DISTRIBUTED RADAR TRACKING SIMULATION USING MATLAB

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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;

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);

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 seconds\n', 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);

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