtaining critical information like target speed and heading. The wakes generated by the movement of ships can be used to solve both of these issues. Several interesting solutions have been developed over the years, based on both traditional and learning-based methodologies. This review aims to provide the first thorough overview of ship wake detection solutions, highlighting the key ideas behind traditional applications, then covering more innovative applications based on deep learning (DL), to serve as a solid starting point for present and future researchers interested in the field.

Pose-Constrained Control of Proximity Maneuvering for Tracking and Observing Noncooperative Targets with Unknown Acceleration

This paper proposes a pose control scheme of for proximity maneuvering for tracking and observing noncooperative targets with unknown acceleration, which is an important prerequisite for on-orbit operations in space. It mainly consists of a finite-time extended state observer and constraint processing procedures. Firstly, relative pose-coupled kinematics and dynamics models with unknown integrated disturbances are established based on dual quaternion representations. Then, a finite-time extended state observer is designed using the super-twisting algorithm to estimate the integrated disturbances. Both observation field of view and collision avoidance pose-constrained models are constructed to ensure that the service spacecraft continuously and safely observes the target during proximity maneuvering. And the constraint models are further incorporated into the design of artificial potential function with a unique minimum. After that, the proportional–derivative-like pose-constrained tracking control law is proposed based on the estimated disturbances and the gradient of the artificial potential function. Finally, the effectiveness of the control scheme is verified through numerical simulations.

Intention inference for space targets using deep convolutional neural network

Intention inference for space targets is crucial for space situational awareness. This paper introduces a rapid and precise method for recognizing the intentions of non-cooperative space targets using a deep convolutional neural network (CNN). By employing a relative orbital dynamics model, an analysis of relative motion was performed, resulting in the identification of 11 distinct motion intentions for space targets. This study also describes how to generate relative motion trajectory images to create a training set for intention inference, effectively converting the problem into one of image recognition and classification. Extensive simulations were carried out to fine-tune the network hyperparameters, and the results highlight the exceptional performance of the proposed CNN-based method, which achieved an accuracy of 99.682%. This method significantly enhances recognition accuracy over other neural network-based methods for space objects and offers considerable potential for applications like spacecraft collision avoidance and strategic maneuvers among space targets.

Multiple Mobile Robots Planning Framework for Herding Non-Cooperative Target

Multiple Mobile Robots Planning Framework for Herding Non-Cooperative Target

Rotational Motion Compensation for ISAR Imaging Based on Minimizing the Residual Norm

In inverse synthetic aperture radar (ISAR) systems, image quality often suffers from the non-uniform rotation of non-cooperative targets. Rotational motion compensation (RMC) is necessary to perform refocused ISAR imaging via estimated rotational motion parameters. However, estimation errors tend to accumulate with the estimated processes, deteriorating the image quality. A novel RMC algorithm is proposed in this study to mitigate the impact of cumulative errors. The proposed method uses an iterative approach based on a novel criterion, i.e., the minimum residual norm of the signal phases, to estimate different rotational parameters independently to avoid the issue caused by cumulative errors. First, a refined inverse function combined with interpolation is proposed to perform the RMC procedure. Then, the rotation parameters are estimated using an iterative procedure designed to minimize the residual norm of the compensated signal phases. Finally, with the estimated parameters, RMC is performed on signals in all range bins, and focused images are obtained using the Fourier transform. Furthermore, this study utilizes simulated and real data to validate and evaluate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm shows dominance in the aspects of estimation accuracy, entropy values, and focusing characteristics.

A Space Non-Cooperative Target Recognition Method for Multi-Satellite Cooperative Observation Systems

A Space Non-Cooperative Target Recognition Method for Multi-Satellite Cooperative Observation Systems

Recognition of precession angles of non-cooperative targets based on deep learning with privileged information

With an increasing number of countries engaging in space activities worldwide, space non-cooperative target tracking and identification technology has become a prerequisite for safely conducting space operations. In order to the identify distant non-cooperative targets performing complex motions, this paper proposes a method to recognize difficult parameters by using easily available signal labels as privileged information, which is named Pi-FcResNet. The privileged information is connected to the output end of the network through a fully connected network and coupled with the linear layer of the main network. Through testing, our network achieved a recognition accuracy of 94.45 % for precession angles under high signal-to-noise ratio conditions. After incorporating the Convolutional Block Attention Module (CBAM), our method demonstrates fast fitting speed and robust performance. Testing on experimental data shows that, compared to traditional methods, our approach offers better stability and reproducibility in recognizing micro-motion parameters. This approach of using known information as additional information for deep learning networks holds great potential in the field of feature extraction for space non-cooperative targets undergoing complex motions.

Motion planning of free-floating space robots for tracking tumbling targets by two-axis matching via reinforcement learning

The increasing amount of space debris poses a persistent threat to the safety of spacecraft. These non-cooperative targets are generally in a tumbling state, which places stringent requirements on the precision of the end-effector pose for capture tasks. Moreover, there are still difficulties in exploring the orientation state space, making it impractical to track targets with complex tumbling states based on reinforcement learning. To track tumbling targets quickly with high accuracy, we propose Two-Axis Matching Path Tracking (TMPT) algorithm. Our algorithm reduces the complexity of orientation exploration and improves the convergence speed through two-axis matching and the self-guidance module. Furthermore, the training environment has been redesigned by adopting staged tumbling target environment, which expands the range of trackable targets. Comparative simulation results demonstrate the efficiency and robustness of the algorithm.

Non-cooperative target recognition and relative motion estimation with inertial measurement unit assistance

This study investigated the problems of non-cooperative target recognition and relative motion estimation during spacecraft rendezvous maneuvers. A structure integrating an Inertial Measurement Unit (IMU) and a visual camera was presented. The angular velocity output of the IMU was used to calculate the motion trajectories of star points in multiple image frames, which can highlight the motion of non-cooperative targets with respect to the image background to improve the probability of target recognition. To solve the problem of target misidentification caused by new star points entering the field of view, a target-tracking link based on IMU prediction was introduced to track the position of the target in the image. Furthermore, a measurement model was constructed using the line-of-sight vector generated from target recognition, and the relative motion state was estimated using a Huber-based non-linear filter. Semi-physical and numerical simulations were performed to evaluate the effectiveness and efficiency of the proposed method.

Moving Real-Target Imaging of a Beam-Broaden ISAL Based on Orthogonal Polarization Receiver and Along-Track Interferometry

In response to the application requirement of wide-range high-resolution imaging of non-cooperative moving real targets by inverse synthetic-aperture ladar (ISAL), experiments were conducted on the depolarization effect of target materials, and the polarization selection of ISAL receiving and transmitting channels was discussed. Considering the impact of target depolarization and the demand for along-track interferometry, combined with beam-broaden and high-gain amplifiers, an ISAL system design method that can stably image multiple non-cooperative real targets has been proposed. Under the condition of broadening the transmitting and receiving beams to 3° in the elevation direction for non-cooperative moving vehicles, echo data with a duration of 1 s is obtained. The spatial correlation algorithm combined with along-track interferometry is used to estimate the vibration phase error. The sub-aperture Range-Doppler algorithm is used for imaging. The ISAL imaging results of the moving vehicle validated the high-resolution imaging ability of ISAL and its potential for stable imaging of non-cooperative moving real targets.

Reinforcement Learning-Based Pose Coordination Planning Capture Strategy for Space Non-Cooperative Targets

Reinforcement Learning-Based Pose Coordination Planning Capture Strategy for Space Non-Cooperative Targets

Ultrasensitive detection of remote acoustic vibrations at 300 m distance by optical feedback enhancement

Sensitive detection of remote vibrations at nanometer scale owns promising potential applications such as geological exploration, architecture, and public security. Nevertheless, how to detect remote vibration information with high sensitivity and anti-disturbance has become a major challenge. Reported current non-contact measurement methods are difficult to simultaneously possess characteristics of high light intensity sensitivity, long working distance, high vibration response sensitivity, and anti-disturbance of ambient light. Here, we propose a polarization-modulated laser frequency-shifted feedback interferometry method with the above characteristics, to obtain remote vibration information. The method can directly measure non-cooperative targets without the need for any cooperative markers. In each interference cycle, the energy as low as 2.3 photons can be effectively responded to, and the vibration amplitude sensitivity at 300 m can reach 0.72 nm/Hz1/2 at 1 kHz. This approach provides a strategy for the ultrasensitive detection of remote vibration that is immune to electromagnetic interference.

WDICD: A novel simulated dataset and structure-aware framework for semantic segmentation of spacecraft component

The identification and localization of non-cooperative target, especially for the components such as solar panel and docking ring, is crucial for missions involving the on-orbit space services, navigation, and positioning. However, on-orbit spacecraft images are difficult to obtain, and it requires substantial effort to label them with pixel-wise accuracy for semantic segmentation. To address the above problems, we construct a simulated Wide-Depth Illumination-Composite Dataset (WDICD) and introduces an automatic pixel-level semantic annotation method. Furthermore, this paper proposes a structure-aware framework for semantic segmentation of spacecraft component, which introduces a boundary-aware auxiliary task and constructs a boundary-distance-aware contrast loss based on batch-wise grouping. The related boundary-aware tasks alleviate the prediction error of semantic segmentation near the boundary. Experimental results demonstrate that the proposed method achieves SOTA mIoU results in spacecraft component segmentation against complex backgrounds on two datasets. Particularly, the test results on spaceborne device show that the proposed framework balances precision and efficiency without generating additional computational complexity.

Design and workspace analysis of a cable-driven space capture robot for noncooperative targets

Capturing noncooperative targets in space has garnered continuous research interest in aerospace applications. This study addresses the demands of large-scale, multifaceted activities and varied working conditions for space capture missions by designing a space capture robot composed of multiple cable-driven manipulators operating in parallel. First, single- and multi-segment cable-driven robot models were designed, and a geometric model was subsequently built. The optimal number of segments was determined by analysing the condition number of a Jacobian matrix using the Monte Carlo method. Subsequently, based on the constant-curvature assumption, a kinematic model of the cable-driven space capture robot was formulated, and capture methods for different capture targets were designed using the Monte Carlo method. Finally, an eight-segment cable-driven robot prototype was developed, and compliance and driving experiments were conducted. This robot exhibits promising application potential for space noncooperative target capture and can be feasibly manufactured using on-orbit 3D machining technology.

Motion Clutter Suppression for Non-Cooperative Target Identification Based on Frequency Correlation Dual-SVD Reconstruction.

Non-cooperative targets, such as birds and unmanned aerial vehicles (UAVs), are typical low-altitude, slow, and small (LSS) targets with low observability. Radar observations in such scenarios are often complicated by strong motion clutter originating from sources like airplanes and cars. Hence, distinguishing between birds and UAVs in environments with strong motion clutter is crucial for improving target monitoring performance and ensuring flight safety. To address the impact of strong motion clutter on discriminating between UAVs and birds, we propose a frequency correlation dual-SVD (singular value decomposition) reconstruction method. This method exploits the strong power and spectral correlation characteristics of motion clutter, contrasted with the weak scattering characteristics of bird and UAV targets, to effectively suppress clutter. Unlike traditional clutter suppression methods based on SVD, our method avoids residual clutter or target loss while preserving the micro-motion characteristics of the targets. Based on the distinct micro-motion characteristics of birds and UAVs, we extract two key features: the sum of normalized large eigenvalues of the target's micro-motion component and the energy entropy of the time-frequency spectrum of the radar echoes. Subsequently, the kernel fuzzy c-means algorithm is applied to classify bird and UAV targets. The effectiveness of our proposed method is validated through results using both simulation and experimental data.

Adaptive optimal concurrent control for detumbling space non-cooperative target via multipoint repeated contact

Space debris generally exhibits complex tumbling motions, and its direct capture may cause damage to space manipulator and base spacecraft. Current detumbling strategies typically require continuous contact collisions with the target and do not take into account actuator limitations. Thus, this paper presents an adaptive optimal concurrent control of space free-floating multi-fingered robot (SFMR) for multipoint repeated contact detumbling of non-cooperative targets. Firstly, the multipoint repeated intermittent contact model between the SFMR and the target is established. Further, an optimal admittance control that considers manipulator actuator limits and target motion bounds is formulated to generate a compliant detumbling trajectory. Through transforming the state and input inequality constraints into extended dynamical subsystems and saturation functions, respectively, the optimal control problem (OCP) is transformed into a readily solvable equality constraint problem. Moreover, an enhanced nonsingular terminal sliding mode (ENTSM) control with radial basis function neural network (RBFNN) compensation is presented in the presence of lumped uncertainties and external disturbances, which achieves rapid finite-time convergence and low-chattering. The simulation results show that the proposed method can reduce the velocity of the space target effectively without causing base spacecraft interference while achieving accurate trajectory tracking.

Vision-based relative position and attitude determination of non-cooperative spacecraft using a generative model architecture

A wide range of spacecraft operations in orbit relies on the precise and accurate estimation of the relative attitude and position of target objects. This requirement is especially critical during missions involving non-cooperative targets, where even minor errors can result in mission failure or catastrophic collisions. This paper presents an innovative approach that integrates a novel Point Cloud Reconstruction Generative Adversarial Network (PCR-GAN) with a Generalized Iterative Closest Point (GICP) algorithm to enhance pose estimation of known non-cooperative targets using stereo-camera data. Initially, a machine learning algorithm is employed to extract feature points from the stereo images of the target spacecraft. The PCR-GAN model then is used to improve the resolution and distribution of the point clouds generated, which are subsequently aligned using the GICP algorithm. Experimental results from a testbed implementation demonstrate that this architecture significantly enhances the accuracy of attitude and position estimation, thereby representing a potential tool for improving the efficiency and safety of proximity operations in space.