Greedy Maximization for Asset‐Based Weapon–Target Assignment with Time‐Dependent Rewards

Cho, Doo‐Hyun; Choi, Han‐Lim

doi:10.1002/9781119266235.ch5

Cited by 13 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this scenario, the proposed MILP solution was compared with a heuristic technique, termed the First-in First-out (FIFO) greedy algorithm (Appendix A). This greedy heuristic is an extension of a sequential greedy algorithm for assignment [34] to take into account the handover requirement. Note that the greedy scheme can be a good reference algorithm as it works very well in many domains and also guarantees some optimality gap when the objective function satisfies certain conditions [34,35].…”

Section: Resultsmentioning

confidence: 99%

“…This greedy heuristic is an extension of a sequential greedy algorithm for assignment [34] to take into account the handover requirement. Note that the greedy scheme can be a good reference algorithm as it works very well in many domains and also guarantees some optimality gap when the objective function satisfies certain conditions [34,35]. Detailed theoretical analysis of the greedy heuristic is omitted as it is out of the focus of this paper.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Target Assignment with Seamless Handovers for Networked Radars

Kim

Cho

Lee

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

This paper proposes a binary linear programming formulation for multiple target assignment of a radar network and demonstrates its applicability to obtain optimal solutions using an off-the-shelf mixed-integer linear programming solver. The goal of radar resource scheduling in this paper is to assign the maximum number of targets by handing over targets between networked radar systems to overcome physical limitations such as the detection range and simultaneous tracking capability of each radar. To achieve this, time windows are generated considering the relation between each radar and target considering incoming target information. Numerical experiments using a local-scale simulation were performed to verify the functionality of the formulation and a sensitivity analysis was conducted to identify the trend of the results with respect to several parameters. Additional experiments performed for a large-scale (battlefield) scenario confirmed that the proposed formulation is valid and applicable for hundreds of targets and corresponding radar network systems composed of five distributed radars. The performance of the scheduling solutions using the proposed formulation was better than that of the general greedy algorithm as a heuristic approach in terms of objective value as well as the number of handovers.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Optimal Target Assignment with Seamless Handovers for Networked Radars

Kim

Cho

Lee

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…DWTA assigns the sequence of weapons for the equilibrium plan during multi-stage [7]. According to the operational mission, there are mainly target-based WTA (T-WTA) [8], asset-based WTA (A-WTA) [9] and sensor-WTA (S-WTA) [10,11] model. The T-WTA model adopts the kill effectiveness of weapons against targets as the optimization objective.…”

Section: Introductionmentioning

confidence: 99%

Efficient Decision Approaches for Asset-Based Dynamic Weapon Target Assignment by a Receding Horizon and Marginal Return Heuristic

et al. 2020

View full text Add to dashboard Cite

The weapon-target assignment problem is a crucial decision support in a Command and Control system. As a typical operational scenario, the major asset-based dynamic weapon target assignment (A-DWTA) models and solving algorithms are challenging to reflect the actual requirement of decision maker. Deriving from the “shoot–look–shoot” principle, an “observe–orient–decide–act” loop model for A-DWTA (OODA/A-DWTA) is established. Focus on the decide phase of the OODA/A-DWTA loop, a novel A-DWTA model, which is based on the receding horizon decomposition strategy (A-DWTA/RH), is established. To solve the A-DWTA/RH efficiently, a heuristic algorithm based on statistical marginal return (HA-SMR) is designed, which proposes a reverse hierarchical idea of “asset value-target selected-weapon decision.” Experimental results show that HA-SMR solving A-DWTA/RH has advantages of real-time and robustness. The obtained decision plan can fulfill the operational mission in the fewer stages and the “radical-conservative” degree can be adjusted adaptively by parameters.

show abstract

“…According to the number of optimization objectives in the problem, WTA problems can be divided into single-objective WTA (SOWTA) problems, with only one optimization objective, and MOWTA problems, with at least two optimization objectives. Currently, research on the SOWTA problem is more in-depth, and respective optimization algorithms [3][4][5][6][7] can quickly obtain a better allocation scheme. For the SOWTA problem with the background of air defense intercept, Liu et al [4] proposed a firepower unit correlation matrix and designed a hybrid optimized algorithm based on the PSO and tabu search algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…For the SOWTA problem with the background of against-ground targets, Wang et al [5] improved the particle initialization and inertia weight selection methods of the PSO algorithm and effectively improved the optimization efficiency and allocation results of the large-scale SOWTA problem. For the dynamic SOWTA problem, Cho et al [6] constructed a static SOWTA model with several constraints and designed an improved greedy algorithm with phased optimization, which effectively improved the optimization speed of the problem, but it was still solving a static SOWTA problem; Mei et al [7] constructed a dynamic SOWTA model based on the killing region of weapon platform and proposed a combinatorial algorithm derived from heuristic algorithm and receding horizon control. The experimental results show that the proposed model can describe the combat scene accurately and the algorithm can improve the speed of solving the dynamic SOWTA problem.…”

Section: Introductionmentioning

confidence: 99%

A Multiobjective Particle Swarm Optimization Algorithm Based on Multipopulation Coevolution for Weapon‐Target Assignment

Wang

Zhang

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Weapon-target assignment (WTA) is critical to command and decision making in modern battlefields and is a typical nondeterministic polynomial complete problem. To solve WTA problems with multiple optimization objectives, a multipopulation coevolution-based multiobjective particle swarm optimization (MOPSO) algorithm is proposed to realize the rapid search for the globally optimal solution. The algorithm constructs a master-slave population coevolution model. Each slave population corresponds to an objective function and is used to search for noninferior solutions. The master population receives all the noninferior solutions from the slave populations, repairs the gaps between the noninferior solutions, and generates a relatively optimal Pareto optimal solution set. In addition, to accelerate the slave populations searching for noninferior solutions and master population repairing the gaps between noninferior solutions, the particle velocity update method is improved. The simulation results show that the proposed algorithm has higher computational efficiency and achieves better solutions than existing algorithms capable of providing a good solution. The method is suitable for rapidly solving multiobjective WTA (MOWTA) problems.

show abstract

Greedy Maximization for Asset‐Based Weapon–Target Assignment with Time‐Dependent Rewards

Cited by 13 publications

References 15 publications

Optimal Target Assignment with Seamless Handovers for Networked Radars

Optimal Target Assignment with Seamless Handovers for Networked Radars

Efficient Decision Approaches for Asset-Based Dynamic Weapon Target Assignment by a Receding Horizon and Marginal Return Heuristic

A Multiobjective Particle Swarm Optimization Algorithm Based on Multipopulation Coevolution for Weapon‐Target Assignment

Contact Info

Product

Resources

About