Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.666085
Title: Single data set detection for multistatic Doppler radar
Author: Shtarkalev, Bogomil Iliev
ISNI:       0000 0004 5353 5498
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2015
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Abstract:
The aim of this thesis is to develop and analyse single data set (SDS) detection algorithms that can utilise the advantages of widely-spaced (statistical) multiple-input multiple-output (MIMO) radar to increase their accuracy and performance. The algorithms make use of the observations obtained from multiple space-time adaptive processing (STAP) receivers and focus on covariance estimation and inversion to perform target detection. One of the main interferers for a Doppler radar has always been the radar’s own signal being reflected off the surroundings. The reflections of the transmitted waveforms from the ground and other stationary or slowly-moving objects in the background generate observations that can potentially raise false alarms. This creates the problem of searching for a target in both additive white Gaussian noise (AWGN) and highly-correlated (coloured) interference. Traditional STAP deals with the problem by using target-free training data to study this environment and build its characteristic covariance matrix. The data usually comes from range gates neighbouring the cell under test (CUT). In non-homogeneous or non-stationary environments, however, this training data may not reflect the statistics of the CUT accurately, which justifies the need to develop SDS methods for radar detection. The maximum likelihood estimation detector (MLED) and the generalised maximum likelihood estimation detector (GMLED) are two reduced-rank STAP algorithms that eliminate the need for training data when mapping the statistics of the background interference. The work in this thesis is largely based on these two algorithms. The first work derives the optimal maximum likelihood (ML) solution to the target detection problem when the MLED and GMLED are used in a multistatic radar scenario. This application assumes that the spatio-temporal Doppler frequencies produces in the individual bistatic STAP pairs of the MIMO system are ideally synchronised. Therefore the focus is on providing the multistatic outcome to the target detection problem. It is shown that the derived MIMO detectors possess the desirable constant false alarm rate (CFAR) property. Gaussian approximations to the statistics of the multistatic MLED and GMLED are derived in order to provide a more in-depth analysis of the algorithms. The viability of the theoretical models and their approximations are tested against a numerical simulation of the systems. The second work focuses on the synchronisation of the spatio-temporal Doppler frequency data from the individual bistatic STAP pairs in the multistatic MLED scenario. It expands the idea to a form that could be implemented in a practical radar scenario. To reduce the information shared between the bistatic STAP channels, a data compression method is proposed that extracts the significant contributions of the MLED likelihood function before transmission. To perform the inter-channel synchronisation, the Doppler frequency data is projected into the space of potential target velocities where the multistatic likelihood is formed. Based on the expected structure of the velocity likelihood in the presence of a target, a modification to the multistatic MLED is proposed. It is demonstrated through numerical simulations that the proposed modified algorithm performs better than the basic multistatic MLED while having the benefit of reducing the data exchange in the MIMO radar system.
Supervisor: Mulgrew, Bernie; Davies, Michael Sponsor: Engineering and Physical Sciences Research Council (EPSRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.666085  DOI: Not available
Keywords: MIMO ; STAP ; doppler detection ; multiple-input multiple-output ; single data set detection
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