Is It Human or Is It Animal? Target Classification With Doppler

The Role of Radio Signals in Distinguishing Between Targets and MAFAT’s Latest Data Science Challenge. How humans and animals leave different doppler-pulse footprints and MAFAT’s latest data science prize for creating a model that can distinguish between them.

In the world of data science the industry, academic, and government sectors often collide when enthusiasts and experts alike, work together to tackle the challenges we face day-to-day. A prime example of this collaboration is the Israeli Ministry of Defense Directorate of Defense Research & Development (DDR&D)’s MAFAT challenges. A series of data science related challenges with real-world application and lucrative prize pools. In the program’s own words:

The goal of the challenge is to explore the potential of advanced data science methods to improve and enhance the IMOD current data products. The winning method may eventually be applied to real data and the winners may be invited to further collaborate with the IMOD on future projects.

_- [MAFAT Competition Coordinators_](https://competitions.codalab.org/competitions/25389)

Given the recent inception of the program, there haven’t been many challenges yet, however, there are expected to be a variety of challenges ranging from complicated Natural Language Processing puzzles to computer-vision related endeavors.

One such challenge, their second one made available thus far, caught my eye. It involves creating a model for classifying living, non-rigid objects that have been detected by doppler-pulse radar systems. The challenge, “_MAFAT Radar Challenge — Can you distinguish between humans and animals in radar tracks?_” implores competitors to develop a model that can accurately distinguish humans from animals based on a spectrum of radio signals recorded from various doppler-pulse radar sites on various days. If you are interested in participating I recommend visiting thechallenge site before reading on.

So what kind of data are we working with and what do we need to know about it?

An example of the data included for the competition split by Animal/Human and High/Low Signal-Noise-Ratio. The I/Q matrices have been converted into spectrograms for visualization, and the doppler readings have been added in white. As you can see there are some differences present in the files. Images provided by MAFAT. Reposted with Author’s permission.

The key to developing an accurate and competitive model is to first understand the data, how it was sourced, and what it is missing. Included with the competition is 5 CSV files containing the metadata, and 5 pickle files (serializing Python object structure format) containing doppler readings that track the object’s center of mass and slow/fast time readings in the form of a standardized I/Q matrix.

machine-learning radar challenge editors-pick neural-networks

We supply you with world class machine learning experts / ML Developers with years of domain experience who can add more value to your business.

Learn Machine Learning with Python using neural networks with this machine learning beginners course. In this tutorial we will look at taking an existing sol...

We supply you with world class machine learning experts / ML Developers with years of domain experience who can add more value to your business.

This chapter continues the series on Bayesian deep learning. In the chapter we’ll explore alternative solutions to conventional dense neural networks.

We supply you with world class machine learning experts / ML Developers with years of domain experience who can add more value to your business.