Multiple Ground Targets Research Articles

Distance-Based Multiple Noncooperative Ground Target Encirclement for Complex Environments

Distance-Based Multiple Noncooperative Ground Target Encirclement for Complex Environments

3D Trajectory Planning for Real-Time Image Acquisition in UAV-Assisted VR

Nowadays, unmanned aerial vehicles (UAVs), empowered with the capability of high-definition image transmission, are used to capture the rapidly changing physical environment by leveraging its high flexibility to reconstruct an immersive realistic virtual environment for metaverse users. In this paper, we consider a novel UAV-assisted image acquisition system where a UAV is dispatched to take off from an initial location to capture real-time images of multiple ground targets and then transfer the captured images back to the ground user for virtual environment reconstruction. We aim to minimize the time for the UAV to complete the image acquisition task by optimizing the three-dimensional UAV trajectory under the constraints of image quality, information causality and energy consumption. To this end, we first formulate the investigated scenario into a mixed integer optimization problem, which, however, is difficult to solve due to the infinite time-varying variables closely coupled with each other. Then, a three-stage progressive algorithm is proposed to obtain an efficient solution to the formulated mixed integer optimization problem, where the constraints of image quality, information causality and energy consumption can be sequentially satisfied. Finally, comprehensive performance evaluation is conducted to verify the effectiveness of the proposed three-stage progressive trajectory design algorithm, and the results show that the proposed algorithm significantly outperforms the benchmark schemes.

Multitrajectory Combination for Multiple Ground Target Observation by Maneuvering On-Orbit Satellites

Multitrajectory Combination for Multiple Ground Target Observation by Maneuvering On-Orbit Satellites

Image-Based Adaptive Staring Attitude Control for Multiple Ground Targets Using a Miniaturized Video Satellite

A miniaturized video satellite can observe the ground targets by recording real-time video clips in staring control mode and therefore obtains a unique advantage over traditional remote sensing techniques. To further extend the application of a video satellite, a strategy for simultaneously observing a group of ground targets is to be developed. To cope with the impacts of an uncalibrated camera on the pointing accuracy which can lead to the failure of a multi-target observation task, an adaptive attitude control method is to be exploited. Hence, to observe multiple ground targets using an onboard uncalibrated camera, this paper proposes an image-based adaptive staring attitude controller. First, a target-selection strategy is proposed to realize a more balanced staring observation of the target group. Second, an updating law is proposed to estimate the camera parameters according to the projection equations. At last, an adaptive staring controller based on the estimated parameters is formulated, so that the center of mass of the ground targets on the image can be controlled towards its desired location, which is normally the image center. The stability of the proposed staring controller is proved using Barbalat’s Lemma. The simulation results show that even though the camera parameters are uncertain, the adaptive control method effectively achieves the staring observation for multiple ground targets by keeping their midpoint at the image center.

Pigeon-inspired fuzzy multi-objective task allocation of unmanned aerial vehicles for multi-target tracking

In this paper, a pigeon-inspired fuzzy multi-objective optimization algorithm is proposed for task allocation of multiple unmanned aerial vehicles tracking multiple ground targets in urban environment. Firstly, a multi-objective integer programming of task allocation, involving minimum total flight distance, best task allocation balance and minimum completion time, is established. Secondly, fuzzy two-phase optimization based on the relaxed order of desirable satisfactory degrees is proposed to formulate mixed integer programming regarding the linguistic importance preference of objectives. Then, an adaptive pigeon-inspired algorithm combined with auction mechanism is proposed to solve the optimization model. The position of pigeon is defined as the bidding price given by unmanned aerial vehicle for target. To satisfy the constraints and avoid existence of inferior pigeons, the auction mechanism is designed to decode the pigeon position into a feasible task allocation scheme. Finally, by comparing with the conventional particle swarm optimization, simulations validate the effectiveness and efficiency of the proposed method.

Multitree Search for Multisatellite Responsiveness Scheduling Considering Orbital Maneuvering

Rapid and responsive earth observation satellites enable the evaluation of disaster risk, improve relief effectiveness, and reduce suffering and fatalities in the event of sudden disaster events.One promising method for responsive space satellites is orbital maneuvering. This paper describes a multi-tree search framework for multi-satellite responsiveness scheduling considering orbital maneuvering, where multiple ground targets are cooperatively observed by multiple observation satellites over a short period of time. Based on the traditional tree search, the proposed method constructs multiple trees and distributes all the targets to multiple satellites, allowing the observation sequence of single satellite to be optimized. To apply the tree search algorithm, a tree node collection (TNC) composed of the corresponding nodes in multiple trees is used to represent the state of each satellite. Before the expansion phase, one specific node in the TNC is determined for later expansion. A beam search is then used to optimize the observation sequence. Ground track adjustment techniques using impulse maneuver are considered for visiting a given target. The proposed framework achieves better performance than the previous best method in a typical multi-satellite responsiveness scheduling problem scenario.

Task scheduling of the collaborative aerial–ground system for the search and capture of multiple targets

Small unmanned rotorcraft (SUR) have been used widely in various civil applications since the opening of the low-altitude airspace. They enable public security maintenance missions via surveilling, searching, and tracking specific areas and targets. SUR functions can be further extended by complementing the ground vehicle and human. In this study, a new collaborative aerial–ground system is introduced to perform a search and capture (SAC) of multiple ground targets. The system comprises several SUR, cars, and humans. The targets primarily refer to criminals or terrorists who tend to escape from the aerial–ground system with holistic and scattered running modes, depending on different situations. A heuristic enumeration optimization (HEO) algorithm is proposed to maximize the probability of detecting the targets in the search space by determining the waypoints for the SUR and cars that can cover additional areas where the targets may occur. Moreover, a dynamic grouping-based task scheduling (DGBTS) algorithm is designed to allocate targets and calculate waypoints for SUR, cars, and humans. In the task allocation problem, the targets are assigned to the SUR by considering the balance of resources, and the quadrant each human is heading for is specified so as to surround the target from different directions. Accordingly, the waypoints are calculated in a distributed approach to highlight the different significance levels of SUR, cars, and humans in the capture phase. Simulation results reveal that the HEO algorithm can detect targets in fewer steps compared to the simulated annealing (SA) algorithm and the asynchronous planning strategy. Moreover, the DGBTS algorithm can deal with different situations in real applications. Additionally, the collaborative aerial–ground system exhibits a better search ability and comprehensive functionality compared to the car–human system and SUR–car systems.

Multitarget allocation strategy based on adaptive SA-PSO algorithm

AbstractWeapon target allocation (WTA) is an effective method to solve the battlefield fire optimisation problem, which plays an important role in intelligent automated decision-making. We researched the multitarget allocation problem to maximise the attack effectiveness when multiple interceptors cooperatively attack multiple ground targets. Firstly, an effective and reasonable fitness function is established, based on the situation between the interceptors and targets, by comprehensively considering the relative range, relative angle, speed, capture probability and radiation source matching performance and thoroughly evaluating them based on the advantage of the attack effectiveness. Secondly, the optimisation performance of the particle swarm optimisation (PSO) algorithm is adaptively improved. We propose an adaptive simulated annealing-particle swarm optimisation (SA-PSO) algorithm by introducing the simulated annealing algorithm into the adaptive PSO algorithm. The proposed algorithm can enhance the convergence speed and overcome the disadvantage of the PSO algorithm easily falling into a local extreme point. Finally, a simulation example is performed in a scenario where ten interceptors cooperate to attack eight ground targets; comparative experiments are conducted between the adaptive SA-PSO algorithm and PSO algorithm. The simulation results indicate that the proposed adaptive SA-PSO algorithm demonstrates great performance in convergence speed and global optimisation capabilities, and a maximised attack effectiveness can be guaranteed.

Design of Regional Coverage Low Earth Orbit (LEO) Constellation with Optimal Inclination

In this study, we describe an analytical process for designing a low Earth orbit constellation for discontinuous regional coverage, to be used for a surveillance and reconnaissance space mission. The objective of this study was to configure a satellite constellation that targeted multiple areas near the Korean Peninsula. The constellation design forms part of a discontinuous regional coverage problem with a minimum revisit time. We first introduced an optimal inclination search algorithm to calculate the orbital inclination that maximizes the geometrical coverage of single or multiple ground targets. The common ground track (CGT) constellation pattern with a repeating period of one nodal day was then used to construct the rest of the orbital elements of the constellation. Combining these results, we present an analytical design process that users can directly apply to their own situation. For Seoul, for example, 39.0° was determined as the optimal orbital inclination, and the maximum and average revisit times were 58.1 min and 27.9 min for a 20-satellite constellation, and 42.5 min and 19.7 min for a 30-satellite CGT constellation, respectively. This study also compares the revisit times of the proposed method with those of a traditional Walker-Delta constellation under three inclination conditions: optimal inclination, restricted inclination by launch trajectories from the Korean Peninsula, and inclination for the sun-synchronous orbit. A comparison showed that the CGT constellation had the shortest revisit times with a non-optimal inclination condition. The results of this analysis can serve as a reference for determining the appropriate constellation pattern for a given inclination condition.

Tracking Multiple Vehicles Constrained to a Road Network From a UAV with Sparse Visual Measurements.

Multiple-target tracking algorithms generally operate in the local frame of the sensor and have difficulty with track reallocation when targets move in and out of the sensor field-of-view. This poses a problem when an unmanned aerial vehicle (UAV) is tracking multiple ground targets on a road network larger than its field-of-view. To address this problem, we propose a Rao-Blackwellized Particle Filter (RBPF) to maintain individual target tracks and to perform probabilistic data association when the targets are constrained to a road network. This is particularly useful when a target leaves and then re-enters the UAV’s field-of-view. The RBPF is structured as a particle filter of particle filters. The top level filter handles data association and each of its particles maintains a bank of particle filters to handle target tracking. The tracking particle filters incorporate both positive and negative information when a measurement is received. We implement two path planning controllers, receding horizon and deep reinforcement learning, and compare their ability to improve the certainty for multiple target location estimates. The controllers prioritize paths that reduce each target’s entropy. In addition, we develop an algorithm that computes the upper bound on the filter’s performance, thus facilitating an estimate of the number of UAVs needed to achieve a desired performance threshold.

A Novel Framework for Multiple Ground Target Detection, Recognition and Inspection in Precision Agriculture Applications Using a UAV

Unmanned Aerial Vehicles (UAVs) have been recently used for different civilian applications such as remote sensing, search, and rescue (SAR), precision agriculture (PA), etc. A UAVs ability to sense and find targets remotely and, based on that, hover close to the target for a particular action makes it an ideal platform for the aforementioned applications. There has been extensive work carried out in the field of visual-based detection, navigation, and control, but the problem of detecting different ground targets and performing certain actions is still an open research area. This study proposes a novel framework for multiple target detection, recognition, and navigation of the UAV to the desired target and closely inspect it. This proposed framework can be deployed for accurately spot spraying in PA applications or SAR. The target detection and recognition in the framework are achieved through a computationally efficient Convolutional Neural Network (CNN) trained model, whereas the close inspection of the target is achieved through a PID-based tracking algorithm which ensures the UAV hover around the target for few seconds. The developed framework performed the desired objective in five stages employing Lawson’s control theory of sense, process, compare, decide and act. The target detection and recognition in the framework were validated with the field experiment, while the entire framework was validated through a variety of simulation flights conducted in Gazebo and PX4. The experiments’ results showed the versatility of the developed system to many complex missions where the targets are added or removed.

Cooperative Task Assignment of a Heterogeneous Multi-UAV System Using an Adaptive Genetic Algorithm

The cooperative multiple task assignment problem (CMTAP) is an NP-hard combinatorial optimization problem. In this paper, CMTAP is to allocate multiple heterogeneous fixed-wing UAVs to perform a suppression of enemy air defense (SEAD) mission on multiple stationary ground targets. To solve this problem, we study the adaptive genetic algorithm (AGA) under the assumptions of the heterogeneity of UAVs and task coupling constraints. Firstly, the multi-type gene chromosome encoding scheme is designed to generate feasible chromosomes that satisfy the heterogeneity of UAVs and task coupling constraints. Then, AGA introduces the Dubins car model to simulate the UAV path formation and derives the fitness value of each chromosome. In order to comply with the chromosome coding strategy of multi-type genes, we designed the corresponding crossover and mutation operators to generate feasible offspring populations. Especially, the proposed mutation operators with the state-transition scheme enhance the stochastic searching ability of the proposed algorithm. Last but not least, the proposed AGA dynamically adjusts the number of crossover and mutation populations to avoid the subjective selection of simulation parameters. The numerical simulations verify that the proposed AGA has a better optimization ability and convergence effect compared with the random search method, genetic algorithm, ant colony optimization method, and particle search optimization method. Therefore, the effectiveness of the proposed algorithm is proven.

A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS.

Small unmanned aerial systems (UASs) now have advanced waypoint-based navigation capabilities, which enable them to collect surveillance, wildlife ecology and air quality data in new ways. The ability to remotely sense and find a set of targets and descend and hover close to each target for an action is desirable in many applications, including inspection, search and rescue and spot spraying in agriculture. This paper proposes a robust framework for vision-based ground target finding and action using the high-level decision-making approach of Observe, Orient, Decide and Act (OODA). The proposed framework was implemented as a modular software system using the robotic operating system (ROS). The framework can be effectively deployed in different applications where single or multiple target detection and action is needed. The accuracy and precision of camera-based target position estimation from a low-cost UAS is not adequate for the task due to errors and uncertainties in low-cost sensors, sensor drift and target detection errors. External disturbances such as wind also pose further challenges. The implemented framework was tested using two different test cases. Overall, the results show that the proposed framework is robust to localization and target detection errors and able to perform the task.

Multiple Maneuvering Target Tracking Using a Single Unmanned Aerial Vehicle

This paper solves the problem of tracking multiple maneuvering ground targets using an unmanned aerial vehicle (UAV). The targets are widespread so that they may not be covered by the UAV’s detection range simultaneously. Such a strategy is applied in which, whenever it is unable to detect all the targets, the UAV temporarily covers only some of the targets and goes back to the lost targets later. A recursive Bayesian estimation algorithm is proposed to keep track of the lost targets; a guidance objective function is then formulated to minimize the tracking uncertainty. By integrating the proposed estimation and the guidance methods, an efficient closed-loop multitarget tracking algorithm is developed. The effectiveness of the proposed approach is demonstrated with illustrative numerical examples.

A Threat Assessment Algorithm for Multiple Ground Targets

A Threat Assessment Algorithm for Multiple Ground Targets

基于图像序列的地面慢动多目标识别与跟踪

基于图像序列的地面慢动多目标识别与跟踪

Tracking Multiple Ground Targets in Urban Environments Using Cooperating Unmanned Aerial Vehicles

A distributed approach is proposed for planning a cooperative tracking task for a team of heterogeneous unmanned aerial vehicles (UAVs) tracking multiple predictable ground targets in a known urban environment. The solution methodology involves finding visibility regions, from which the UAV can maintain line-of-sight to each target during the scenario, and restricted regions, in which a UAV cannot fly, due to the presence of buildings or other airspace limitations. These regions are then used to pose a combined task assignment and motion planning optimization problem, in which each UAV's cost function is associated with its location relative to the visibility and restricted regions, and the tracking performance of the other UAVs in the team. A distributed co-evolution genetic algorithm (CEGA) is derived for solving the optimization problem. The proposed solution is scalable, robust, and computationally parsimonious. The algorithm is centralized, implementing a distributed computation approach; thus, global information is used and the computational workload is divided between the team members. This enables the execution of the algorithm in relatively large teams of UAVs servicing a large number of targets. The viability of the algorithm is demonstrated in a Monte Carlo study, using a high fidelity simulation test-bed incorporating a visual database of an actual city.

Simultaneous Tracking of Multiple Ground Targets from a Multirotor Unmanned Aerial Vehicle

An algorithm for simultaneous tracking of multiple ground targets by an unmanned aerial vehicle is presented. The algorithm is specifically tailored toward multirotor vehicles, and it consists of a particle filter to predict target motion, a reference trajectory generator, and a finite-horizon model predictive controller for trajectory tracking. Two versions of the algorithm are proposed: for a vehicle equipped with a gimbaled camera, and for a vehicle equipped with a fixed camera. Furthermore, a target rejection algorithm is included to prune targets that inhibit accurate tracking of the majority of the target set. The tracking algorithm and multirotor vehicle dynamic model are first described, followed by example simulations for both the gimbaled and fixed-camera cases. Trade studies are presented, analyzing the effects of controller tuning parameters and camera field of view, as well as performance of the target rejection algorithm. In simulation experiments, real-world target data are used to improve simulation fidelity. Overall, results show that the algorithm is effective in capturing a set of ground targets within the field of view simultaneously when using either gimbaled or fixed-camera configurations, but performance is somewhat degraded in the fixed-camera case when target dynamics occur on timescales similar to tracking vehicle dynamics.

Multi-agent cooperative target search.

This paper addresses a vision-based cooperative search for multiple mobile ground targets by a group of unmanned aerial vehicles (UAVs) with limited sensing and communication capabilities. The airborne camera on each UAV has a limited field of view and its target discriminability varies as a function of altitude. First, by dividing the whole surveillance region into cells, a probability map can be formed for each UAV indicating the probability of target existence within each cell. Then, we propose a distributed probability map updating model which includes the fusion of measurement information, information sharing among neighboring agents, information decay and transmission due to environmental changes such as the target movement. Furthermore, we formulate the target search problem as a multi-agent cooperative coverage control problem by optimizing the collective coverage area and the detection performance. The proposed map updating model and the cooperative control scheme are distributed, i.e., assuming that each agent only communicates with its neighbors within its communication range. Finally, the effectiveness of the proposed algorithms is illustrated by simulation.

On the Atmospheric Correction of Antarctic Airborne Hyperspectral Data

The first airborne hyperspectral campaign in the Antarctic Peninsula region was carried out by the British Antarctic Survey and partners in February 2011. This paper presents an insight into the applicability of currently available radiative transfer modelling and atmospheric correction techniques for processing airborne hyperspectral data in this unique coastal Antarctic environment. Results from the Atmospheric and Topographic Correction version 4 (ATCOR-4) package reveal absolute reflectance values somewhat in line with laboratory measured spectra, with Root Mean Square Error (RMSE) values of 5% in the visible near infrared (0.4–1 µm) and 8% in the shortwave infrared (1–2.5 µm). Residual noise remains present due to the absorption by atmospheric gases and aerosols, but certain parts of the spectrum match laboratory measured features very well. This study demonstrates that commercially available packages for carrying out atmospheric correction are capable of correcting airborne hyperspectral data in the challenging environment present in Antarctica. However, it is anticipated that future results from atmospheric correction could be improved by measuring in situ atmospheric data to generate atmospheric profiles and aerosol models, or with the use of multiple ground targets for calibration and validation.