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Algorithm: Hypothesizing Kalman Filter Evaluation

Simulates "nNodes"-runs with 1 to nNodes measuring and the other ones
doing nothing. The fused result is compared for different algorithms.

@nNodes number of nodes in the network
@nTimeSteps number of estimation time steps
@relDeviationInAss ratio between actually utilized models and
assumption for the DKF/HKF and the federated ... (show more)
Tags: HKF t2tf filtering DKF estimator distributed
Usage: Algorithm is public.
Viewed 2975 times, called 102 times
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3 votes
Marc Reinhardt
06/27/2012 7:43 p.m. (version #1)

Run Algorithm

: Number of nodes in the network
: Number of estimation time steps
: Ratio between actually utilized models and assumption for the DKF/HKF and the federated KF
: Allow cached result?

Please Wait

Computation is running...


showing plot # of

Result Value(s)


Resource Usage

Execution of the algorithm took seconds.
Peak memory usage for running the algorithm was kilobytes (total usage: kilobytes).

Run ID / Link

ID of this run:

Result from Cache

Result has been delivered from cache and computed on (UTC).

Matlab log




Using this algorithm in your local MATLAB environment is easy: Click here for instructions!

Usage Instructions for CloudRunner Client

  1. Download the CloudRunner Client by clicking here and place the downloaded file in your MATLAB working directory.

  2. Inside MATLAB, initialize the CloudRunner Client by calling CloudRunner:
    >> CloudRunner

    A login dialog will prompt for your CloudRunner mail address and password. For a start, you can leave the dialog empty and just click "Connect".

    Alternatively, you can provide the login credentials (or empty strings to skip login) as a parameter and hence skip the login dialog. This is useful when using CloudRunner in non-interactive scripts.
    >> CloudRunner('', 'password')

  3. Select this algorithm by its URL. Selecting an algorithm creates the lambda function that proxies calls to the algorithm to the server for execution:
    >> CloudRunnerSelect('')

    For the sake of convenience, you can also use the algorithm ID instead of its URL for public algorithms.

  4. Call functions from the algorithm like any regular local function.

Note: You can find further information on the help page.


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