## About

54

Publications

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Introduction

Murat Uney is a lecturer in statistical signal processing and data science at the School of Electrical Engineering, Electronics and Computer Science of the University of Liverpool.
His research interests are in probabilistic models and statistical methodology in signal, data, and information processing, and, machine learning. Achievements have included leading-edge scientific publications that were carried to higher technology readiness levels for industrial exploitation.

Additional affiliations

March 2021 - present

February 2018 - March 2021

June 2013 - present

## Publications

Publications (54)

Multi-sensor state space models underpin fusion applications in networks of sensors. Estimation of latent parameters in these models has the potential to provide highly desirable capabilities such as network self-calibration. Conventional solutions to the problem pose difficulties in scaling with the number of sensors due to the joint multi-sensor...

A recent trend in distributed multi-sensor fusion is to use random finite set filters at the sensor nodes and fuse the filtered distributions algorithmically using their exponential mixture densities (EMDs). Fusion algorithms which extend the celebrated covariance intersection and consensus based approaches are such examples. In this article, we an...

In this work, we propose a data driven trajectory forecasting algorithm that utilizes both recorded historical and streaming trajectory observations. The algorithm performs Bayesian inference on a directed graph the walks on which represent stochastic change point models of trajectory classes. Parameter distributions of these models are learnt from...

Multiple hypothesis tracking (MHT) is a computational procedure for recursively estimating multi-object configurations and states from measurements with association uncertainties , noise, false alarms and less than one probability of detection. From a probabilistic modelling perspective, the complete multi-object tracking (MT) model is intractable...

In this work, we consider joint micro-Doppler signature estimation and track-before-detect for detecting and classifying manoeuvring and small rotary-wing aircraft (e.g., drones) with a phased array radar receiver. Such aircraft induce low signal-to-noise ratio (SNR) reflections which complicates their detection. These difficulties are exacerbated...

This paper develops optimal procedures for point estimation with Bernoulli filters. These filters are of interest to radar and sonar surveillance because they are designed for stochastic targets that can enter and exit the surveillance region at random instances. Because of this property they are not served by the minimum mean square estimator, whi...

This work proposes a fast trajectory forecasting algorithm to use with automatic identification system (AIS) broadcasts of vessels. The algorithm involves fast sub-optimal model parameter estimation from AIS messages and the computation of Gaussian location predictions for a series of future timestamps. The underlying trajectory model is a stochast...

Passive sensors pose challenges in the localisation and tracking of targets due to the inherent ambiguity in the source range-specifically, accurate 2-D localisation using only a single sensor is not guaranteed. On the other hand, the underwater domain exhibits certain characteristics that induce some structure to the problem that help reduce the r...

Bi-static sensing, where the transmitter and receiver of sensors are separately located, underlies a wide range of collaborative sensing systems. Bi-static detections generally feature a signal time-of-flight (ToF) and an angle-of-arrival (AoA). The current practice in multi-object tracking uses the bi-static geometry to map these pairs onto a sele...

Cooperation among multiple autonomous surface and underwater vehicles is an important capability for detection and tracking of underwater objects. Cooperative autonomy in the underwater environment, however, is challenged by the communication bandwidth. In this work, we propose a selective communication scheme that underpins collaborative surveilla...

This presentation accompanies the Fusion 2019 article with the same title.

ICASSP 2019 presentation slides.

SSPD 2019 Presentation slides.

In this work, we consider maximum likelihood estimation of parameters in a stochastic trajectory model. The velocity paths are generated from an Ornstein-Uhlenbeck process and thus revert to a latent expected value. In addition to this expected velocity, parameters that specify the reversion characteristics and the process noise covariance determin...

In this work, we consider the estimation of micro-Doppler signatures of small rotary-wing aircraft such as drones using a pulsed radar with a uniform planar array (UPA) receiver in order to identify/classify them within the processing performed for their detection. Such objects induce low signal to noise ratio (SNR) reflections which complicate the...

In this work, we consider the problem of algo-rithmically predicting rendezvous among vessels based on their trajectory forecasts in a maritime environment. The problem is treated as hypothesis testing on the expected value of the distance between trajectories. We relate this quantity to the first and second degree Wasserstein distances between tra...

These are the presentation slides for "Enabling self-configuration of fusion networks via scalable opportunistic sensor calibration", Proceedings Volume 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 2018

The range of applications in which sensor networks can be deployed depends heavily on the ease with which sensor locations/orientations can be registered and the accuracy of this process. We present a scalable strategy for algorithmic network calibration using sensor measurements from non-cooperative objects. Specifically, we use recently developed...

In this work, we consider the problem of synchronising separately located transmitters and a staring array receiver that also has a local transmitter. The acknowledged benefits of using separate transmitters in active sensing are often undermined by the difficulty in accurate synchronisation of the receiver and the transmitters. In this work, we pr...

In this work, we consider the detection of manoeuvring small objects with radars. Such objects induce low signal to noise ratio (SNR) reflections in the received signal. We consider both co-located and separated transmitter/receiver pairs, i.e., mono-static and bi-static configurations, respectively, as well as multi-static settings involving both...

This is the UDRC statistical signal processing summer school handouts I use to cover optimal filtering of stochastic processes (i.e., Wiener filters), adaptive filters that configure themselves as the Wiener filter, and, optimal signal detection in noise. The techniques are covered with references to their historical and current impact - from Norbe...

In this work, we focus on the detection of manoeuvring low signal to noise ratio (SNR) objects in multiple collaborating radars. Collaboration involves having the knowledge of the locations of the transmitters and their transmission characteristics up to a synchronisation term which has to be estimated during the operation. We propose a local proce...

In this work, we are interested in detecting manoeuvring objects in high noise background using an active sensor with a uniform linear array (ULA) receiver and propose a joint pulse integration and trajectory estimation algorithm. This algorithm allows us to detect low SNR objects by integrating multiple pulse returns while taking into account the...

We consider geographically distributed sensor platforms with limited field of views (FoVs) networked together in order to cover a larger surveillance region. Each sensor has a partially overlapping FoV with its neighbours, and, collects both target originated and spurious measurements. We are interested in estimating the locations of the sensors in...

Motivated by object tracking applications with networked sensors, we consider multi sensor state space models. Estimation of latent parameters in these models requires centralisation because the parameter likelihood depend on the measurement histories of all of the sensors. Consequently, joint processing of multiple histories pose difficulties in s...

Motivated by object tracking applications with networked sensors, we consider multi sensor state space models. Estimation of latent parameters in these models requires centralisation because the parameter likelihood depend on the measurement histories of all of the sensors. Consequently, joint processing of multiple histories pose difficulties in s...

We consider self-localisation of networked sensor platforms which are located disparately and collect cluttered and noisy measurements from an unknown number of objects (or, targets). These nodes perform local filtering of their measurements and exchange posterior densities of object states over the network to improve upon their myopic performance....

In this work, we consider the front-end processing for an active sensor. We are interested in estimating signal amplitude and noise power based on the outputs from filters that match transmitted waveforms at different ranges and bearing angles. These parameters identify the distributions in, for example, likelihood ratio tests used by detection alg...

In this work, we consider a network of bearing only sensors in a surveillance scenario. The processing of target measurements follow a two-tier architecture: The first tier is com-posed of centralised processing clusters whereas in the second tier, cluster heads perform decentralised processing. We are interested in the first tier problem of locati...

We propose a new methodology for designing decentralized random field estimation schemes that takes the tradeoff between the estimation accuracy and the cost of communications into account. We consider a sensor network in which nodes perform bandwidth limited two-way communications with other nodes located in a certain range. The in-network process...

We consider geographically dispersed and networked sensors col-lecting measurements from multiple targets in a surveillance region. Each sensor node filters the set of cluttered, noisy target measure-ments it collects in a sensor centric coordinate system and with imperfect detection rates. The filtered multi-target information is, then, communicat...

Recent progress in multi-object filtering has led to algorithms that compute
the first-order moment of multi-object distributions based on sensor
measurements. The number of targets in arbitrarily selected regions can be
estimated using the first-order moment. In this work, we introduce explicit
formulae for the computation of the second-order stat...

In this paper, we consider the problem of Distributed Multi-sensor Multi-target Tracking (DMMT) for networked fusion systems. Many existing approaches for DMMT use multiple hypothesis tracking and track-to-track fusion. However, there are two difficulties with these approaches. First, the computational costs of these algorithms can scale factoriall...

In the investigation of multi-sensor fusion problems, it is commonly assumed that all the parameters necessary to transform the information from the sensors to a common frame are known. Imperfect knowledge of these registration parameters induce systematic biases which would inhibit the benefits of multisensor fusion. For example, they can result i...

Motivated by the vision of sensor networks, we consider decentralized estimation networks over bandwidth-limited communication links, and are particularly interested in the tradeoff between the estimation accuracy and the cost of communications due to, e.g., energy consumption. We employ a class of in-network processing strategies that admits direc...

In this paper, we consider the role that different information measures play in the problem of decentralised multi-target tracking. In many sensor networks, it is not possible to maintain the full joint probability distribution and so suboptimal algorithms must be used. We use a distributed form of the Probability Hypothesis Density (PHD) filter ba...

We consider the problem of distributed target tracking in a multi-object, multi-sensor scenario in which the structure of the joint distribution of the estimate between different nodes is unknown. In this paper we present a preliminary implementation of Generalised Covariance Intersection (GCI) fusion rule for multi-object posteriors through a Mont...

We consider a decentralized estimation network subject to communication constraints such that nearby platforms can communicate with each other through low capacity links rendering an undirected graph. After transmitting symbols based on its measurement, each node outputs an estimate for the random variable it is associated with as a function of bot...

We consider the design problem of a decentralized estimation net-work under communication constraints. The underlying low capac-ity links are modeled by introducing a directed acyclic graph where each node corresponds to a sensor platform. The operation of the platforms are constrained by the graph such that each node, based on its measurement and...

We consider the problem of decentralized estimation of a random-field under communication constraints in a Bayesian setting. The underlying system is composed of sensor nodes which collect measurements due to random variables they are associated with and which can communicate through finite-rate channels in accordance with a directed acyclic topolo...

We consider the problem of decentralized estimation of a
random-field under communication constraints in a Bayesian
setting. The underlying system is composed of sensor nodes
which collect measurements due to random variables they are
associated with and which can communicate through finite-rate
channels in accordance with a directed acyclic topolo...

We consider designing decentralized estimation schemes over bandwidth limited communication links with a particular interest in the tradeoff between the estimation accuracy and the cost of communications due to, e.g., energy consumption. We take two classes of in–network processing strategies into account which yield graph representations through m...

We consider the problem of localizing targets which act as acoustic sources over a region covered by a sensor network in which each node is equipped with an acoustic intensity sensor. The a posteriori distribution of each target location is constructed through a message passing algorithm on the factor graph representation of the joint posterior whi...

We consider the problem of localizing targets which act as acoustic sources over a region covered by a sensor network in which each node is equipped with an acoustic intensity sensor. The a posteriori distribution of each target location is constructed through a message passing algorithm on the factor graph representation of the joint posterior whi...

We consider the problem of localizing targets which act as acoustic sources over a region covered by a sensor network in which each node is equipped with an acoustic intensity sensor. The a posteriori distribution of each target location is constructed through a message passing algorithm on the factor graph representation of the joint posterior whi...

Sensor networks have provided a technology base for distributed target tracking applications among others. Conventional centralized approaches to the problem lack scalability in such a scenario where a large number of sensors provide measurements simultaneously under a possibly non-collaborating environment. Therefore research efforts have focused...

zetçe Tam çift yönlü modemlerin en önemli işlem öbeklerinden bir tanesi yankı gidericidir. Uygulamada yakın ve uzak yankı, mümkün olan en kısa süre içinde, belli bir seviyenin altına bastırılmalıdır. Bunun için, modem tokalaşma protokolünde özel bir eğitime ihtiyaç duyulmaktadır. Bu makalede, kanalın dürtü tepkesini belirlemek için yeni bir yöntem...

## Projects

Projects (5)

This research investigated the trade-off between the cost of communication and accuracy of estimation in sensor networks. The goal was to establish a design paradigm for finding decentralised estimation/communication strategies allowing us to invoke this trade-off as desired.