Sei-Hoon Moon
The V/L code simulation method has been employed to develop Pcd/h of an electric relay which was used as a target in an experimental study for achieving its Pcd/h. Three different simulations have been conducted to reproduce kill probabilities of the relay resulted from the experiment. While vulnerability simulations produced much lower kill probabilities than the experiment result, a lethality simulation correctly reproduced it. It is discussed that if the lethality simulation setup correctly re-enacted the experiment, the experiment result cannot be interpreted as correct Pcd/h of the relay. Rather the correct one should be the result of the vulnerability simulation performed with the same setup as the lethality simulation. Finally, this paper concludes with stress on the need and importance of complementary use of the simulation and experiment methods in deriving component Pcd/h.
Gregor Thomeczek, Ian Colwill, and Elias Stipidis
Mobility and network connectivity to the rest of the fleet are critical capabilities of battlefield assets. Battlefield Mobile Ad-Hoc Networks (MANETs) face a dynamic and unpredictable operating environment. Bottlenecks and fluctuating connectivity can stem from a number of factors: such as clusters of vehicles separated by buildings or terrain obstacles; intentional interference, such as jamming; and by failure or damage. Exploiting node mobility is a common solution to reintegrate disjointed clusters of mobile nodes in a network, however given that each mobile node in the battlefield has a task of given criticality to perform as part of an overall mission plan, one must recognise that in a realistic battlefield scenario, a Topology Management Algorithm (TMA) cannot assume that all mobile nodes are available to be relocated to recover a partitioned network and that further nodes deployed in the area not equally be lost due to a persistent localised danger. In this paper, a mission aware network topology healing algorithm is presented which harvests mission information from a fleet wide shared data model and relocates mobile nodes within their mission parameters taking into account mission goals, criticality of goals and waypoints, mission critical neighbours and terrain danger zones. This novel algorithm is verified by modelling and simulation by demonstrating a significant performance gain when comparing it to a traditional TMA without mission awareness.
R.J. Mukhedkar and S.D. Naik
The main objective of the weapon-target allocation problem (WTAP) is to achieve maximum survival of assets and maximum engagement of threats with the minimum number of resources such as weapons, ammunition, and human resources. The WTAP solution space is very large and complex with very large combinations of weapon type, ammunition type and number of rounds to be fired. Within the solution space, there may only be a few local minima and global minima. A very large number of iterations and execution time may be necessary when using conventional methods to identify optimum solutions within the solution space. This paper present a WTAP solution for mixed weapons. The weapon target ammunition data has been generated using Monte-Carlo simulation. An optimum solution has been estimated for a WTAP solution using a fuzzy approach—WTAP-Fuzzy. In addition to WTAP-Fuzzy, WTAP solutions using genetic algorithm (WTAP-GA); particle swarm optimization (WTAP-PSO); and simulated annealing (WTAP-SA) have been simulated and analysed and shown to have similar performance to WTAP-Fuzzy.
Patricia Dexter
A robust and consistent classification framework was required to undertake emerging technology impact assessments for the Australian Army as the literature did not provide suitable alternatives. A context specific and fit for purpose two-sided contextual classification framework for technologies for the Australian Army has been developed using a combination of literature sources and a Delphi-like group technique. This classification framework provides a consistent and common language for an emerging technology impact assessment process. The robust classification framework and the method used in its development can be readily applied to other military contexts in order to generate specific frameworks for those contexts.
Kevin Y.K. Ng and Neville G.F. Sancho
Maritime surveillance initially involves the military operational commander deciding on the areas to be searched. The aim is to detect and classify targets/ships subject to the aircraft’s time-on-station constraint, namely the aircraft is restricted to no more than 12 flying hours/day. Very often, a precedence relationship exists between regions to be searched. In addition, time windows or rigid lower and upper bounds on the time the regions have to be searched are also specified. It has recently been shown by Marlow et al [1] and the authors [5] that the surveillance problem can be modelled as a travelling salesman problem (TSP). However, our problem is unique and distinct from the classical TSP. The time windows and precedence constraints are frequently subject to changes or revisions because of continuous intelligence updates. Previously calculated flight path can become infeasible. The solution procedure should only involve easy implementable algorithms so as to allow flight crew to readily recalculate the revised flight path, preferably in an interactive mode. This paper develops an implementable hybrid dynamic programming/heuristic algorithm for surveillance mission planning. To avoid incorporating the difficulty of time windows or precedence constraint changes in the formulation, we adopt the K best solution strategy approach [2]. The essence is to put aside the time windows and/or the precedence constraint and compute successively more desirable solutions for the TSP until the best solution which does satisfy the change restrictions is found, namely the kth best solution.
