Real-Time Heuristics and Metaheuristics for Static and Dynamic Weapon Target Assignments

The problem of targeting and engaging individual missiles (targets) with an arsenal of interceptors (weapons) is known as the weapon target assignment problem. This problem has been well-researched since the seminal work in 1958. There are two distinct categories of the weapon target assignment problem: static and dynamic. The static weapon target assignment problem considers a single instance in which a known number of incoming missiles is to be engaged with a finite number of interceptors. By contrast, the dynamic weapon target assignment problem considers either follow on engagement(s) should the first engagement(s) fail, a subsequent salvo of incoming missiles, or both. This research seeks to define and solve a realistic dynamic model. First, assignment heuristics and metaheuristics are developed to provide rapid near-optimal solutions to the static weapon target assignment. Next, a technique capable of determining how many of each interceptor type to reserve for a second salvo by means of approximate dynamic programming is developed. Lastly, a model that realistically considers erratic flight paths of incoming missiles and determines assignments and firing sequences of interceptors within a simulation to minimize the number of hits to a protected asset is developed. Additionally, the first contemporary survey of the weapon target assignment problem since 1985 is presented. Collectively, this work extends the research of missile defense into practical application more so than currently is found within the literature

Efficient Heuristic Algorithms for Single-Vehicle Task Planning With Precedence Constraints

This article investigates the task planning problem where one vehicle needs to visit a set of target locations while respecting the precedence constraints that specify the sequence orders to visit the targets. The objective is to minimize the vehicle’s total travel distance to visit all the targets while satisfying all the precedence constraints. We show that the optimization problem is NP-hard, and consequently, to measure the proximity of a suboptimal solution from the optimal, a lower bound on the optimal solution is constructed based on the graph theory. Then, inspired by the existing topological sorting techniques, a new topological sorting strategy is proposed; in addition, facilitated by the sorting, we propose several heuristic algorithms to solve the task planning problem. The numerical experiments show that the designed algorithms can quickly lead to satisfying solutions and have better performance in comparison with popular genetic algorithms

Approximate Dynamic Programming for Military Resource Allocation

This research considers the optimal allocation of weapons to a collection of targets with the objective of maximizing the value of destroyed targets. The weapon-target assignment (WTA) problem is a classic non-linear combinatorial optimization problem with an extensive history in operations research literature. The dynamic weapon target assignment (DWTA) problem aims to assign weapons optimally over time using the information gained to improve the outcome of their engagements. This research investigates various formulations of the DWTA problem and develops algorithms for their solution. Finally, an embedded optimization problem is introduced in which optimization of the multi-stage DWTA is used to determine optimal weaponeering of aircraft. Approximate dynamic programming is applied to the various formulations of the WTA problem. Like many in the field of combinatorial optimization, the DWTA problem suffers from the curses of dimensionality and exact solutions are often computationally intractability. As such, approximations are developed which exploit the special structure of the problem and allow for efficient convergence to high-quality local optima. Finally, a genetic algorithm solution framework is developed to test the embedded optimization problem for aircraft weaponeering

Models and algorithms for multi-agent search problems

The problem of searching for objects of interest occurs in important applications ranging from rescue, security, transportation, to medicine. With the increasing use of autonomous vehicles as search platforms, there is a need for fast algorithms that can generate search plans for multiple agents in response to new information. In this dissertation, we develop new techniques for automated generation of search plans for different classes of search problems. First, we study the problem of searching for a stationary object in a discrete search space with multiple agents where each agent can access only a subset of the search space. In these problems, agents can fail to detect an object when inspecting a location. We show that when the probabilities of detection only depend on the locations, this problem can be reformulated as a minimum cost network optimization problem, and develop a fast specialized algorithm for the solution. We prove that our algorithm finds the optimal solution in finite time, and has worst-case computation performance that is faster than general minimum cost flow algorithms. We then generalize it to the case where the probabilities of detection depend on the agents and the locations, and propose a greedy algorithm that is 1/2-approximate. Second, we study the problem of searching for a moving object in a discrete search space with multiple agents where each agent can access only a subset of a discrete search space at any time and agents can fail to detect objects when searching a location at a given time. We provide necessary conditions for an optimal search plan, extending prior results in search theory. For the case where the probabilities of detection depend on the locations and the time periods, we develop a forward-backward iterative algorithm based on coordinate descent techniques to obtain solutions. To avoid local optimum, we derive a convex relaxation of the dynamic search problem and show this can be solved optimally using coordinate descent techniques. The solutions of the relaxed problem are used to provide random starting conditions for the iterative algorithm. We also address the problem where the probabilities of detection depend on the agents as well as the locations and the time periods, and show that a greedy-style algorithm is 1/2-approximate. Third, we study problems when multiple objects of interest being searched are physically scattered among locations on a graph and the agents are subject to motion constraints captured by the graph edges as well as budget constraints. We model such problem as an orienteering problem, when searching with a single agent, or a team orienteering problem, when searching with multiple agents. We develop novel real-time efficient algorithms for both problems. Fourth, we investigate classes of continuous-region multi-agent adaptive search problems as stochastic control problems with imperfect information. We allow the agent measurement errors to be either correlated or independent across agents. The structure of these problems, with objectives related to information entropy, allows for a complete characterization of the optimal strategies and the optimal cost. We derive a lower bound on the performance of the minimum mean-square error estimator, and provide upper bounds on the estimation error for special cases. For agents with independent errors, we show that the optimal sensing strategies can be obtained in terms of the solution of decoupled scalar convex optimization problems, followed by a joint region selection procedure. We further consider search of multiple objects and provide an explicit construction for adaptively determining the sensing actions

Development and demonstration of a performance evaluation framework for threat evaluation and weapon assignment systems

Thesis (MEng)--Stellenbosch University, 2016.ENGLISH ABSTRACT: defending ground assets against aerial threats. These weapon systems are employed in conjunction with an array of sensor systems which are capable of detecting and tracking aerial threats, and providing information for determining the level of danger that the threats pose to the defended system. In this context, the purpose of a Threat Evaluation and Weapon Assignment (TEWA) system is to provide decision support to the human operators who are tasked with assigning weapon systems to counter the aerial threats. The TEWA system typically assigns appropriate system threat values to each of the aerial threats which indicates the level of danger they pose to the defended system. These system threat values are used, in turn, when seeking to optimise the allocation of weapon systems to threats in such a way that the weighted cumulative survival probability of the aerial threats is minimised. These weapon allocations are suggested to a human operator for implementation via a human machine interface. A large number of TEWA systems are already in use around the world, but due to the confidential nature of this research area, descriptions of the working of these systems are typically not available in the open literature. Despite the critical role these systems play in the current, evolving network-centric warfare environment, there exists no generic framework in the literature for evaluating the performance of TEWA systems. The work contained in this thesis therefore adds to the South African TEWA knowledge base by determining the extent to which current TEWA-related research in a ground-based air-defense environment forms a coherent foundation from which further system development and performance evaluation can continue. This broad research aim is achieved by developing a performance evaluation simulation framework for TEWA systems and demonstrating the feasibility of locally developed TEWA algorithms. A system-of-systems simulation modelling approach is adopted in the design of this framework. Using the framework, limitations present in the TEWA algorithms are identified and mitigation strategies are suggested. These strategies include a novel threat value fusion methodology, an alternative weapon system modelling approach and the implementation of a genetic algorithm for solving the weapon allocation problem approximately. Design requirements for an effective human machine interface are also described in some detail and several TEWA system performance metrics are suggested. The working of the framework is finally demonstrated in the context of a comprehensive, near-realistic, but hypothetical, ground-based air-defense scenario.AFRIKAANSE OPSOMMING: In 'n militêre lugafweeromgewing word grond-gebaseerde wapenstelsels gebruik om grondbates teen lugbedreigings te beskerm. Hierdie wapenstelsels word in oorleg met 'n aantal sensorstelsels aangewend wat daartoe in staat is om lugbedreigings op te spoor en te volg, en inligting te verskaf waarvolgens die vlak van gevaar wat hierdie bedreigings vir die verdedigde stelsel inhou, bepaal kan word. In hierdie konteks is die doel van 'n Bedreigingsafskatting-en-wapentoewysing (TEWA) stelsel om besluitsteun aan menslike operateurs te bied wat daarvoor verantwoordelik is om wapenstelsels aan lugbedreigings toe te ken. Die TEWA stelsel heg tipies 'n gepaste stelsel-wye bedreigingswaarde aan elkeen van die lugbedreigings wat die vlak van gevaar aandui wat hul met betrekking tot die verdedigde stelsel inhou. Hierdie stelsel-wye bedreigingswaardes word dan gebruik in die soeke na optimale toewysings van wapenstelsels aan die bedreigings om sodoende die geweegde, geakkumuleerde oorlewingswaarskynlikheid van die lugbedreigings te minimeer. Wapentoewysingsvoorstelle word deur middel van 'n mens-masjien koppelvlak aan 'n menslike operateur vir implementasie voorgel^e. 'n Groot getal TEWA stelsels is reeds w^ereldwyd in gebruik, maar as gevolg van die vertroulike aard van hierdie navorsingsgebied, is beskrywings van die werking van hierdie stelsels tipies nie in die oop literatuur beskikbaar nie. Ten spyte van die kritiese rol wat hierdie stelsels in die huidige, evoluerende netwerk-sentriese oorlogvoeringsomgewing speel, bestaan daar geen generiese raamwerke in die literatuur waarvolgens die werkverrigting van TEWA stelsels geëvalueer kan word nie. Die werk wat in hierdie tesis vervat is, dra dus by tot die Suid-Afrikaanse TEWA stelselkennisbasis deur vas te stel tot watter mate die huidige TEWA stelsel-verwante navorsing in die konteks van 'n grond-gebaseerde lugafweeromgewing 'n samehorige grondslag vorm waarop verdere stelselontwikkeling en werkverrigtingsanalise kan voortbou. Hierdie breë navorsingsdoel word bereik deur 'n simulasieraamwerk daar te stel waarvolgens die werkverrigting van TEWA stelsels gemeet kan word en met behulp waarvan die werkbaarheid van plaaslik-ontwikkelde TEWA algoritmes gedemonstreer kan word. Deur van die raamwerk gebruik te maak, word beperkings in die TEWA algoritmes geïdentifiseer en word strategieë voorgestel waarvolgens hierdie beperkings reggestel kan word. Hierdie strategieë sluit in 'n nuwe metodologie vir die samevoeging van bedreigingswaardes, 'n alternatiewe wapenstelsel-modelleringsbenadering en die implementasie van 'n genetiese algoritme vir die benaderde oplossing van die wapentoewysingsprobleem. Ontwerpsvereistes vir 'n doeltreffende mens-masjien koppelvlak word ook noukeurig beskryf, en 'n aantal TEWA stelsel werkverrigtingsmaatstawwe word voorgestel. Die werking van die raamwerk word uiteindelik aan die hand van 'n omvattende, byna realistiese, maar hipotetiese, grond-gebaseerde lugafweerscenario gedemonstreer

Capturing Risk in Capital Budgeting

NPS NRP Technical ReportThis proposed research has the goal of proposing novel, reusable, extensible, adaptable, and comprehensive advanced analytical process and Integrated Risk Management to help the (DOD) with risk-based capital budgeting, Monte Carlo risk-simulation, predictive analytics, and stochastic optimization of acquisitions and programs portfolios with multiple competing stakeholders while subject to budgetary, risk, schedule, and strategic constraints. The research covers topics of traditional capital budgeting methodologies used in industry, including the market, cost, and income approaches, and explains how some of these traditional methods can be applied in the DOD by using DOD-centric non-economic, logistic, readiness, capabilities, and requirements variables. Stochastic portfolio optimization with dynamic simulations and investment efficient frontiers will be run for the purposes of selecting the best combination of programs and capabilities is also addressed, as are other alternative methods such as average ranking, risk metrics, lexicographic methods, PROMETHEE, ELECTRE, and others. The results include actionable intelligence developed from an analytically robust case study that senior leadership at the DOD may utilize to make optimal decisions. The main deliverables will be a detailed written research report and presentation brief on the approach of capturing risk and uncertainty in capital budgeting analysis. The report will detail the proposed methodology and applications, as well as a summary case study and examples of how the methodology can be applied.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Robust distributed planning strategies for autonomous multi-agent teams

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012.Cataloged from department-submitted PDF version of thesis. This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 225-244).The increased use of autonomous robotic agents, such as unmanned aerial vehicles (UAVs) and ground rovers, for complex missions has motivated the development of autonomous task allocation and planning methods that ensure spatial and temporal coordination for teams of cooperating agents. The basic problem can be formulated as a combinatorial optimization (mixed-integer program) involving nonlinear and time-varying system dynamics. For most problems of interest, optimal solution methods are computationally intractable (NP-Hard), and centralized planning approaches, which usually require high bandwidth connections with a ground station (e.g. to transmit received sensor data, and to dispense agent plans), are resource intensive and react slowly to local changes in dynamic environments. Distributed approximate algorithms, where agents plan individually and coordinate with each other locally through consensus protocols, can alleviate many of these issues and have been successfully used to develop real-time conflict-free solutions for heterogeneous networked teams. An important issue associated with autonomous planning is that many of the algorithms rely on underlying system models and parameters which are often subject to uncertainty. This uncertainty can result from many sources including: inaccurate modeling due to simplifications, assumptions, and/or parameter errors; fundamentally nondeterministic processes (e.g. sensor readings, stochastic dynamics); and dynamic local information changes. As discrepancies between the planner models and the actual system dynamics increase, mission performance typically degrades. The impact of these discrepancies on the overall quality of the plan is usually hard to quantify in advance due to nonlinear effects, coupling between tasks and agents, and interdependencies between system constraints. However, if uncertainty models of planning parameters are available, they can be leveraged to create robust plans that explicitly hedge against the inherent uncertainty given allowable risk thresholds. This thesis presents real-time robust distributed planning strategies that can be used to plan for multi-agent networked teams operating in stochastic and dynamic environments. One class of distributed combinatorial planning algorithms involves using auction algorithms augmented with consensus protocols to allocate tasks amongst a team of agents while resolving conflicting assignments locally between the agents. A particular algorithm in this class is the Consensus-Based Bundle Algorithm (CBBA), a distributed auction protocol that guarantees conflict-free solutions despite inconsistencies in situational awareness across the team. CBBA runs in polynomial time, demonstrating good scalability with increasing numbers of agents and tasks. This thesis builds upon the CBBA framework to address many realistic considerations associated with planning for networked teams, including time-critical mission constraints, limited communication between agents, and stochastic operating environments. A particular focus of this work is a robust extension to CBBA that handles distributed planning in stochastic environments given probabilistic parameter models and different stochastic metrics. The Robust CBBA algorithm proposed in this thesis provides a distributed real-time framework which can leverage different stochastic metrics to hedge against parameter uncertainty. In mission scenarios where low probability of failure is required, a chance-constrained stochastic metric can be used to provide probabilistic guarantees on achievable mission performance given allowable risk thresholds. This thesis proposes a distributed chance-constrained approximation that can be used within the Robust CBBA framework, and derives constraints on individual risk allocations to guarantee equivalence between the centralized chance-constrained optimization and the distributed approximation. Different risk allocation strategies for homogeneous and heterogeneous teams are proposed that approximate the agent and mission score distributions a priori, and results are provided showing improved performance in time-critical mission scenarios given allowable risk thresholds.by Sameera S. Ponda.Ph.D

Proceedings, MSVSCC 2019

Old Dominion University Department of Modeling, Simulation & Visualization Engineering (MSVE) and the Virginia Modeling, Analysis and Simulation Center (VMASC) held the 13th annual Modeling, Simulation & Visualization (MSV) Student Capstone Conference on April 18, 2019. The Conference featured student research and student projects that are central to MSV. Also participating in the conference were faculty members who volunteered their time to impart direct support to their students’ research, facilitated the various conference tracks, served as judges for each of the tracks, and provided overall assistance to the conference. Appreciating the purpose of the conference and working in a cohesive, collaborative effort, resulted in a successful symposium for everyone involved. These proceedings feature the works that were presented at the conference. Capstone Conference Chair: Dr. Yuzhong Shen Capstone Conference Student Chair: Daniel Pere

Deep Learning Based Malware Classification Using Deep Residual Network

The traditional malware detection approaches rely heavily on feature extraction procedure, in this paper we proposed a deep learning-based malware classification model by using a 18-layers deep residual network. Our model uses the raw bytecodes data of malware samples, converting the bytecodes to 3-channel RGB images and then applying the deep learning techniques to classify the malwares. Our experiment results show that the deep residual network model achieved an average accuracy of 86.54% by 5-fold cross validation. Comparing to the traditional methods for malware classification, our deep residual network model greatly simplify the malware detection and classification procedures, it achieved a very good classification accuracy as well. The dataset we used in this paper for training and testing is Malimg dataset, one of the biggest malware datasets released by vision research lab of UCSB