Your SlideShare is downloading. ×
Distributed Radar Tracking Simulation Project
Distributed Radar Tracking Simulation Project
Distributed Radar Tracking Simulation Project
Distributed Radar Tracking Simulation Project
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Distributed Radar Tracking Simulation Project

124

Published on

Published in: Education, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
124
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
3
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 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

×