This document describes a distributed radar tracking simulation using MATLAB and the Parallel Computing Toolbox. It divides a Monte Carlo simulation of tracking an aircraft's path across multiple radar station tasks. The tasks are created and submitted to a cluster, and the results are retrieved and concatenated upon completion. Plotting the standard deviation of estimation errors over time allows comparing the distributed computation time to a sequential simulation.

1. DISTRIBUTED RADAR TRACKING SIMULATION USING MATLAB
Our online Tutors are available 24*7 to provide Help with Distributed Radar Tracking Simulation
Homework/Assignment or a long term Graduate/Undergraduate Distributed Radar Tracking
Simulation Project. Our Tutors being experienced and proficient in Distributed Radar Tracking
Simulation ensure to provide high quality Distributed Radar Tracking Simulation Homework
Help. Upload your Distributed Radar Tracking Simulation Assignment at ‘Submit Your
Assignment’ button or email it to info@assignmentpedia.com. You can use our ‘Live Chat’
option to schedule an Online Tutoring session with our Distributed Radar Tracking Simulation
Tutors.
PARALLEL COMPUTING TOOLBOX
This example uses the Parallel Computing Toolbox™ to perform a Monte Carlo simulation of a
radar station that tracks the path of an aircraft.
Load the Example Settings and the Data
The example uses the default profile when identifying the cluster to use. The profiles
documentation explains how to create new profiles and how to change the default profile.
Customizing the Settings for the Examples in the Parallel Computing Toolbox for instructions on
how to change the example difficulty level or the number of tasks created.
[difficulty, myCluster, numTasks] = pctdemo_helper_getDefaults();
We define the number of simulations and the length of each simulation in pctdemo_setup_radar.
The example difficulty level controls the number of simulations we perform. The
function pctdemo_setup_radar also shows examples of the different paths that the aircraft can
take, as well as the error in the estimated aircraft location. You can view the code for
pctdemo_setup_radar
for full details.
[fig, numSims, finishTime] = pctdemo_setup_radar(difficulty);
startClock = clock;

2. Divide the Work into Smaller Tasks
The computationally intensive part of this example consists of a Monte Carlo simulation and we
use the functionpctdemo_helper_split_scalar to divide the numSims simulations among
the numTasks tasks.
[taskSims, numTasks] = pctdemo_helper_split_scalar(numSims, numTasks);
fprintf(['This example will submit a job with %d task(s) ' ...
'to the cluster.n'], numTasks);
This example will submit a job with 4 task(s) to the cluster.
Create and Submit the Job
Let us create the simulation job and the tasks in the job. We let
task i perform taskSims(i) simulations. Notice that the task function is the same function that you
used in the sequential example. You can view the code for pctdemo_task_radar for full details.
job = createJob(myCluster);
for i = 1:numTasks
createTask(job, @pctdemo_task_radar, 1, {taskSims(i), finishTime});
end
We can now submit the job and wait for it to finish.
submit(job);

3. wait(job);
Retrieve the Results
Let us obtain the job results, verify that all the tasks finished successfully, and then delete the
job. fetchOutputs will throw an error if the tasks did not complete successfully, in which case we
need to delete the job before throwing the error.
try
jobResults = fetchOutputs(job);
catch err
delete(job);
rethrow(err);
end
Let us format the results. Notice how we concatenate all the arrays in jobResults along the
columns, thus obtaining a matrix of the size (finishTime + 1)-by-numSims.
residual = cat(2, jobResults{:});
We have now finished all the verifications, so we can delete the job.
delete(job);
Measure the Elapsed Time
The time used for the distributed computations should be compared against the time it takes to
perform the same set of calculations in theSequential Radar Tracking Simulation example. The
elapsed time varies with the underlying hardware and network infrastructure.
elapsedTime = etime(clock, startClock);
fprintf('Elapsed time is %2.1f secondsn', elapsedTime);
Elapsed time is 31.1 seconds
Plot the Results
We use the simulation results to calculate the standard deviation of the range estimation error
as a function of time. You can view the code for pctdemo_plot_radar for full details.
pctdemo_plot_radar(fig, residual);

4. visit us at www.assignmentpedia.com or email us at info@assignmentpedia.com or call us at +1 520 8371215