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Range estimation

The `phased.RangeEstimator`

System
object™ estimates
the ranges of targets. Input to the estimator consists of a range-response
or range-Doppler response data cube, and detection locations from
a detector. When information about clusters of detections is available,
the ranges are computed using cluster information. Clustering associates
multiple detections into one extended detection.

To compute the detections for a range-response or range-Doppler cube:

Define and set up a range estimator using the Construction procedure that follows.

Call the

`step`

method to compute the range, using the properties you specify for the`phased.RangeEstimator`

System object.

Instead of using the `step`

method to perform
the operation defined by the System
object, you can call the object
with arguments, as if it were a function. For example, ```
y
= step(obj,x)
```

and `y = obj(x)`

perform
equivalent operations.

`estimator = phased.RangeEstimator`

creates
a range estimator System
object, `estimator`

.

`estimator = phased.RangeEstimator(`

creates
a System
object, `Name`

,`Value`

)`estimator`

, with each specified
property `Name`

set to the specified `Value`

.
You can specify additional name and value pair arguments in any order
as (`Name1,Value1`

,...,`NameN,ValueN`

).

step | Estimate target range |

Common to All System Objects | |
---|---|

`clone` | Create System object with same property values |

`getNumInputs` | Expected number of inputs to a System object |

`getNumOutputs` | Expected number of outputs of a System object |

`isLocked` | Check locked states of a System object (logical) |

`release` | Allow System object property value changes |

The `phased.RangeEstimator`

System
object estimates
the range of a detection by following these steps:

Input a range-processed response data cube obtained from either the

`phased.RangeResponse`

or`phased.RangeDopplerResponse`

System object. The first dimension of the cube represents the fast-time or equivalent range of the returned signal samples. Only this dimension is used to estimate detection range. All others are ignored.Input a matrix of detection indices that specify the location of detections in the data cube. Each column denotes a separate detection. The row entries designate indices into the data cube. You can obtain detection indices as an output of the

`phased.CFARDetector`

or`phased.CFARDetector2D`

detectors. To return these indices, set the`OutputFormat`

property of either CFAR detector to`'Detection index'`

.Optionally input a row vector of cluster IDs. This vector is equal in length to the number of detections. Each element of this vector assigns an ID to a corresponding detection. To form clusters of detections, the same ID can be assigned to more than one detection. To enable this option, set the

`ClusterInputPort`

property to`true`

.When

`ClusterInputPort`

is`false`

, the object computes the range for each detection. The algorithm finds the response values at the detection location and at two adjacent indices in the cube along the range dimension. Then, the algorithm fits a quadratic curve to the magnitudes of the range response at these three locations and finds the location of the peak. When detections occur at the first or last sample in the range dimension, the range response is estimated from a two-point centroid. The estimation is at the location of the detection index and at the sample adjacent to the detection index.When

`ClusterInputPort`

is`true`

, the object computes range for each cluster. The algorithm finds the indices of the largest response value in the cluster and fits a quadratic formula to that detection in the same way as for individual detections.Convert the fractional index values of the fitted peak locations to range. To convert the indices, choose appropriate units for the

`rnggrid`

input argument of the`step`

method. You can use values for`rnggrid`

obtained from either the`phased.RangeResponse`

or`phased.RangeDopplerResponse`

System objects.

The object computes the estimated range variance using the Ziv-Zakai bound.

[1] Richards, M. *Fundamentals of Radar Signal
Processing*. 2nd ed. McGraw-Hill Professional Engineering,
2014.

[2] Richards, M., J. Scheer, and W. Holm. * Principles
of Modern Radar: Basic Principles*. SciTech Publishing,
2010.

`phased.CFARDetector`

|`phased.CFARDetector2D`

|`phased.DopplerEstimator`

|`phased.RangeDopplerResponse`

|`phased.RangeResponse`

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