Cooperative Target Assignment Research Articles

Flexible Combinatorial-Bids-Based Auction for Cooperative Target Assignment of Unmanned Aerial Vehicles

Flexible Combinatorial-Bids-Based Auction for Cooperative Target Assignment of Unmanned Aerial Vehicles

Multi-UAV Cooperative Target Assignment Method Based on Reinforcement Learning

To overcome the problems of traditional distributed target allocation algorithms in terms of lack of target strategic priority, poor scalability, and robustness, this paper proposes a proximal strategy optimization algorithm that combines threat assessment and attention mechanism (TAPPO). Based on the distributed training framework, the algorithm integrates a threat assessment and dynamic attention strategy and designs a dynamic reward function based on the current hit rate of the drone and the missile benefit ratio to improve the algorithm’s exploration ability and scalability. Through an 8vs8 multi-UAV confrontation experiment in a digital twin simulation environment, the results show that the agent using the TAPPO algorithm for target allocation defeats the state machine with an 85% winning rate and is significantly better than other current mainstream target allocation algorithms, verifying the effectiveness of the algorithm.

An Evolutionary Game-Theoretic Approach to Unmanned Aerial Vehicle Network Target Assignment in Three-Dimensional Scenarios

Target assignment has been a hot topic of research in the academic and industrial communities for swarms of multiple unmanned aerial vehicle (multi-UAVs). Traditional methods mainly focus on cooperative target assignment in planes, and they ignore three-dimensional scenarios for the multi-UAV network target assignment problem. This paper proposes a method for target assignment in three-dimensional scenarios based on evolutionary game theory to achieve cooperative targeting for multi-UAVs, significantly improving operational efficiency and achieving maximum utility. Firstly, we construct an evolutionary game model including game participants, a tactical strategy space, a payoff matrix, and a strategy selection probability space. Then, a multi-level information fusion algorithm is designed to evaluate the overall attack effectiveness of multi-UAVs against multiple targets. The replicator equation is leveraged to obtain the evolutionarily stable strategy (ESS) and dynamically update the optimal strategy. Finally, a typical scenario analysis and an effectiveness experiment are carried out on the RflySim platform to analyze the calculation process and verify the effectiveness of the proposed method. The results show that the proposed method can effectively provide a target assignment solution for multi-UAVs.

UAV cooperative attack and route planning based on DPSO algorithm

For UAV cooperative attack and route planning problem, a cooperative attack allocation model is established in fully considering the attack cost and attack revenue. Considering the threat cost and path distance cost, a flight path planning model is also established. An improved particle swarm optimization algorithm called DPSO is proposed by discrete space mapping, which can reduce redundant search and speed up search. Based on the model and optimization algorithm, reasonable cooperative attack target assignment and route planning are worked out, and the optimal performance is achieved.

Cooperative target assignment and dwell allocation for multiple target tracking in phased array radar network

Motivated by networked anti-missile defense applications, a cooperative target assignment and dwell allocation (CTADA) algorithm is developed for multiple target tracking (MTT) in phased array radar (PAR) network. The basis of the CTADA scheme is to not only optimize the target-to-radar assignment, but also effectively allocate the limited time resource of each PAR to its responsible targets, such that the MTT performance could be efficiently improved in overload situations (the number of targets greatly exceeds the number of PARs). We formulate the resource allocation framework as a mathematical optimization problem, and use the normalized Bayesian Cramér-Rao lower bound as its objective function. The resulting optimization problem consists of two adaptable parameters, one for target-to-radar assignment and the other for dwell allocation. By exploiting the unique relationship between these two adaptable parameters, an efficient two-step solution technique, which consists of a convex relaxation step and a heuristic dividing step, is developed for the CTADA optimization problem. Simulation results verify the superiority of the proposed CTADA algorithm, in terms of the worst case tracking accuracy of the multiple targets.

A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for Cooperative Air Combat DWTA

A hybrid multiobjective discrete particle swarm optimization (HMODPSO) algorithm is proposed to solve cooperative air combat dynamic weapon target assignment (DWTA). First, based on the threshold of damage probability and time window constraints, a new cooperative air combat DWTA multiobjective optimization model is presented, which employs the maximum of the target damage efficiency and minimum of ammunition consumption as two competitive objective functions. Second, in order to tackle the DWTA problem, a mixed MODPSO and neighborhood search algorithm is proposed. Furthermore, the repairing operator is introduced into the mixed algorithm, which not only can repair infeasible solutions but also can improve the quality of feasible solutions. Besides, the Cauchy mutation is adopted to keep the diversity of the Pareto optimal solutions. Finally, a typical two-stage DWTA scenario is performed by HMODPSO and compared with three other state-of-the-art algorithms. Simulation results verify the effectiveness of the new model and the superiority of the proposed algorithm.

Improved discrete mapping differential evolution for multi-unmanned aerial vehicles cooperative multi-targets assignment under unified model

The cooperative multi-targets assignment for multiple unmanned aerial vehicles (UAV) is a complex combinatorial optimization problem. Multi-UAVs cooperation increases the scale of problems which cause a noticeable increase in task planning time. Moreover, it is difficult to build a unified assignment model because different tasks often require different numbers of UAVs and targets. Besides, the cooperative constraints of multi-UAVs in a three-dimensional environments are more complex than that in a two-dimensional environments, which makes it difficult to obtain an optimal solution. To solve these problems, we present a unified gene coding strategy to handle various models in a consistent framework. Then, a cooperative target assignment algorithm in a three-dimensional environments based on discrete mapping differential evolution is given. First, we use flight path cost to indicate the assignment relationship between the UAV and the target, which turns the optimization problem from discrete space to continuous space, and so the solving process can be simplified. Secondly, in order to obtain reasonable offspring for differential evolution, we map the solution back to the assignment relationship space according to inverse mapping rules. Finally, to avoid falling into a local optimal, a balance between exploration and exploitation is achieved by combining the dynamic crossover rate with the hybrid evolution strategy. The simulation results show that the proposed discrete mapping differential evolution algorithm with the unified gene coding strategy not only effectively solves the cooperative multi-targets assignment problem, but also improves the accuracy of the multi-targets assignment. It is also suitable for solving the large scale problem of assignment.

A Unified Modeling Method of UAVs Cooperative Target Assignment by Complex Multi-constraint Conditions

The multi-UAVs cooperative target assignment problem (MUCTAP) is a condition-complicated multi-models and multi-constraint combinatorial optimization problem, and it difficult to get a feasible solution by unified processing method. In order to solve this problem, a uniform model has been built for all kinds of unmanned aerial vehicles (UAVs) target assignment situations in this paper. We also design the unified processing method so that it can handle different types of MUCTAP. Considering the 3D environmental character, we present a method for MUCTAP by using spatially vertical section to compute the cost of flight path length, and apply the cost matrix of flight path length to optimize the target assignment algorithm. We also add the collaborative constraint relation to compute, so that it can improve the target assignment algorithm s accuracy. Simulation results show that this model can adapt to a variety of target assignment problems, and it has a high universality and accuracy. So it can effectively accomplish the task of MUCTAP.

Coordinated target assignment and intercept for unmanned air vehicles

Presents an end-to-end solution to the cooperative control problem represented by the scenario where M unmanned air vehicles (UAVs) are assigned to transition through N known target locations in the presence of dynamic threats. The problem is decomposed into the subproblems of: 1) cooperative target assignment; 2) coordinated UAV intercept; 3) path planning; 4) feasible trajectory generation; and 5) asymptotic trajectory following. The design technique is based on a hierarchical approach to coordinated control. Simulation results are presented to demonstrate the effectiveness of the approach.