Radar Signal Processing using Artificial Neural Networks
Time: Fri 2023-10-13 10.00
Location: F3 (Flodis), Lindstedtsvägen 26 & 28, Stockholm
Video link: https://kth-se.zoom.us/j/66647545115
Language: English
Subject area: Electrical Engineering
Doctoral student: Alexander Karlsson , Teknisk informationsvetenskap
Opponent: Professor Maria Sabrina Greco,
Supervisor: Professor Magnus Jansson, Teknisk informationsvetenskap; Mikael Hämäläinen, ; Dr. Henrik Holter,
Reserach funder: SAAB
QC 20230914
Abstract
This thesis combines radar signal processing, with data driven artificial neuralnetwork (ANN) methods. Signal processing algorithms are often based on modelingassumptions of how the data was formed. In some cases, such models are sufficientfor designing good, or even optimal, solutions.In many cases however, these models may be too complicated to form analyticalsolutions; be too simplified, such that practical results may differ significantly fromwhat was theoretically indicated; be unknown in the sense that one of several knownmodels or parameter values may fit the data, but we do not know which; or be toocomplex such that the solution will be too heavy to compute.Data driven ANN methods provide a simple way of bridging these gaps. Wedemonstrate this in four different studies, where we make use of radar data modelsto formulate data driven solutions that are both accurate and computationallyefficient.We compare ANN based results to computationally demanding least squares,and exhaustive matched filtering approaches. We show that the performance of theANNs are comparable to these, but come at a fraction of the computational load.We train ANNs on data sampled from models using a wide range of parametervalues. This naturally handles drifts and unknown parameter values, which maychange the data, but not the desired prediction. We show that an ANN classifiertrained on data from simple models may in practice perform significantly worsethan what was expected from theory. We improve this by combining a limitedamount of real data with synthetic model data. In all cases, we make use of modelsthat are simple to evaluate. They are however not simple to analyze for the purposeof creating analytical solutions.In particular we present a method for achieving non-coherent pulse compressionthat resolves targets within a single pulse width. We present a method fordetecting weak target trajectories that does not require prior assumptions ontarget acceleration, the signal-to-noise ratio, etc. We present different methodsof incorporating imperfect model data in the training of classifiers of drone andnon-drone targets. Finally we present a method for estimating the path differencein sea surface multipath propagation, for the purpose of target tracking.