This repository holds the dataset that accompanies our IEEE Radar paper, as well as coding to minimally load and process the dataset.
Using AISTAP-SIM for your research? Please cite the following publication:
@inproceedings{vega2024radar,
author={Vega, Dalton and Newey, Michael and Barrett, David and Axelrod, Alan and Myne, Anu and Wollaber, Allan},
title={STAP-Informed Neural Network for Radar Moving Target Indicator},
booktitle={Proc. {IEEE} Intl. Radar Conf.},
year={2024},
month={May},
}
First, get a working copy of the code and data onto your machine:
- Clone this repository, e.g.,
git clone git@github.com:mit-ll/AISTAP-SIM.git - Download the dataset from the releases
To get started using the data, we are providing a data reader as a python package and a Matlab file.
For Matlab, consult the scripts/sampleread_matlab.m file for a few examples of loading
data and plotting.
Create a virtual environment (using conda, venv, etc.). We tested in conda using python v3.11.
Then, from the project root directory, run:
pip install .
Use the -e switch if you would like to edit the code in-place.
Finally, run one of the example scripts in the scripts folder. For example, from the
root directory, run
python scripts/run_train_example.pyThe dataset is distributed as Matlab v7.3 formatted .mat files. These are also
parseable as HDF5-formatted files. Each dataset is composed of training set, a test
set, and a small "sample" file to get started.
| Name | Filenames | Description |
|---|---|---|
| Ground Clutter train set | simMed/simMed_train??.mat simMed/simMed_test.mat simMed/simMed_sample.mat |
Standard GMTI dataset with low wind (train, test, sample files) |
| Windy Ground Clutter | simWind/simWind_train??.mat simWind/simWind_test.mat simWind/simWind_sample.mat |
Same as ground clutter but much higher wind speeds |
| Noise Only | simNoise/simNoiseOnly_train??.mat simNoise/simNoiseOnly_test.mat simNoise/simNoiseOnly_sample.mat |
No ground clutter, only simulated noise |
Note that the ?? in the training files indicates a numeral (01-16), as the files had to be broken up to fit onto github.
The data directory structure with sample files can be downloaded in the
releases tab as filename sampledata.zip.
The radar simulator models a two-meter long Ku band antenna with a 50 ms Ground Moving Target Indicator (GMTI) coherent processing interval (CPI). The range resolution is set to 100 meters. We built our model on top of a simple baseline simulation based on the stochastic features of radar clutter and noise. We modeled both the noise and the clutter with multiplicative speckle noise that is circularly complex and normally distributed. To simulate radar returns, we employ the far-field approxima-tion and assume a non-squinted (broadside) radar collection geometry. We also include a small amount of both magnitude and phase noise. To challenge STAP, we implemented heterogeneous clutter. We created a randomized map that covers the imaged region, and represents the percentage of trees, grass, roads, and buildings in each pixel. The power for the speckle in each region is set according to measured values from the a collection over Joint Base Cape Cod and is also hand-tuned. Motion of trees in the wind relies on empirical modelling and measurement, and is hand-tuned to match realistic data. Clutter power is also randomly modulated in a randomly selected region of the range extent in a subset of the images. This results in a bimodal distribution of clutter powers across the image. Targets were injected into the clutter and noise data as ideal point scatterers. We created three datasets for evaluation. The first has a 7 pixel wide clutter ridge, with 30 dB average target SNR and 15 dB average peak clutter to noise ratio, which we refer to as "Ground Clutter". In a second dataset, we increased the level of the wind significantly, providing a much more pronounced blur effect in the image. Finally, we have a dataset with Gaussian noise but no clutter called "No Clutter".
For each simulated image, we vary simulation parameters including:
- radar sensor speed
- number of targets
- presence and location of non-homogeneous clutter
- noise level
- clutter level
Doppler coordinates were randomly sampled from one of 2 possible distributions: from one that covered the entire Doppler range uniformly, or from one that was highly concentrated on the clutter ridge. We tapered all of the data in range and Doppler with a 35 dB Taylor window to reduce sidelobes. We specified a range resolution of 100 meters and a cross-range resolution that varied randomly per image about 140 meters. We simulate a 2 meter antenna with 6 evenly spaced antenna channels at a 50 km standoff from beam center.
Dimensions are as shown when loading with LazyMatfileReader.
If loaded with pymatreader, the dimension order is reversed.
Additionally, the order is reversed when read into Matlab.
The simulated data are nominally in an NxCxDxR-sized arrays in which
N= number of images (2048)C= channel dimension size (6)D= Doppler dimension size (64)R= range dimension size (1024)
There are two primary arrays in each file that contain the raw "imagery" data and the target-only data:
rd_img : (N, C, D, R), complex128 array
Radar image input data containing clutter, noise, and injected targets
rd_targ_only : (N, C, D, R), complex128 array
Training labels containing target response onlyAdditionally, there is rich metadata associated with each of these datasets
that are described in their corresponding dictionaries (or Matlab structures),
including metadata for the entire file and meta_per_image for each
of the N images. Below is a brief description of each field.
metadata : dict
Dictionary of dataset metadata containing the following fields:
lambda_c Center wavelength of radar
midp_ch Mid channel cell
range_nominal Nominal range to dwell center
midp_dop Mid doppler cell
num_antenna_channels Number of antenna channels
channel_spacing Meters between antenna channels
fc Center frequency of band
Vs_nominal Nominal sensor speed
range_taper Taper in the range dimension (applied in fft of range)
midp_range Mid range cell
dop_taper Taper in the Doppler dimension (applied in fft of Doppler)
antenna_length Length of the antenna
meta_per_image : list[dict]
List of dictionaries for metadata pertanining to each image, containing the following fields
truth_pix_dop_axis Conversion between targ_pix values and Doppler pixels in the image (e.g. is the first index 1 or 0)
Ntrue Number of targets in the data
Vs Estimated sensor velocity component perpendicular to the range vector to dwell center (m/s).
CPI Coherent processing interval
range_step Range pixel spacing (m)
rr_axis Range rate axis values
targ_pix_dop Doppler of injected targets (in pixel units)
targ_pix_xr Cross-range of injected targets (in pixel units)
truth_pix_range_axis Conversion between targ_pix values and range pixels in the image (e.g. is the first index 1 or 0)
rs_axis Range axis values
xrange_step Cross-range pixel spacing (m)
xr_axis Cross-range axis values
dop_beamwidth_pred Predicted nominal beamwidth of the radar at dwell center in Doppler pixels
Vstrue True sensor velocity component perpendicular to the range vector to dwell center (m/s).
range_center Range to dwell center (m)
targ_pix_range Range of injected targets (in pixel units)
dop_axis Doppler axis values
DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.
© 2024 MASSACHUSETTS INSTITUTE OF TECHNOLOGY
Subject to FAR 52.227-11 – Patent Rights – Ownership by the Contractor (May 2014) SPDX-License-Identifier: MIT
DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited. This material is based upon work supported by the Under Secretary of Defense for Research and Engineering under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Under Secretary of Defense for Research and Engineering. Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.
The software/firmware is provided to you on an As-Is basis.