On weapons scores and force strengths

Anderson, Lowell Bruce; Miercort, Frederic A.

doi:10.1002/1520-6750(199504)42:3<375::aid-nav3220420305>3.0.co;2-0

Cited by 8 publications

(4 citation statements)

References 7 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…12 Following the guidance of Anderson and Miercort (1995) we set ß to 1.0 and solve the N R + N B linear equations for N R + N B -1 unknowns.…”

Section: Discussionmentioning

confidence: 99%

An Investigation of System Identification Techniques for Simulation Model Abstraction

Popken¹,

Cox²

2000

View full text Add to dashboard Cite

PubUc raporling burden lor this colectian of mlormation a estsnated to average 1 how par response, including tha tima for reviewing instructions, seerching oiisting data sourcts. gathering and maintaining Itta data needed, and competing and ravtawing lha coatction of information. Sand comments regarding this burdan estanata or any othtr aspect ol this collaction of intotmatwn, including suggasttons for raducmg this burden, to Washington Headquarters Services. This report summarizes research into the application of system identification techniques to simulation model abstraction. System identification produces simplified mathematical models that approximate the dynamic behaviors of the underlying stochastic simulations. Four state-space system identification techniques were examined: Canonical State-space, Compartmental Models, Maximum Entropy, and Hidden Markov Models (HMM). Two stochastic simulation models were identified: the "Attrition Simulation", a simulation of two opposing forces, each operating with multiple weapon system types; and the "Mission Simulation," a simulation of a squadron of aircraft performing battlefield air interdiction. The system identification techniques were evaluated and compared under a variety of scenarios on how well they replicate the distributions of the simulation states and decision outputs. Encouraging results were achieved by the HMM technique applied to Attrition Simulation -and by the Maximum Entropy technique applied to the Mission Simulation. This report also discusses the run-time performance of the algorithms, the development of suitable model structures, and implications for future efforts. SUBJECT TERMS AbstractThis report summarizes research into the application of system identification techniques to simulation model abstraction. System identification produces simplified mathematical models that approximate the dynamic behaviors of the underlying stochastic simulations. Four state-space system identification techniques were examined: Canonical State-Space, Compartmental Models, Maximum Entropy, and Hidden Markov Models (HMM). Two stochastic simulation models were identified: the "Attrition Simulation", a simulation of two opposing forces, each operating with multiple weapon system types; and the "Mission Simulation", a simulation of a squadron of aircraft performing battlefield air interdiction. The system identification techniques were evaluated and compared under a variety of scenarios on how well they replicate the distributions of the simulation states and decision outputs. Encouraging results were achieved by the HMM technique applied to the Attrition Simulation -and by the Maximum Entropy technique applied to the Mission Simulation. This report also discusses the run-time performance of the algorithms, the development of suitable model structures, and implications for future efforts.

show abstract

“…12 Following the guidance of Anderson and Miercort (1995) we set ß to 1.0 and solve the N R + N B linear equations for N R + N B -1 unknowns.…”

Section: Discussionmentioning

confidence: 99%

An Investigation of System Identification Techniques for Simulation Model Abstraction

Popken¹,

Cox²

2000

View full text Add to dashboard Cite

show abstract

“…The performances considered there are similar to our degrading platforms' performances. The models presented in [16], called Pexpot, Levpot and Dynpot, were also developed as vulnerability considerations based on the attrition rate function and thus indirectly describe the kind of expected degradation capabilities. An essential difference of our model is that the degradation of the system appears as a direct consequence of self-degradation caused by the effects of the repeated cycles.…”

Section: Explanation Of the General Modelmentioning

confidence: 99%

Deterministic Mathematical Modelling of Platform Performance Degradation in Cyclic Operation Regimes

Kapor¹,

Milinović²,

Jeremić³

et al. 2015

SV-JME

View full text Add to dashboard Cite

Machines operating in cycles and their properties have not been studied in depth in literature and, as such, are not well described by integral mathematical models. If the effects of their operation are actions on the given working surfaces under given constraints, then the quality of the affected surfaces can be described by reliability functions. In this manner, the operating capabilities of the platform can be determined. A majority of the published papers use a standard approach to the measured performances that depend on the machine's designed purposes. Such processes are described in [1] to [3] for the abrasive flow machines (AFM) with which material is hardened by randomly treating the working surface with abrasive particles with polymeric fillers, and dispersed within the flow media. The authors of [1] classified the work piece parameters into three groups based, among others, on the number of cycles (operations) and the machining time. Some of these parameters were determined experimentally in [2], in which the authors recognized that the parameters denoted as the creeping time and the cycles frequency have impact on the quality of the machining process. In [3], the authors experimentally prove that the aforementioned parameters influence the process. Common for all three papers is that they do not include hidden random effects caused by particles affecting the surfaces in cyclic operations, although such effects significantly influence the quality of the surface treatment. In all three papers, there is no mathematical modelling of the process.Another similar type of machine with cyclic operation affecting working surfaces is described in [4] as shot-peening (SP) platforms. They bombard a surface with spherical beads to increase the material fatigue strength. The physical modelling of the influence of the bead shapes on the performance of the surface hardening process is presented in [5]. Random surface effects due to bombing cycles are a result of the quality of the machine's performance. However, the connection between the effects and the particular operations is missing in • Proposed methodology for performances degradation caused by operations composed in cycles.• Using Gaussian probability distribution law to predict degradation measures.• Predicting the changes of probability dispersion based on modelling and experimental data.• Determination of an analytical model based on a hypothesis that the degrading effects are a function of the platform capacity, frequency of operations and the number of available cycles.

show abstract

“…Hartley claims the Lanchester square law and linear laws do not provide good models of combat attrition as evidenced by historical data, but that a particular homogeneous linear-logarithmic law provides a good approximation to historical data (6). Another possibility we considered, one having some additional theoretical basis, is that of using a Helmbold-type system; for instance,…”

Section: Attritionmentioning

confidence: 99%

A Model-Based Approach to Battle Execution Monitoring

Kaste¹,

Hansen²

2005

View full text Add to dashboard Cite

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY) January 20052. REPORT TYPE ARL-TR-3394 SPONSOR/MONITOR'S ACRONYM(S) 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTES ABSTRACTThe work set forth in this report is intended to lead to portions of an integrated system to dynamically link automated plan generation and analysis with execution monitoring. The report deals with algorithms for, and interfaces to, a prototype battletrajectory decision aid. We explored complexities of navigating parameter tradeoff spaces and performed detailed studies of strength/attrition parameter estimation and calibration techniques. We investigated software for solving differential systems and for visualizing and interacting with decision aid data. We considered aspects of the analytical theory of dynamic systems: mathematical chaos, stability, and control. We set the groundwork for formulation of models to produce systems exhibiting inherently chaotic behavior and for studying qualitative aspects of phase plane trajectories. It is probable that nonlinear analytic techniques will soon be considered as invaluable to commanders.

show abstract

On weapons scores and force strengths

Cited by 8 publications

References 7 publications

An Investigation of System Identification Techniques for Simulation Model Abstraction

An Investigation of System Identification Techniques for Simulation Model Abstraction

Deterministic Mathematical Modelling of Platform Performance Degradation in Cyclic Operation Regimes

A Model-Based Approach to Battle Execution Monitoring

Contact Info

Product

Resources

About