Shozo Mori

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David Seamon

Kansas State University

T. Florian Jaeger

University of Rochester

Ibrahim Cahit

Near East University

Alessio Palmisano

Università degli Studi di Torino

Marlon Tayag

Holy Angel University

Omar Amin

Ain Shams University

Yagnik Rathod

Gujarat Technological University

Adnan Awad

The University Of Jordan

Lyudmila S Mihaylova

The University of Sheffield

Armando Marques-Guedes

UNL - New University of Lisbon

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Papers by Shozo Mori

A random finite set formalism for multiple hypothesis tracking

Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX, 2020

This paper is generally concerned with mathematical formalisms to support theory and algorithm de... more This paper is generally concerned with mathematical formalisms to support theory and algorithm developments of multiple hypothesis tracking (MHT), as a class of solutions to multiple target tracking (MTT) problems based on targetwise detections. In particular, this paper presents a new perspective on random set (RFSet) formalism to support a form of MHT, in which an unknown number of targets is modeled by a RFSet of continuous-time stochastic processes, rather than a single stochastic process defined on the space of finite sets in a given target state space, while generally multiple sensors provide noisy and cluttered target detections without any explicit indications of origins. The focus is on a clearcut approach to avoid any complication resulting from diagonal sets in direct-product spaces when a space of finite subsets of a state space is defined as its quotient space, instead of a subspace of the space of closed subsets in the state space with Fell-Matheron topology.

A Review of Forty Years of Distributed Estimation

2018 21st International Conference on Information Fusion (FUSION), 2018

This paper reviews forty years of distributed estimation research since the first papers on decen... more This paper reviews forty years of distributed estimation research since the first papers on decentralized filtering appeared in 1978. Starting with a formulation of the problem, it reviews the assumptions and objectives of the main approaches, including information decorrelation, cross-covariance fusion, channel filters, covariance intersection, maximum a posteriori probability fusion, best linear unbiased estimate, and distributed Kalman filters based on pseudo estimates and augmented state estimates. It also reviews algorithms motivated by sensor networks with flexible communication including consensus and diffusion filters. Suggestions for future research are provided.

Graphical models for nonlinear distributed estimation

Citeseer, 2004

AbstractDistributed estimation has advantages over centralized estimation in reducing communicat... more

Theory of Bargaining Process: A Game Theoretical Approach

1982 American Control Conference, 1982

Based on bargaining problems in the form that J. Nash originally formulated, a two-person bargain... more Based on bargaining problems in the form that J. Nash originally formulated, a two-person bargaining process is modelled as an infinite game in extensive form. In this game, both bargainers make their proposals in terms of the utility pair resulting from possible agreement, and express approval or disapproval, alternatively changing roles. The existence and the uniqueness of subgame-perfect pure-strategy Nash equilibrium are explored. Limiting results when the time between bargaining sessions becomes shorter and shorter are connected with one of the conventional bargaining theories. Throughout this note, time-preference modelled by constant discounting plays an important role.

Forty Years of Multiple Hypothesis Tracking - A Review of Key Developments

2018 21st International Conference on Information Fusion (FUSION), 2018

Multiple hypothesis tracking addresses difficult multiple target tracking problems by making asso... more Multiple hypothesis tracking addresses difficult multiple target tracking problems by making association decisions using multiple scans or frames of data. This paper reviews forty years of its development, including the original measurement-oriented approach of Reid, track-oriented approach first formulated by Morefield, distributed processing, and recent graph-based approaches. It also discusses its relationship with random set approaches for tracking.

Bayesian Estimation of Multi-Attribute Utility Function and It's Application to the Evaluation Problem

Transactions of the Society of Instrument and Control Engineers, 1978

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Distributed fusion and communication management for target identification

The performance of target identification can be improved by fusing the data from multiple sensors... more The performance of target identification can be improved by fusing the data from multiple sensors. Even though distributed fusion has advantages of lower communication bandwidth, less processing at a central location, and increased robustness over centralized fusion, it has to address technical issues such as the conditional dependence of information to be fused by a fusion agent. This paper presents distributed fusion and communication management algorithms for target identification. Information graphs are used to select fusion architectures that minimize the effect of information double counting due to communication. Bayesian networks are used to model the target identification problem and identify the sufficient information that needs to be communicated between processing agents for optimal fusion. Communication strategies are developed to determine when a fusion agent should communicate with another fusion agent. Simulation examples demonstrate the performance of distributed fus...

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Distributed tracking in wireless ad hoc sensor networks

Sixth International Conference of Information Fusion, 2003. Proceedings of the, 2003

Target tracking is an important application for wireless ad hoc sensor networks. Because of the e... more Target tracking is an important application for wireless ad hoc sensor networks. Because of the energy and communication constraints imposed by the size of the sensors, the processing has to be distributed over the sensor nodes. This paper discusses issues associated with distributed multiple target tracking for ad hoc sensor networks and examines the applicability of tracking algorithms developed for traditional networks of large sensors. When data association is not an issue, the standard predict/update structure in single target tracking can be used to assign individual tracks to the sensor nodes based on their locations. Track ownership will have to be carefully migrated, using for example information driven sensor tasking, to minimize the need for communication when targets move. When data association is needed in tracking multiple interacting targets, clusters of tracks should be assigned to groups of collaborating nodes. Some recent examples of this type of distributed processing are given.

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Analytical and Computational Evaluation of Scalable Distributed Fusion Algorithms

IEEE Transactions on Aerospace and Electronic Systems, 2010

The theoretical fundamentals of distributed information fusion have been developed over the past ... more The theoretical fundamentals of distributed information fusion have been developed over the past two decades and are now fairly well established. However, practical applications of these theoretical results to dynamic sensor networks have remained a challenge. There has been a great deal of work in developing distributed fusion algorithms applicable to a network centric architecture. In general, in a distributed system such as ad hoc sensor networks, the communication architecture is not fixed. In those cases, the distributed fusion approaches based on pedigree information may not scale because of limited communication bandwidth. In this paper, we focus on scalable fusion algorithms and conduct analytical performance evaluation to compare their performance. The goal is to understand the performance of these algorithms under different operating conditions. Specifically, we evaluate the performance of channel filter fusion, naïve fusion, Chernoff fusion, Shannon fusion, and Bhattacharyya fusion algorithms. We also compare their performance to "optimal" centralized fusion under a specific communication pattern. The results show that the channel filter fusion, representing a first order approximation to the information graph fusion, is the only "consistent" fusion algorithm.

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A Multitarget Tracking Algorithm - Independent but Non-Poisson Cases

1985 American Control Conference, 1985

A general multi-target tracking algorithm is described under the assumption of independent target... more A general multi-target tracking algorithm is described under the assumption of independent targets but without the Poisson assumption on the total number of targets. The standard assumptions, conditional independence of the measurements, etc., are also maintained. A related issue of targets with/without a priori identification will be briefly discussed.

Tracking Multiple Air Tragets with Distributed Acoustic Sensors

1987 American Control Conference, 1987

The tracking of multiple air targets by a network of distributed acoustic sensor/processor nodes ... more The tracking of multiple air targets by a network of distributed acoustic sensor/processor nodes is considered. Since each sensor measures only the acoustic azimuths of the targets, cooperation among nodes in the distributed sensor network (DSV) is needed. A multiple-hypothesis approach to distributed tracking is used. Each sensor/processor forms hypotheses consisting of local azimuth and azimuth rate tracks for the targets. When the nodes communicate, hypotheses consisting of global position and velocity tracks are then formed. Simulation results for a network of several nodes are presented to illustrate the algorithms.

Tracking Aircraft by Acoustic Sensors; Multiple Hypothesis Approach Applied to Possibly Unresolved Measurements

1987 American Control Conference, 1987

Poor resolution of acoustic sensors frequently lead to merged measurements for closely spaced tar... more Poor resolution of acoustic sensors frequently lead to merged measurements for closely spaced targets. This paper considers tracking low-altitude aircraft by a network of acoustic sensors. Merging measurement outputs from sensors are probabilistically analysed. A multiple hypothesis approach is then used to derive an algorithm for tracking the targets. The likelihood functions used in hypothesis evaluation are derived assuming two-way merging and a simulated example is used to illustrate the algorithm.

Information Fusion in Distributed Sensor Networks

A distributed sensor network (DSN) consists of a set of processing nodes collecting data from sen... more A distributed sensor network (DSN) consists of a set of processing nodes collecting data from sensors. When the nodes communicate, each node fuses the information received from other nodes with the local information to obtain an updated situation assessment. This paper presents fusion results based on a multiple hypothesis approach. The two important problems of hypothesis formation and evaluation are discussed.

Distributed Tracking in Distributed Sensor Networks

1986 American Control Conference, 1986

This paper considers distributed multitarget track-ing in a distributed sensor network(DSN). A DS... more

Performance evaluation of a multiple-hypothesis multi-target tracking algorithm

29th IEEE Conference on Decision and Control, 1990

ABSTRACT This paper is concerned with the perfor-mance evaluation of multiple-hypothesis, multi-t... more

Performance analysis of graph-based track stitching

Recently graph-based tracking (GBT) algorithms were proposed as a new approach to multiple hypoth... more Recently graph-based tracking (GBT) algorithms were proposed as a new approach to multiple hypothesis tracking (MHT) because of efficiency issues of existing MHT algorithms. This paper analyzes the data association performance of a particular class of GBT algorithms when applied to track stitching problems. The tracklets or track segments to be stitched are the nodes (vertices) in the association or track graph and the arcs (edges) represent possible associations. The best stitching hypothesis is found by solving an optimization problem similar to that of MHT. When tracklet stitching or association likelihoods satisfy the Markov or path-independence assumption, the optimization problem can be reduced to a maximum weight bipartite matching problem. While several polynomial-time bipartite matching algorithms are available, the stitching performance depends on the validity of the Markov or path-independence assumption. This paper examines the effect of this assumption on the associatio...

Optimal fusion for non-zero process noise

Centralized fusion provides, by definition, the best (optimal) estimation performance by directly... more Centralized fusion provides, by definition, the best (optimal) estimation performance by directly using measurements of all sensors. When bandwidth is limited, sensors can only communicate their local processing results or “state estimates” instead of measurements to the fusion node. The goal of optimal fusion is to reconstruct the optimal centralized estimate from the local estimates. When the dynamic system for the state has non-zero process noise, the optimal estimate cannot be obtained by fusing the optimal local estimates unless the communication and fusion rates are the same as the sensor observation rate. For arbitrary communication rates, recent research under the name of distributed Kalman filter (DKF) has developed optimal fusion algorithms that combine local estimates that are not locally optimal. This paper presents a very simple derivation of the DKF that highlights the nature of the DKF. It also generalizes the DKF to handle fusion with memory when the fusion node util...

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Generalized Murty's algorithm with application to multiple hypothesis tracking

2007 10th International Conference on Information Fusion, 2007

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Point process formalism for multiple target tracking

Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)

This paper discusses the point process formalism of multiple target tracking problems. Finite poi... more This paper discusses the point process formalism of multiple target tracking problems. Finite point processes are defined as random elements in the spaces of finite sequences with their orders ignored, rather than as random integer-valued measures. Two recently developed algorithms, Joint Multitarget Probability (JMP) algorithm and Unified Data Fusion (UDF) algorithm will be described as algorithms based on the point process formalism. Two more established algorithms, Multiple Hypothesis Tracking (MHT) and Probabilistic Data Association (PDA) algorithms, will also be examined as algorithms using the point process concept. The point process formalism will then be compared with the random set formalism developed by Ron Mahler.

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Tracking and classifying multiple targets without<tex>a priori</tex>identification

IEEE Transactions on Automatic Control, 1986

Abstract-Based on a general target/sensor model which allows dependence among targets and state-d... more

A random finite set formalism for multiple hypothesis tracking

Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX, 2020

A Review of Forty Years of Distributed Estimation

2018 21st International Conference on Information Fusion (FUSION), 2018

Graphical models for nonlinear distributed estimation

Citeseer, 2004

AbstractDistributed estimation has advantages over centralized estimation in reducing communicat... more

Theory of Bargaining Process: A Game Theoretical Approach

1982 American Control Conference, 1982

Forty Years of Multiple Hypothesis Tracking - A Review of Key Developments

2018 21st International Conference on Information Fusion (FUSION), 2018

Bayesian Estimation of Multi-Attribute Utility Function and It's Application to the Evaluation Problem

Transactions of the Society of Instrument and Control Engineers, 1978

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Distributed fusion and communication management for target identification

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Distributed tracking in wireless ad hoc sensor networks

Sixth International Conference of Information Fusion, 2003. Proceedings of the, 2003

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Analytical and Computational Evaluation of Scalable Distributed Fusion Algorithms

IEEE Transactions on Aerospace and Electronic Systems, 2010

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A Multitarget Tracking Algorithm - Independent but Non-Poisson Cases

1985 American Control Conference, 1985

Tracking Multiple Air Tragets with Distributed Acoustic Sensors

1987 American Control Conference, 1987

Tracking Aircraft by Acoustic Sensors; Multiple Hypothesis Approach Applied to Possibly Unresolved Measurements

1987 American Control Conference, 1987

Information Fusion in Distributed Sensor Networks

Distributed Tracking in Distributed Sensor Networks

1986 American Control Conference, 1986

This paper considers distributed multitarget track-ing in a distributed sensor network(DSN). A DS... more

Performance evaluation of a multiple-hypothesis multi-target tracking algorithm

29th IEEE Conference on Decision and Control, 1990

ABSTRACT This paper is concerned with the perfor-mance evaluation of multiple-hypothesis, multi-t... more

Performance analysis of graph-based track stitching

Optimal fusion for non-zero process noise

Download

Generalized Murty's algorithm with application to multiple hypothesis tracking

2007 10th International Conference on Information Fusion, 2007

Download

Point process formalism for multiple target tracking

Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)

Download

Tracking and classifying multiple targets without<tex>a priori</tex>identification

IEEE Transactions on Automatic Control, 1986

Abstract-Based on a general target/sensor model which allows dependence among targets and state-d... more

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