from Scythe : Proceedings and Bulletin of the International Data Farming Community, Issue 1 Workshop 13The German Federal Office of Defense Technology and 
Procurement, has been analyzing the influence of networked
sensors and effectors on military capabilities. Background 
for the actual technical evaluations of sensors, effectors and 
the connecting network is the scenario vignette: Convoy 
Protection as part of an over all scenario PSO in an urban 
environment

Erdmann, B.

Haymann, Karsten

Musselman, Roger

Schwierz, Klaus-P.

Calhoun, Institutional Archive of the Naval Postgraduate School

Calhoun: The NPS Institutional ArchiveFaculty and Researcher Publications Faculty and Researcher Publications2006-11Team 2: Simulation of TechnicalAspects in Network-Centric Operations: ResultsErdmann, B.http://hdl.handle.net/10945/35601Team 2: Simulation of Technical Aspectsin Network-Centric Operations: ResultsTEAM 2 MEMBERSB. ERDMANN – Lead, Contact*Karsten HaymannRoger MusselmanDr. Klaus-P. Schwierz EADS Deutschland GmbHTEAM PROPOSALThe German Federal Office of Defense Technology and Procurement, has been analyzing the influence of networked sensors and effectors on military capabilities. Background for the actual technical evaluations of sensors, effectors and the connecting network is the scenario vignette: Convoy Protection as part of an over all scenario PSO in an urban environment. The convoy transports fuel, ammunition and food in an urban environment and is protected and supported by:• Two check points as flank protections• UAV, UGV• 4 AWC (Wiesel)• 3 LIV (Fuchs) Evaluation of the Ground PictureThere is an asymmetric threat: A local burning obstacle brings the convoy to a stop. Mobile barriers are used in an ambush, with snipers and bazooka shots that are looking for an opportunity to intercept the convoy. The convoy leader can react on information from UAV and UGV on possible trafficability of the pre planned route. Detours are possible.The MOÉs are:• RED casualties and• BLUE casualties. The technical effects of special sensors and effectors at the convoy and his NCO capability are examined. Variations are investigated in the technical representation of UAV / UGV speeds, communication and sensors, in scenario details and in a variation of protection and equipment and these will be interpreted in the following three step approach:1. using existing equipment (sensors, effectors),2. using equipment under development (sensors, effectors),3. using future equipment (sensors, effectors). ResultsThe basic implementation of the scenario in MANA is challenging because of the high level of detail and clearly we reach the limitations of the tool MANA. Especially the implementation of the convoy as a sequence of vehicles was really time consuming. In defining the “working point” of the scenario in various cases secondary and third order effects in MANA lead to completely unpredictable model behavior.Sequentially, in 5 Data Farming activities on the 128 / 32 node clusters at EADS, we found a parameter set corresponding to our question base. We continued the work of PAIW12 where we looked at 16 parameters: Convoy Speed, Hit-probability and Combat Distance of Fighting Vehicles, UAV / UGV Speed, UAV / UGV Sensor Range, UAV / UGV Classification Probability, Network Reliability, Network Accuracy, Bazooka Range, Bazooka Hit-probability, Bazooka Reaction Time, Rifle Range, Rifle Hit-probability, Rifle Maximum Number of Targets per Timestep.Unfortunately the results were almost all equal to zero. That means no casualties on the blue side happened.With our interpretations of the datafarming results from PAIW12, we saw that we did not appropriately appreciate the cumulative effect of the UAV’s and UGV’s detection probability ((per time-step). Furthermore we saw that with the selected ranges for the weapon factors we gave a certain disadvantage to the red side. Therefore we studied with our first investigations on IDFW13, the effect of the following factors:• UGV’s detection probability• Rifle’s hit probability• Rifle’s maximum number of targets per time-step10 - IDFW 13 - Team 2* For more information contact: Bertram Erdmann, bertram.erdmann@eads.com, 804-765-1768, USA• Bazooka’s hit probability• AWC/LIV’s hit probabilityWe found that the rifle’s maximum number of targets per time-step is the most import factor (it already explains 70 % of the responses by itself). The following factors are as expected: the UGV’s detection probability and the rifle’s hit probability.At the same time we had to observe that there were no casualties on the red side. We could explain this by the stealth factor of the red agents. Casualties only appeared when it was smaller than 95 %. This confirms a  result we got during the revision of PAIW12.Here we studied the probability of  casualties of the second tanker by a gridded design for the factors stealth and UGV’s detection probability. In the direction of the stealth factor there is a discontinuity in the surface and it is constant on the two branches. This behavior is strange and contradicts the results of a simple experiment we made with the default scenario of MANA where we added a terrain with the same attributes.We made a thousand replications for each level of the stealth factor of the blue agent. The left part of the diagram shows the casualties of both agents with the terrain attribute concealment = 0.9 and cover = 0. For the right part the cover attribute was increased to 0.8. Obviously the curves are not constant. This fact should be discussed with the developers of MANA.To go on with our investigations we found a reasonable and practical workaround. By adding the “shot-at” - trigger state the blue escort can’t open fire but return fire. And with the “taken shot” - trigger state the red side reduces the stealth factor from 95 % to 90 % when opening fire.With these modifications and some changes in the design we carried out the data farming. In comparison to the design of PAIW12, besides some changes of ranges, we removed the concealment factor on the red side and added the hit-probability of the escort and the maximum number of target per timestep of the rifles. Instead of the 16 factors we had 17, and so we had to switch to the bigger NOLH - design with 129 excursions.Again we worked with a hundred replications. Looking at the results we were especially interested in:• the probability that the convoy takes the original route (to study the effects of the sensor and network parameters) =: MOE1• the kill-probability of the first truck (food) (to study the effects of the escort parameters) =: MOE2• the kill-probability of the last truck (gas) (to study the effects of the escort parameters) =: MOE3The regression tree for the probability that the convoy takes the original route (MOE1) shows that in the following order the network accuracy, the network reliability, the UGV’s sensor range, the UGV’s speed and the UAV’s sensor range are the most important factors.For example the following picture shows MOE1 depending on network accuracy and reliability.It shows the expected behavior that with low accuracy and reliability of the network the convoy leader doesn’t get the information to stay on the original route.11 - IDFW 13 - Team 2In contrast to the results of PAIW12 the next picture shows the distributions of the kill-probabilities of the trucks which obviously are not equal to zero.So we could go on with first analysis. The regression tree neglecting the sensor and network parameters showed that the rifle’s maximum number of targets per timestep and it’s hit-probability followed by the hit-probability of the escort are the most important factors as far as MOE2 and MOE3 are concerned.The upper regression tree for MOE3 was built taking all factors into account. It shows that for MOE3 the weapon parameters are more important than the sensor and network parameters.By a regression model we studied interactions between factors. The model that explains the results to 75 % came up with some minor interactions :The workshop ended with a convoy scenario that worked fairly well. There are still open questions:• Why wasn’t there an action of the bazooka?• Why was the bazooka not killed?• How will the results change depending on the behavior on the red side?• What are the effects of the sensors of the escort (without UGV an UAV)?• What will be the effect of a mine road block?• Can the upper questions be answered by MANA?Above all we have to have a closer look at the results of the datafarming session at IDFW13! 12 - IDFW 13 - Team 2

Team 2: Simulation of Technical Aspects in Network-Centric Operations: Results

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Team 2: Simulation of Technical Aspects in Network-Centric Operations: Results

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