Environment and Clutter

Radar propagation, land and sea clutter, atmospheric models, attenuation due to gas, fog, rain and snow

Flat and curved Earth models with and without ITU atmospheric models. Lens effects losses. Radar propagation factor. Sea surface characterization with sea state and permittivity. Land surface characterization with vegetation type and permittivity. Polarization, grazing angle, and velocity-dependent sea and land clutter. ITU and Crane attenuation models due to rain. ITU attenuation model due to cloud and fog. Gunn-East snow model.

Functions

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Atmosphere

`atmositu`	Use ITU reference atmospheres (Since R2021a)
`fspl`	Free space path loss
`gaspl`	RF signal attenuation due to atmospheric gases (Since R2020b)
`lenspl`	Calculate loss due to tropospheric lens effect (Since R2021a)
`radarpropfactor`	One-way radar propagation factor (Since R2021a)
`refractiveidx`	Calculates the refractive index (Since R2021a)
`refractionexp`	CRPL exponential reference atmosphere refraction exponent (Since R2021b)
`tropopl`	Slant-path loss due to atmosphere gaseous absorption (Since R2021a)

Clutter

`billingsleyicm`	Billingsley’s intrinsic clutter motion (ICM) model (Since R2021a)
`clutterSurfaceRCS`	Surface clutter radar cross section (Since R2021a)
`clutterSurfaceRangeDopplerRCS`	Surface RCS as a function of range and Doppler (Since R2022b)
`clutterGenerator`	Add clutter generator for radar (Since R2022a)
`earthSurfacePermittivity`	Permittivity and conductivity of earth surface materials (Since R2020a)
`landreflectivity`	Reflectivity of land surface (Since R2021a)
`landroughness`	Surface height standard deviation for land (Since R2021a)
`landSurface`	Add land surface to radar scenario (Since R2022a)
`seareflectivity`	Normalized sea surface reflectivity (Since R2021a)
`searoughness`	Surface height standard deviation for sea (Since R2021a)
`seaSpectrum`	Sea surface omnidirectional motion spectrum model (Since R2022a)
`seaSurface`	Add sea surface to radar scenario (Since R2022a)
`surfacegamma`	Gamma value for different terrains (Since R2021a)
`surfaceReflectivity`	Normalized reflectivity of surface (Since R2022a)
`surfaceReflectivityLand`	Normalized reflectivity of land surface (Since R2022a)
`surfaceReflectivityCustom`	Normalized reflectivity of custom surface (Since R2022a)
`surfaceReflectivitySea`	Normalized reflectivity of sea surface (Since R2022a)
`tirempl`	Path loss using Terrain Integrated Rough Earth Model (TIREM) (Since R2019b)
`clutterVolumeRCS`	Radar cross-section of volume clutter (Since R2021a)
`surfclutterrcs`	Surface clutter radar cross section (RCS) (Since R2021a)

Weather

`cranerainpl`	RF signal attenuation due to rainfall using Crane model (Since R2020a)
`fogpl`	RF signal attenuation due to fog and clouds
`rainpl`	RF signal attenuation due to rainfall
`snowpl`	Path loss due to wet snow (Since R2021a)

Scenario Geometry

`depressionang`	Depression angle of surface target (Since R2021a)
`effearthradius`	Effective earth radius (Since R2021a)
`grazingang`	Grazing angle of surface target (Since R2021a)
`horizonrange`	Horizon range (Since R2021a)
`llarangeangle`	Propagation range between two geolocations (Since R2022b)
`el2height`	Convert target elevation angle to height (Since R2021a)
`height2el`	Convert target height to elevation angle (Since R2021a)
`height2range`	Convert target height to propagated range (Since R2021b)
`height2grndrange`	Convert target height to ground range (Since R2021b)
`range2height`	Convert propagated range to target height (Since R2021b)
`slant2range`	Convert slant range to propagated range (Since R2022b)

Featured Examples

Predict Surface Clutter Power in Range-Doppler Space

Calculate the radar cross section (RCS) of a surface clutter seen by a pulse-Doppler radar system.

Open Live Script

Modeling the Propagation of Radar Signals

Model RF propagation effects such as free space path loss, atmospheric attenuation due to rain, fog and gas, and multipath propagation due to bounces on the ground.

Open Script

Simulate a Coastal Surveillance Radar

Simulate a stationary land-based radar that collects returns from extended targets at sea.

Open Live Script

Maritime Radar Sea Clutter Modeling

Introduce a sea clutter simulation for a maritime surveillance radar system.

Open Live Script

$Modeling Target Position Errors Due to Refraction$

Modeling Target Position Errors Due to Refraction

Explore some environmental factors that are beyond a radar system designer's control and can result in losses and estimation errors.

Open Live Script

Planning Radar Network Coverage over Terrain

Plan a radar network using propagation modelling over terrain. DTED level-1 terrain data is imported for a region that contains five candidate monostatic radar sites. The radar equation is used to determine whether target locations can be detected, where additional path loss is calculated using either the Longley-Rice propagation model or Terrain Integrated Rough Earth Model™ (TIREM™). The best three sites are selected for detection of a target that is flying at 500 meters above ground level. The scenario is updated to model a target that is flying at 250 meters above ground level. Radar coverage maps are shown for both scenarios.

Open Live Script

Radar Performance Analysis over Terrain

The performance of a radar system can depend on its operating environment. This example shows how radar detection performance improves as target elevation increases above the terrain.

Open Live Script

Radar Vertical Coverage over Terrain

Define an L-band radar system in the presence of heavy clutter and visualize its three-dimensional vertical coverage over terrain.

Open Live Script

$Simulating Radar Systems with Atmospheric Refraction$

Simulating Radar Systems with Atmospheric Refraction

The performance of a radar system is closely tied to the environment in which it operates. This example will discuss one of the environmental factors that is outside of the system designer's control, namely the atmosphere. This example will focus on refraction effects due to the atmosphere and its impact on target parameter estimation. As a part of this example, two different sensor types, one a waveform-level sensor that produces IQ and the other a statistical measurement-level sensor that produces detections, will be presented to show how to work at different levels of abstraction and how the two sensors produce similar results when configured appropriately.

Open Live Script