Dr. X. Wang'S Personal Page

· Current Work

Personal Details

Name :	Xuezhi Wang
Address :	Dept. of Electrical & Electronic Engineering The University of Melbourne Victoria, 3010
Phone :	(03) 8344 0373
Room :	3.26 EEE Building
Email :	xu.wang@ee.unimelb.edu.au

Research Interest

Bayesian estimation theory, stochastic filtering, probability theory

Target tracking, sensor fusion,

Situation awareness

Distributed information processing

Current Research Work

1. Variable structure IMM-PDA for maneuvering target tracking in clutter.

2. Maneuvering target tracking in clutter with OOSM problem
3. Approximated solution for reducing augmented state approach to non-augmented state approach

4. Multi-sensor network On-line Sensor Registration with OOSI

5. Low elevation sea-surface target tracking in clutter.

6.   Probabilistic data association techniques.

7.   Distributed sensor fusion with network constraints.

8. Situation assessment.

9. Simulink Toolbox for Advanced Radar Tracking System Simulation.

10. Multi-target tracking in binary sensor networks

Past Presentations:

1. Multitarget tracking in binary sensor networks

2. Situation awareness demo --- Threat probability assessment via a centralized sensor fusion network

3. Cross target tracking using mote networks -- demo

Teaching

Forth year projects:

1. Advanced Radar Tracking System (ARTS)

Tracking moving vehicles on land, sea and air using several types of radars is at the forefront of technology.
A Radar tracking system estimates target kinematical states, such as target position, velocity, etc. based on
a set of noisy observations received from one of the radar sensors. Examples of such a system are air traffic
control systems and defense surveillance systems. One of the most important tasks in any Radar tracking
system is to track multiple targets that come and go out of its surveillance systems. A Radar tracking system
includes the following four basic functions, i.e., track initiation, measurement-to-track, track-to-track association,
track deletion and track maintenance. This project deals with the design and development of the advanced
tracking system, integrating the basic functions to build up a complete ART system.

(Dr. Xuezhi Wang and Dr. Subhash Challa)

2. Eye diagram reconstruction with asynchronous sampling

In wavelength division multiplexed (WDM) optical networks, each channel (wavelength) carries a data stream that is actually a string of 1’s and0’s arranged in a random order. Since the transmission link, consisting of an optical fibre, an optical transmitter, and an optical receiver, is susceptible to various types of noise and nonlinearities, the signal quality is degraded during transmission. So the signal quality of each channel is continually monitored at every node of the network in terms of optical signal to noise ratio (OSNR) and/or bit-error-rate (BER).

One of such techniques is to observe the eye diagram after converting optical signal to electrical one using a photo-detector and a digital oscilloscope and correlate it with OSNR or BER. Since the synchronous sampling is rather expensive, asynchronous sampling technique is now actively investigated. The incoming signal is asynchronously sampled using a digital oscilloscope and A/D converted data are stored in a computer. The eye diagram at this stage is very difficult to identify, so a special signal processing is executed on these data to reconstruct a clear eye diagram. The project aims at developing a signal processing software for the job described above and experimental demonstration.

(Dr. Thomas Chae and Dr. Xuezhi Wang)

3. 3D Multiple Sound-Objects Tracking System Design

We assume that the locations of all sensors (microphones) are known in advance.

Task 1. (Hardware portion) Mapping noisy audio signal to the relative geometric

measurement that can be accepted by a multi-target tracking subsystem.

Work: 1. Sensor behavior analysis (calibration for detection characteristics)

2. Auto gain design to dynamically control the output amplitude of the audio

receiver.

3. Math model for both sound-object 3D location and motion.

4. Analog to digital conversion to get relative time intervals between sensor signals after detection.

Task 2. (Software portion) The system itself will have the capability to calibrate and

eliminate a fixed sensor measurement bias for all sensors involved. Apply

advanced multi-target tracking technique to design a multi-sound-object

tracking system.

Option: Sensor can be active or passive types. Probably use passive sensors to get an

easy start. Other sensor choice could be Infrared sensor.

Prospective applications:

General security sub-system, untouchable sound-object monitor, traffic control.

Note: It is possible to extend this project to the size that suitable for a prospective student who intends to pursue a PhD.

(Dr. Xuezhi Wang)

Research project:

1. Fault detection and identification

using variable structure multiple model estimation

Variable structure multiple model estimation has its broad application in target tracking and system (model based) fault detection and identification for the case where a large amount of models need to be included while system resource is constraint. However, many potential applications have not been explored.

This project aims to:

Learning and understanding variable structure multiple model estimation and its practical implementation (via Matlab).

Design a generic industry production fault detection and identification scenario.

Apply a variable structure multiple model estimation technique to estimate or detect a fault process so that to control the production quality.

( comments: this is a research project for students who wish to look for higher education)

Publications

· Journal Papers

X. Wang, S. Challa, and R. J. Evans, ``Gating Techniques for Maneuvering Target Tracking in Clutter'', IEEE AES, Vol. 38, No. 3,

pp. 1087-1097, July 2002.

S. Challa, R. Evans and X. Wang, `` A Bayesian solution and its approximations to out-of-sequence measurement problems '', Journal of Information Fusion, Vol. 4, Issue 3, pp. 185—199, September 2003.

X. Wang, S. Challa, R. J. Evans and X. Rong Li, ``Minimal Submodel-Set Algorithm for Maneuvering Target Tracking'',

IEEE Trans. AES, vol.39, Issue 4, pp. 1218- 1231, Oct. 2003.

S. Challa, X. Wang and J. Legg, ``A Fixed-Lag Smoothing Solution to Out-of-Sequence Information Fusion Problems '', Communications in Information and Systems, Special issue celebrating John Moores 60th birthday, Vol 2, No. 4, pp. 327--350, December 2002.

X. Wang, R. Evans, and J. Legg, ``Distributed Sensor Fusion with Network Constraints'', in preparation for IEEE AES.

X. Wang and D. Musicki, `` Low Elevation Sea-Surface Target Tracking’’, submitted to IEEE AES 2005, under review.

· Recent Conference Papers

X. Wang, S. Challa, and G. W. Pulford, ``Target Tracking and Classification Using Radar and ESM Sensors'', Proc. of 8th International

Aerospace Congress, Adelaide, Australia, March 1999.

X. Wang, and S. Challa, ``Comparison of Gating Techniques in IMM-PDAF for Maneuvering Target Tracking in Clutter'', Proc. of IEEE

WoSpa 2000, Brisbane, Australia, Dec. 2000.

X. Wang, Maneuvering Target Tracking and Classification Using Multiple Model Estimation Theory, PhD Thesis, University of Melbourne, Feb. 2001.

X. Wang, S. Challa, and R. J. Evans, ``Variable Structure IMM Using Minimal Sub-Model-Set Switching'', In Proc. SPIE Vol. 5096, Signal Processing, Sensor Fusion and Target Recognition XII, Orlando, FL, USA, April, 2003, pp. 80--91.

X. Wang, S. Challa, and R. J. Evans, ``Maneuvering target tracking in clutter using VSIMM-PDA'', In Proc. SPIE Vol. 5096, Signal Processing, Sensor Fusion and Target Recognition XII, Orlando, FL, USA, April, 2003, pp. 92--104.

S. Challa, R. Evans and X. Wang, ``Target Tracking in Clutter Using Time-Delayed Out-of-Sequence Measurements'', In Proceedings

of Defence Applications of Signal Processing (DASP), Sep., 2001. .

S. Challa, J. Legg and X. Wang, ``Track-to-Track Fusion of Out-of-Sequence Tracks'',, In Proceedings of the Fifth International Conference on Information Fusion, Annapolis, Maryland, USA, July, 2002, pp. 919-926.

S. Challa, B. Vo and X. Wang, ``Bayesian Approaches to Track Existence-IPDA and Random Sets'', In Proceedings of the Fifth International Conference on Information Fusion, Annapolis, Maryland, USA, July, 2002, pp. 1228—1235.

X. Wang, and D. Musicki . “Evaluation of IPDA Type Filters with a Low Elevation Sea-Surface Target Tracking", in Proceedings of the Sixth International Conference on Information Fusion, Cairns, Queensland, Australia, July, 2003, pp. 1156--1163.

X. Wang, and D. Musicki “Low Elevation Sea-Surface Target Tracking Using IPDA Type Filters ", in Proceedings of Radar 2003, International Conference, Adelaide, Australia, September, 2003, pp. 472--478.

X. Wang, and S. Challa “Augmented state IMM-PDA for OOSM solution to maneuvering target tracking in clutter", in Proceedings of Proceedings of Radar 2003 International Conference, Adelaide, Australia, September, 2003, pp. 479--485.

A. Bessell, B. Ristic, A. Farina, X. Wang, and M. S. Arulampalam, “Error Performance Bounds for Tracking a Maneuvering Target", in Proceedings of the Sixth International Conference on Information Fusion, Cairns, Queensland, Australia, July, 2003, pp. 903--910.

X. Wang, R. Evans, and J. Legg, ``Distributed Sensor Fusion with Network Constraints'', In Proc. SPIE Vol. 5429, Signal Processing, Sensor Fusion and Target Recognition XIII, Orlando, FL, USA, April, 2004.

D. Musicki and X. Wang, “ Reliability of PDA based Target Tracking in Clutter", in Proceedings of the 7^th International Conference on Information Fusion, Stockholm, Sweden, 28 June to 1 July, 2004.

X. Wang and G. Thomes, “Robustness Analysis at the Technical Level of Situation Assessment", in Proceedings of the 7^th International Conference on Information Fusion, Stockholm, Sweden, 28 June to 1 July, 2004.

X. Wang and B. Moran, “Multitarget Tracking Using Virtual Measurement of Binary Sensor Networks”, submitted to Proceedings of the 9th International Conference on Information Fusion, Florence (Italy), 10-13 July, 2006.

X. Wang and D. Musicki, “Improving Tracking Performance of Sensor Networks Using Signal Amplitude Information”, submitted to Proceedings of the 9th International Conference on Information Fusion, Florence (Italy), 10-13 July, 2006.

· Technical Reports

X. Wang and D. Musicki, “Multitarget Tracking Using Active Sonar: Problems, Algorithms and Simulation Results”, technique report to MOD, DSTO, Melbourne, Australia, May 2005.

X. Wang and D. Musicki, “A Literature Review of Data Association Techniques for Target Tracking”, technique report to MOD, DSTO, Melbourne, Australia, March 2005.

X. Wang, J. Legg and R. Evans, “Distributed sensor fusion with network constraints, Part II: Communications with Limited Channel Capacity", CSSIP technical report to DSTO through

TDFL, Melbourne, Australia, June 2004.

X. Wang, R. Evans, S. Challa, D. Musicki and J. Legg. “Distributed sensor fusion with network constraints", CSSIP technical report to DSTO through TDFL, Melbourne, Australia, June 2003.

S. Challa, R. Evans, J. Legg and X. Wang, “OOSM and OOST problems and their Bayesian solutions", CSSIP technical report to DSTO through TDFL, Melbourne, Australia, June 2002.

S. Challa, B. Vo, and X. Wang, “A random sets based tracking filter ", CSSIP technical report to DSTO through TDFL, Melbourne, Australia, June 2002.

S. Challa, R. Evans, B. Vo, N. Okello, X. Wang, and P. Scoullar. “Issues in distributed networked sensor tracking and data fusion", CSSIP technical report to DSTO through TDFL, Melbourne, Australia, June 2001.

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