Blind Source Separation of Radar Signals in Time Domain Using Deep Learning

Identification and further analysis of radar emitters in a contested environment requires detection and separation of incoming signals. If they arrive from the same direction and at similar frequencies, deinterleaving them remains challenging. A solution to overcome this limitation becomes increasingly important with the advancement of emitter capabilities. We propose treating the problem as blind source separation in time domain and apply supervisedly trained neural networks to extract the underlying signals from the received mixture. This allows us to handle highly overlapping and also continuous wave (CW) signals from both radar and communication emitters. We make use of advancements in the field of audio source separation and extend a current state-of-the-art model with the objective of deinterleaving arbitrary radio frequency (RF) signals. Results show, that our approach is capable of separating two unknown waveforms in a given frequency band with a single channel receiver.

Hybrid Baseband Simulation for Single-Channel Radar-Based Indoor Localization System

Indoor localization with chirp sequence radar at millimeter wavelength offers high localization accuracy at low system cost. We propose a hybrid radar baseband signal simulator for our novel single-channel radar-based indoor localization system consisting of an active radar and passive reflectors as references. By combining ray tracing channel simulations with real measurements of the two-way antenna gain of the radar and accurate simulation of the radar cross section of chosen reflectors, realistic modeling of the baseband receive signal in complex scenarios is achieved.

Indoor Positioning based on Active Radar Sensing and Passive Reflectors: Concepts & Initial Results

To navigate reliably in indoor environments, an industrial autonomous vehicle must know its position. However, current indoor vehicle positioning technologies either lack accuracy, usability or are too expensive. Thus, we propose a novel concept called local reference point assisted active radar positioning, which is able to overcome these drawbacks. It is based on distributing passive retroreflectors in the indoor environment such that each position of the vehicle can be identified by a unique reflection characteristic regarding the reflectors. To observe these characteristics, the autonomous vehicle is equipped with an active radar system. On one hand, this paper presents the basic idea and concept of our new approach towards indoor vehicle positioning and especially focuses on the crucial placement of the reflectors. On the other hand, it also provides a proof of concept by conducting a full system simulation including the placement of the local reference points, the radar-based distance estimation and the comparison of two different positioning methods. It successfully demonstrates the feasibility of our proposed approach.