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8/12/2019 S15-2010-Adaptive Kalman Filtering in Networked Systems With Random Sensor Delays, Multiple Packet Dropouts and Missing Measurements

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IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 58, NO. 3, MARCH 2010 1577

Adaptive Kalman Filtering in Networked SystemsWith Random Sensor Delays, Multiple Packet

Dropouts and Missing MeasurementsMaryam Moayedi, Yung K. Foo, Member, IEEE, and Yeng C. Soh

AbstractIn this paper, adaptive filtering schemes are proposedfor state estimation in sensor networks and/or networked controlsystems with mixed uncertainties of random measurement delays,packet dropouts and missing measurements. That is, all three un-certainties in the measurement have certain probability of occur-rence in the network. The filter gains can be derived by solving aset of recursive discrete-time Riccati equations. Examples are pre-sented to demonstrate the applicability and performances of theproposed schemes.

Index TermsKalman filtering, minimum mean-square errorestimation, missing measurements, networked control systems(NCSs), packet dropouts, sensor delays, sensor networks (SNs).

I. INTRODUCTION

IN a networked control system (NCS), the communication

and data networks form an integral part of the system where

the control loop is closed via a communication network channel.

And in a sensor network (SN), which is a network of indepen-

dent sensors, the measured data are sent to the estimator, mon-

itoring station, or the control station via a communication net-

work, usually wireless.While using a communication network in NCS or SN offers

many advantages such as simpler installation, easier mainte-

nance, and lower cost[1], it also leads to other problems such as

intermittent packet losses and/or delays of the communicated

information, [3]. There is another uncertainty that may be

present in the data received from the network: that is where

the data packet contains noise only (i.e., the measurement

has missing observations) and the estimator is not capable

of directly distinguishing between such packets and packets

containing valid measurements,[2],[3]. For example, this may

occur in tracking systems[2]. Therefore, it is not surprising that

the robust state estimation problem involving communicationnetworks has recently attracted considerable attention from

many control researchers.

Manuscript received March 12, 2009; accepted November 05, 2009. Firstpublished December 04, 2009; current version published February 10, 2010.The associate editor coordinating the review of this manuscript and approvingit for publication was Prof. James Lam.

M. Moayedi and Y. C. Soh are with the School of Electrical and ElectronicEngineering, Nanyang Technological University, Singapore 639798 (e-mail:[email protected]; [email protected]).

Y.K. Foo is with LW Electrical and Mechanical Engineering Private, Ltd.,Singapore 608608 (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TSP.2009.2037853

In general, two main approaches are popular for modeling

the uncertainties. The first one is to model the uncertainty by a

stochastic Bernoulli binary switching sequence taking on values

of 0 and 1,[4]. We shall refer to this approach as the stochastic-

parameter method. The second approach is to use a discrete-time

linear system with Markovian jumping parameter to represent

the random uncertainties,[5]. We shall refer to this approach as

the Markov chain approach. There is another approach where

the missing data are replaced by zeros and an incompletenessmatrix is then constructed in the measurement [6]. However, this

approach does not appear to be very popular.

There are already many available results on control and state

estimation in NCS and/or SN context. The interested readers

may refer to[1][22]and the references therein for further in-

formation. We shall review only those works that are closely

related to the current work here.

The state estimation problem for networked systems with

only one of the aforementioned uncertainties has been studied

extensively in the past (see, e.g.,[3],[7]and references therein).

For example, Nahi in 1969 [2] first developed an optimal re-

cursive filter for systems with missing measurements. In thatpaper, systems with random missing measurement were mod-

eled by a binary Bernoulli stochastic parameter and the filter

is derived via solving two Riccati equations. In [9] and [5]

the filtering problem with missing measurements was also

investigated. However, instead of using a stochastic parameter

to model the uncertainty, a two-state Markov chain was used

to probabilistically characterize the missing measurements.

Thus the measurement loss process was modeled as a Mar-

kovian jump linear system[9]and a suboptimal jump linear

estimator was presented where at each time step, a corrector

gain is selected from a finite set of precomputed filter gains

and hence the filter design consists of

choosing the switching logic, determining the size of this finiteset and assigning the filter gains . In[5], the authors employ

a Riccati equation approach (to compute the filter) assuming

that the transitional (conditional) probabilities for transitions

from one Markov-state to another are known. From a NCS or

SN point of view, requiring the knowledge of the transitional

probabilities may not be too satisfactory because they (for

example, the probability of the next packet arriving will be a

packet containing a current measurement given that the current

received packet contains a delayed measurement versus the

probability of the next packet arriving will be a packet con-

taining a current measurement given that no packet has been

received at the current sampling time) may be difficult to be

1053-587X/$26.00 2010 IEEE


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1578 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 58, NO. 3, MARCH 2010

determined or estimated. In[8], the least mean square filtering

problem for systems with one random sampling delay has been

studied using the stochastic parameter approach and an LMI

approach is used for the filter derivation. Results have also been

reported regarding filtering in networked systems with packet

dropout; see [3], [10], and[11]. In [11], an optimal filter, in

the Kalman sense, for systems with multiple packet dropoutswhere the number of consecutive packet dropouts is limited

by a known upper bound has been proposed. The uncertainty

model is, again, based on the stochastic parameter approach

and the filter design is based on the Riccati equation. In [10],

optimal estimators, which include filter, predictor and smoother

are developed based on an innovation analysis approach and

using stochastic parameter uncertainty model. The estimators

are computed recursively in terms of the solution of a Riccati

difference equation. In [3], by introducing a new notion of

stochastic -norms, the filtering problem involving sensor

delay, multiple packet dropouts, and uncertain observations are

modeled by using a stochastic parameter and all treated in a

unified framework. A steady-state filter is then designed via anLMI approach. While the result reported in [3] can handle the

cases in which there is a possibility of sensor delay, or packet

dropout, or missing measurement in data transmission through

the network, it is assumed that the packet may be subject

to only one type of the aforementioned uncertainties during

transmission in the network channel. In other words, the case

of mixed uncertainties isnotadmissible.

There are also some recent works that have considered the

state estimation problem in NCS/SN with two random uncer-

tainties. In [12], the robust estimation for uncertain sys-

tems with signal transmission delay and packet dropout was

considered. However, in their approach, the filter designed isessentially a continuous-time filter fed-on by an event-driven

zero-order hold (ZOH). In[1], the filter design problem is

studied for a class of networked systems where measurements

with random delay and stochastic missing phenomenon (which

is essentially equivalent to the missing measurement or uncer-

tain observation phenomena considered in[3] and[10]) are si-

multaneously considered. In[13], the optimal estimation in net-

worked control systems subject to random delay and packet loss

as well as the stability analysis of the estimator designed has

been investigated.

To the best of our knowledge, the only work which con-

siders the filtering problem for NCS/SN with mixed uncertain-

ties, where all of the three aforementioned uncertainties, i.e.,

random sensor delay, packet dropout and uncertain observation

(missing measurement) are admissible in the data received from

the network, was presented in[14]. The result of[14]is, how-

ever, based on the LMI approach. This renders it quite unsuit-

able for applications that call for online computation of the filter

gains. For online computation of filter gains, a Riccati equation

approach is more desirable. This motivates our present work.

This paper deals with discrete-time partially observed linear

plants where the observations are communicated to the esti-

mator via an unreliable channel with possibilities of random

delays and/or packet dropouts. We also admit the possibility

that the packet received by the estimator consists of the noiseonly [2]. Therefore, we consider the problem of robust min-

imum-variance filtering in the presence of mixed random sensor

delays, packet dropouts and missing measurement uncertainties

where all three types of uncertain observations (sensor delay,

packet dropout and missing measurement) can occur in the

system. A Riccati-like equation approach is adopted here. This

makes the filter designed suitable for online applications. Two

adaptive filtering schemes are proposed. In the first scheme,we make a distinction between the packet dropout case and

the other two uncertainties. The second scheme is a simplified

scheme that aims to maximize the online computational speed.

The organization of the paper is as follows. In the next

section we present the various state equations used to model

the uncertain system with measurement delay, packet dropout

and missing measurement. We then show how all these

sub-models may be combined via Markov chain to model

the whole uncertain system. InSection III, the general formula

which is applicable for one-step predictions with sensor delay

and missing measurement is derived. InSection IV, we con-

sider the problem of one-step prediction with multiple packet

dropouts. An adaptive filtering (Adaptive filter) schemewhere we distinguish the packet dropout case from the other

two uncertainties is developed in Section V. Section VI pro-

poses a simplified version of the filter ofSection V; the aim

is to develop a filter (the Simplified filter) that can be easily

and efficiently implemented. In Section VII, we discuss the

case of the state estimation with multiple (and possibly non-

identical) sensors. Section VIII contains some examples and

simulation results, and we finally give our concluding remarks

inSection IX.

The main contribution of the present paper is the develop-

ment of a Riccati-like equation approach to the filter design in a

networked system with mixed uncertainties involving randomsensor delay, missing measurement and packet dropout. This

makes the filter more suitable for online adaptiveapplications,

since for online computation of the filter gains a Riccati equa-

tion approach is more desirable than an LMI approach.

II. SYSTEMMODELING ANDPROBLEMFORMULATION

Consider the following discrete time linear time-varying

state-space system:

(1)

(2)

where is the state vector, is the measured output, andand are stationary, zero-mean discrete-time white

noise processes with covariance matrices:

(3)

and the initial condition satisfying the mean and covariance

conditions:

(4)

We assume that the plant is asymptotically stable and observablefrom the measured output .


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MOAYEDIet al.: ADAPTIVE KALMAN FILTERING IN NETWORKED SYSTEMS 1579

Systems with mixed uncertainties of packet delay, missing

measurement, and packet dropout may be represented by a

model of the form:

(5)

of compatible dimension

with (6)

where we have defined

(7)

and .

Let , , de-

note the four models corresponding respectively to systems with

no uncertainty, sensor delay, missing measurement and packet

dropout. These are defined by the following system matrices:Current measurement(i.e.,no uncertainty):

(8)

(9)

of compatible dimensions with

(10)

One-step sensor delay:

(11)

(12)


(13)

Missing measurement:

(14)

(15)


(16)

and

Packet dropout:

(17)

(18)

(19)

We may then represent as

shown in(20)at the bottom of the page.

Remark 1: There may be some confusion between packet

dropout and missing measurement in the literature. In our con-

text here, we define missing measurement as one where the mea-

surement is missing before encapsulation into packets. In other

words, the measurement itself is not a valid one, containing only

noise (and the estimator is not able to distinguish such error by,

for example, examining the error detection bits). On the other

hand, a packet dropout is one that occurs at the filter end.

Assume that by carefully analyzing the system, and empiricalexperimentations and observations, we are able to adequately

model the real system by assigning to each of the four models

its probability of occurring at time . Let the probability that the

system at time is be given

by .

Obviously, .

Let . We wish to construct

an estimator of the form

(21)

to generate that minimizes

where . Note that in (21) maybe considered an estimate vector for .

Therefore, an obvious choice for is

In what follows, the arguments of a function may sometimes

be omitted for notational simplicity when there is no danger

of confusion. The reader should note that the system and the

probabilities may be time varying.

III. ONE-STEPPREDICTIONSWITHSENSORDELAYS ANDMISSINGMEASUREMENTS

In this section, we consider only and (i.e., we as-sume cannot happen). The problem of multiple packetdropouts is an exceptional case which warrants separate consid-eration.

Let such that .Observing that the right block column of is 0, we obtain

or (20)


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. Similarly, since the left-most block-columnof is zero, we obtain . Left-multiply(5)by , subtract(21)from it, and add

to obtain

(22)

(23)

where .Given and , and since , the propa-

gation of the covariance matrices for may be described bythe equation

(24)

where denotes the covariance matrix of (where ex-pectation operation is taken over both and ) and we have

chosen

(25)

Remark 2: In the absence ofuncertain asin the caseof stan-dard Kalman filtering, covariance matrices may be obtained bytaking expectation over the uncertain noise. In our case, wherethere are both uncertainties in the noise and , covariance ma-trices have to be defined by taking expectation over both thenoise and in order to reflect the true probability distributionof . Hence, we can write

The problem of minimizing may be posedas

subject to (24) (26)

Let . Left and right multiply(24)by and

respectively, we obtain

(27)

Differentiate with respect to and set thederivative to zero, we obtain the optimal

(28)

And the optimal may be chosen as

(29)

Since is the covariance matrix of , it can be computed from

(30)

Hence,(27)and (30)give a set of recursive discrete-time Ric-cati-like equations which may be applied to compute the covari-ance matrices of the estimation error for any (i.e., not necessarilyoptimal) . To obtain the relevant formula for measurementupdate, we let and in(27)to obtain(31)and(32), shown at the bottom of the page.

(31)

(32)


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IV. ONE-STEPPREDICTIONWITHMULTIPLEPACKETDROPOUT

When a packet dropout occurs, the previous packet is re-tained. So we have . However, this does notimply . In any circumstances, musthave been processed by the filter before so there is no new in-formation to be extracted from . For this reason, the appro-priate thing to do when there is a packet dropout is to ignore

and just proceed with prediction based on past estimates

(33)or equivalently

(34)

Subtract(34)from , wehave the propagation of the estimation error covariance as

(35)

or(35a)

V. ADAPTIVEFILTERDESIGN

If , then because of the presence of the mea-

surement noise, it obviously does not correspond to a packet

dropout case. Thus, if , then the (conditional)

probability that this measurement indeed came from

is given by:

Prob system at time is

given measurement at time is , for

where

and (36)


is given measurement at time is

(37)

On the other hand, if , then may cor-

respond to a packet dropout case with probability 1 (Note that

although it is always possible to have the condition

in other cases, with or without uncertainty, the fact that

the measurement noise is white and nonzero makes the proba-

bility of this occurring zero). Thus we have


given measurement at time is

for or

(38)


given measurement at time is

(39)For notational simplicity, we let

so

We may now combine the results ofSections IIIandIVto pro-pose the following adaptive filtering scheme.

Conceptual Algorithm

Required input parameters: for

and .

Initialization:

(40)

(41)

where we have assumed to derive the above

initialization. Perform equations(42)(47)at the bottom of

the page.

(42)

and (43)

(44)

(45)

(46)

(47)


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State Prediction:

(48)

Prediction Covariance Computation:

(49)

Update

Post-Measurement State Estimation:

(50)

If , then

(51)

else

(52)

end

(53)

Error Covariance Matrix Update:

Perform equation(54)at the bottom of the page.

VI. A SIMPLIFIEDADAPTIVEFILTERINGSCHEME

In the above adaptive filtering scheme, one has to iteratively

compute the filter gain at each sampling time. A suboptimal

alternative is to use a fixed precomputed filter-gain instead. In

this respect, we note that the steady-state is given by

(55)

Hence, if we let and substitute

for in(28), then solving for , we may obtain the

steady-state predictor gain as shown in (56) at bottom of the

page.


Required input parameters : for

and .

Initialization:

(57)

(58)

(59)

and (60)

(61)

(54)

(56)


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(62)

where is found from

(63)

State Prediction:

(64)

Prediction Error Covariance Computation:

(65)

(66)

Update

Post-Measurement Predictor gain Determination:

(67)

If , then

(68)else

(69)

Remark 3: Note that we do not require for the compu-tation of . Its inclusion in the conceptual algorithm is justfor the purpose of performance evaluations.

VII. STATEESTIMATIONWITHMULTIPLE-SENSORSNETWORK

Assume that independent sensors are used to measureso that at each , we would have measurements

, , where andare independent of each other if . Suppose each of

these sensors will send these measurements independently so

that we would have , where orin the case of packet dropout. Assume thefilter has a buffer of size to receive and store .

Because the sensors may be spatially distributed we couldhave measurement noiseswhich depend on . Similar commentsapply to the probabilities (for example packets sent from sen-sors farther away may have higher probability of delay). Asso-ciate with each sensor (node), the covariance matrix of the mea-surement noise , and probabilities , .

Note that need not be equal to and to if . Wealso note that in some applications the measurement matricesmay be different for different sensor-nodes as well. For exampleto determine the state (position, speed, and acceleration) of an

aircraft in flight, a GPS could give better direct measurements ofits position while the in-flight gyroscope may give better directmeasurements for acceleration. Thus, given and ateach , with the associated , , and ,we may apply the following conceptual algorithm at each tofind the optimal :


Required input parameters:

for and and.

Initialization:

(70)

(71)

where we have assumed to derive the aboveinitialization. See equations(72)(75)at the bottom of thepage.

(72)

and (73)

(74)

(75)


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For do equations(76)and(77)at thebottom of the page.

If , then assign,

End if

End do loop

State Prediction:

(78)

Prediction Covariance Computation:

(79)

Update .

Post-Measurement State Estimation:

(80)

For do

If , then perform equation(81)at the bottomof the page.

Else

End if

(82)

Error Covariance Matrix Update:Perform equation(83)at the bottom of the next page.

If , then assign ,

End if

End do loop

(84)

(85)

VIII. EXAMPLES

For the purpose of simulation, we define the following

transition conditional probability[14]to describe our simulated

systems:

Prob (system is given by

at time given that the system was given by

at time .

It then follows that given , may be computed from the

following equation:

It should be pointed out that in our filter design, we do not

require the knowledge of sonly the values of s are

(76)

(77)

(81)


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required for the computation of the filter. This is desirable as

one can often make some good estimations of s by empirical

observations, experimentations, and statistical analyses but not

s.

Note that the standard Kalman filter is recovered when there

is no uncertainty in the measurement, i.e., when , ,

, and .1) Example 1: In this example, we consider the system

adapted from [11] where the following discrete-time linear

time-varying system has been considered:

with , initial values , ,

and noise covariance matrices , .

We shall construct an Adaptive filter with ,, , and evaluate it with simulations.

The conditional probabilities used for system simulations are

, , , ; ,

, , ; ,

, , , , ,

, and . The simulation results are given

inFig. 1, where we have also included the performance of the

optimal filter of[14](the Optimal filter) for comparison. As

can be seen, the Adaptive filter performs better than the Optimal

filter.

The average empirical error variances found from Monte

Carlo simulations (over ten simulations) are:

and .Next we design the filter for the same system with the prob-

abilities , , and and

the conditional probabilities , , ,

; , , , ;

, , , , ,

, , and used for simu-

lations. The average empirical error variances are found to be

and . The simulation result

is given inFig. 2.

We then set ; hence, the system becomes LTI and this

allows us to construct a Simplified filter (designed in Section VI)

as well. The simulation results for the first set of probabilitiesare given inFig. 3(plots for the second set of probabilities have

been omitted due to page constraint).

Fig. 1. Actual (solid line) and estimated (dotted line) states with for:(a) Adaptive filter ofSection V; (b) Optimal filter designed in[14].

Fig. 2. Actual (solid line) and estimated (dotted line) states with for:(a) Adaptive filter ofSection V; (b) Optimal filter designed in[14].

(83)


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Fig. 3. Actual (solid line) and estimated (dotted line) states with for:(a) Simplified filter designed in Section VI; (b) Adaptive filter ofSection V;(c) Optimal filter designed in[14].

As can be seen, both the Adaptive filter and the Simplified

filter perform better than the Optimal filter. Furthermore the

Simplified filter performs quite well in comparison with the

Adaptive filter and hence can be a good choice in certain appli-

cations, because of less computational demand and simplicity.

For the first set of probabilities the average empirical error vari-

ances found from Monte Carlo simulations (over ten simula-

tions) are:

and

And for the second set of probabilities, the corresponding

average empirical error variances are found to be

, and .

2) Example 2: In this example, we consider the example of

multi-sensor tracking over a wireless sensor network[15]. The

discrete dynamics and measurement of the agent is considered

as

where and are white Gaussian noises with zero mean andcovariance and

Fig. 4. Actual and estimated states for: (a) One sensor; (b) Two sensors.

. is the sampling period and the system

matrices are

We construct the Adaptive filter with , ,

, and and evaluate it with simulation using

the conditional probabilities , , ,

; , , ,

; , , , and

, , , .

The tracking performance with 1 sensor and 2 sensors can be

seen inFig. 4where it is clear that using more than one sensor

has helped in improving the state estimation.

The empirical error variance of the state estimation for 1 and

2 sensors are found as follows:

IX. CONCLUSION

In this paper, we have proposed adaptive Kalman filtering

schemes for state estimation applications in NCSs and SNs

where there are mixed uncertainties in the forms of sensor

delays, missing measurements, and packet dropouts. Examples

and simulations are given to demonstrate the performances ofthe designed filter.


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The Riccati-like equation approach is adopted here. This

makes the filter designed suitable for online applications since

for online computations of the filter gains, a Riccati equation

approach is more desirable than an LMI approach.

Although we have considered only one-step sensor delays in

the paper, the approach can be readily extended to incorporate

multiple-step sensor delays by appropriately enlarging the vec-tors , , and to include the relevant delayed states and

noise inputs.

It should also be noted that if sensor delays and missing mea-

surements are the only two types of uncertainties admissible

(i.e., we rule out the possibility of packet dropout), then the

scheme ofSection Vreduces to a (pre-computable) linearfilter

and the scheme ofSection VIreduces to alinear time-invariant

filter.

We acknowledge that although the method proposed in this

paper is intended for both NCS and SN, it is more suited for SN.

This is because normally for NCS the channel between the con-

troller and the actuator would induce additional uncertainties inthe command signal as well; but in(1)and(2), only sensor data

uncertainty has been considered. Nevertheless, if we assume the

actual control inputs are known (possible through the employ-

ment of the control packet acknowledgement,[16]) then the re-

sults presented here are applicable (by including the actual input

in the prediction equation as done in[16]for the TCP case). Al-

ternatively, we may assume no acknowledgment of the control

packet is available but the estimator knows what the likely con-

trol input is (i.e., uncertain input with known probability distri-

bution). In such a case, an additional error covariance term due

to the uncertain control input has to be added to the prediction

covariance equations (The reader is referred to[17]for furtherdetail). The results presented in Section Vare, therefore, ap-

plicable to NCSs with some additional assumptions, like those

introduced in[16]or [17]. However, because the optimal filter

gain will generally be a function of possible control input if the

actual control input is unknown[16],[17], a steady-state filter

gain would in general not exist. For this reason, the approach of

Section VI becomes non-applicable if the actual control input

applied to the plant is unknown to the estimator.

Finally, we suggest two directions for future research. The

first is to extend the present approach to controller design and

hence complete the entire NCS control loop. Another is the

extension of the present approach to nonlinear systems [18],[19].

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Maryam Moayedireceived the B.Sc. degree in elec-trical and electronic engineering from Shiraz Univer-sity, Shiraz, Iran, in 2006. She is currently pursuingthe Ph.D. degree in the Department of Electrical andElectronic Engineering,Nanyang Technological Uni-versity, Singapore.


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Yung Kuan Foo (M85) received the B.S.E.E. andB.S. degrees in computer science and the M.S.E.E.degree from the Massachusetts Institute of Tech-nology (MIT), Cambridge, in 1982, and the D.Phil.degree from Oxford University, Oxford, U.K., in1985.

From 1981 to 1982, he also was with the Pre-cision Products Division of Northrop Corporation,

Norwood, MA, where he completed his M.S. thesisproject in distributed SCADA system for automatedtesting of gyroscopes. He joined Nanyang Techno-

logical University as a Lecturer in 1985 and left to join his family businessin 1989. He visited the Tokyo Institute of Technology as a Senior VisitingFellow in 1988 under the sponsorship of the Japan Society for the Promotion ofScience. A past Vice-President of the Singapore Electrical Trades Association,he is the Managing Director of LW Electrical and Mechanical EngineeringPrivate, Ltd., and chairman and director of several other companies in Australia,China, and Southeast Asia.

Dr. Foo was a recipient of the Distinguished Award from the Standard, Pro-ductivity andInnovation Boardof Singapore, in 2002.In 2003, he wasconferredthe Public Service Medal by the President of the Republic of Singapore. He isa registered Professional Engineer, a licensed HV (22 kV) Electrical Engineer,and a Fellow of the Singapore Institute of Arbitrators, in Singapore.

Yeng Chai Soh received the B.Eng. degree in elec-trical and electronic engineering from the Universityof Canterbury, Christchurch, New Zealand, in 1983,and the Ph.D. degree in electrical engineering fromthe University of Newcastle, Newcastle, Australia, in1987.

From 1986 to 1987, he was a Research Assistantin the Department of Electrical and Computer

Engineering, University of Newcastle. He joined theNanyang Technological University, Singapore, in1987, where he is currently a Professor in the School

of Electrical and Electronic Engineering. His research interests are in robustcontrol, robust filtering and estimations, information theory, and optical signalprocessing, and he has published more than 200 refereed journal articles inthese areas. From 1995 to 2005, he was concurrently the Head of the Controland Instrumentation Division. He is now the Associate Dean (Research) ofthe College of Engineering. He chaired the A STAR TechScan Committee onIntelligent Systems and Sensor Networks, which formed part of Singapores2010 Science and Technology Plan. He also served in several evaluation andreview committees of national research and funding agencies.

Dr. Soh was awarded the IES Prestigious Engineering Achievement Awardin 2005.

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