Pragmatic Strategies for Adopting Model-Based Design for Embedded Applications

When transitioning to Model-Based Design for embedded systems development, it is essential to consider an overall plan spanning people, development processes, and tools. A common sense approach when beginning any process improvement activity is to first identify the problem to be solved and then develop a plan to implement the solution. When transitioning to Model-Based Design, performing the transition in an iterative manner—do, learn, adjust, and repeat—has been shown to be most effective. The end goal is a development process where the model is the design, verification is done throughout the development process using simulation, and the implementation of the entire application onto target hardware is highly automated. Faced with design and organizational complexity, time, quality, and cost pressures, the transition is akin to changing a flat tire while moving down the highway. Choosing the right first steps is key to a successful transition. This presentation provides a set of practical strategies for determining the first steps when deploying Model-Based Design and code generation in production development processes.

Next-Generation Electronic Warfare Modeling and Simulation

Dr. Randall Janka, Zeta Associates, Support to Military Operations

A significant shortfall of legacy and modern electronic warfare (EW) systems in use today is an inability to execute simultaneous electronic support (ES) and electronic attack (EA) missions against a diverse and rich target battlespace. The U.S. Army’s Intelligence and Information Warfare Directorate (I2WD) has developed a prototype system that will go a long way toward providing this capability, centered on software control of dynamically allocated distributed hardware. Such a software-based architecture, especially when implementing advanced surgical EW techniques, requires advanced control and scheduling algorithms that can optimize the utilization of available hardware resources, while complying with user-defined policies such as target priorities and threat levels.

Zeta Associates has developed a cognitive radio–inspired EA “Scheduler” that performs autonomous high-level task planning and low-level task element scheduling. To evaluate the Scheduler, Zeta has also developed a simulation framework built with MATLAB^®, Simulink^®, SimEvents®, and Stateflow^® that dynamically models signal instantiations, their life cycles, and EW engagements, and then produces measures of effectiveness for both the Scheduler in particular and the system in general. This presentation and demonstration provides an overview of this modeling and simulation effort.

Air Traffic Control Modeling with SimEvents

Air traffic management systems (ATMSs) are broad applications encompassing avionics, radar systems, and higher-level flight traffic optimization. NEXTGEN is the umbrella program within the U.S. for upgrading ATMSs. This session discusses how SimEvents® can be used to develop a simulation of an air traffic management system. This system monitors various statistics such as average delay for a flight, number of planes in a particular center or in an airport, length of landing and takeoff queues, and so forth. The demo uses a small-scale (three to four airport) model and a flight schedule of a few hundred flights to illustrate the modeling techniques. The demo also shows you how to scale this approach to a large-scale simulation of 20,000 flights and 194 airports. We also show you how to simulate different “what if” scenarios using this large-scale model.

Modeling Electronic Interference Scenarios

This session demonstrates how to model the effects an electronic interferer on an end-to-end communication system using MATLAB and Simulink. The example shows how to interactively select different interfering signal types, power levels, and locations and then see the effects on system-level metrics such as bit error rate. It also shows how to include mitigation algorithms such as adaptive beam-forming.

Solving Data Analysis Challenges Using MATLAB and Statistics Products

Engineers often have significant quantities of data that need to be analyzed. Complicating the need to rapidly analyze the data are anomalies (drop-outs, sensor failures, etc.), which often lead to manual and laborious tasks to discover, categorize, and deal with missing or bad data. An example application is presented in order to demonstrate how MATLAB and statistics add-on products can be used to improve data quality and enhance understanding of the data through quantitative statistical methods..

Model-Based Design for High-Integrity Systems

MathWorks products enable Model-Based Design, which improves engineering productivity with safety-critical systems, including those that must meet DO-178B certification standards. This session presents a workflow to demonstrate how MathWorks tools can be used for requirements validation, algorithm design, traceability, code generation, test generation, formal methods verification, and processor in-the-loop testing. Interfaces to requirements management and configuration management tools are also presented.

Simulink for Beginners

This session introduces Simulink as a graphical environment for developing and modeling dynamic systems. We develop a model of the descent of an Earth-landing vehicle. With the vehicle initially under freefall while subjected to gravity and drag, we will add thrust control to manage descent velocity.

Key Simulink concepts as we elaborate on our model include:

• Blocks, signals, and parameters
• Solver settings
• Multidimensional signals, buses, and muxes
• Model and block callback functions
• Virtual and atomic subsystems
• Algebraic loops
• Libraries and reference models

The session is geared toward inexperienced users, but we also introduce tips, techniques, and new features that current Simulink developers will find useful.

Speeding Up MATLAB

In this technical session we describe strategies and techniques for speeding up your MATLAB applications. Topics include tips on how to optimize the performance of the MATLAB code itself and how to use the MATLAB product family to take advantage of advances in hardware, such as multicore machines and computer clusters. Specifically, you will learn how to:

• Leverage the power of vector and matrix operations in MATLAB
• Identify and address bottlenecks in your code
• Utilize additional processing power available in multicore machines, clusters, and grids

Optimizing Simulation Performance in Simulink

This technical session covers a variety of techniques that you can use to increase the simulation performance of your Simulink models. Two case studies are covered: one that models a hybrid system and one that models a discrete system. The first example explores concepts such as making “quick checks,” profiling execution times, integrating with MATLAB, and dealing with continuous time solvers. The second example addresses additional performance considerations for discrete systems such as leveraging frames and managing visualizations. The presentation ends with a discussion about speeding up batch runs of simulations by taking advantage of advances in hardware, such as multicore machines and computer clusters, and about running very long simulations.