simuTargetClutter

The function simulates a target signal under clutter environment.

Simulation, estimation, prediction procedure, and model identification methods for nonlinear time series analysis, including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, nonlinearity tests, Kalman filters and various sequential Monte Carlo methods. More examples and details about this package can be found in the book "Nonlinear Time Series Analysis" by Ruey S. Tsay and Rong Chen, John Wiley & Sons, 2018 (ISBN: 978-1-119-26407-1).

Xialu Liu

Nonlinear Time Series Analysis

Ruey Tsay

Rong Chen

simuTargetClutter function

<dl><dt>nobs</dt>
<dd>the number observations.</dd>
<dt>pd</dt>
<dd>the probability to observe the true signal.</dd>
<dt>ssw</dt>
<dd>the standard deviation in the state equation.</dd>
<dt>ssv</dt>
<dd>the standard deviation for the observation noise.</dd>
<dt>xx0</dt>
<dd>the initial location.</dd>
<dt>ss0</dt>
<dd>the initial speed.</dd>
<dt>nyy</dt>
<dd>the dimension of the data.</dd>
<dt>yrange</dt>
<dd>the range of data.</dd></dl>

Arguments

Simulate A Moving Target in Clutter — simuTargetClutter

<dl>

<dt>nobs</dt>
<dd>the number observations.</dd>


<dt>pd</dt>
<dd>the probability to observe the true signal.</dd>


<dt>ssw</dt>
<dd>the standard deviation in the state equation.</dd>


<dt>ssv</dt>
<dd>the standard deviation for the observation noise.</dd>


<dt>xx0</dt>
<dd>the initial location.</dd>


<dt>ss0</dt>
<dd>the initial speed.</dd>


<dt>nyy</dt>
<dd>the dimension of the data.</dd>


<dt>yrange</dt>
<dd>the range of data.</dd>

</dl>

simuTargetClutter: Simulate A Moving Target in Clutter

Description

Usage

Value

Arguments

References

Examples