Plenary Lecture

Modern Approaches to Signal Processing in Remote Sensing Systems

Professor Vyacheslav Tuzlukov

School of Electronic Engineering,
Communications Engineering and Computer Science,
Yeungnam University,
214-1 Dae-dong, Gyeongsan,
Gyeongsangbuk-do, 712-749
SOUTH KOREA
Phone: +82-53-810-3517
Fax: +82-53-810-4770
Cellular: +82-10-4460-3517

Email: tuzlukov@ynu.ac.kr

Abstract: The additive and multiplicative noise exists forever in any remote sensing system, including wireless sensor networks. Quality and integrity of any remote sensing systems and/or wireless sensor network systems are defined and limited by statistical characteristics of the noise and interference, which are caused by an electromagnetic field of the environment.

    The main characteristics of any remote sensing system and/or, naturally, wireless sensor network system, are deteriorated as a result of the effect of the additive and multiplicative noise. The effect of the addition of noise and interference to the signal generates an appearance of false information in the case of the additive noise. For this reason, the parameters of the received signal, which is an additive mixture of the signal, noise, and interference, differ from the parameters of the transmitted signal. Stochastic distortions of parameters in the transmitted signal, attributable to unforeseen changes in instantaneous values of the signal phase and amplitude as a function of time, can be considered as multiplicative noise. Under stimulus of the multiplicative noise, false information is a consequence of changed parameters of transmitted signals; for example, the parameters of transmitted signals are corrupted by the noise and interference. Thus, the impact of the additive noise and interference may be lowered by an increase in the signal-to-noise ratio (SNR). However, in the case of the multiplicative noise and interference, an increase in the SNR does not produce any positive effects.

    The main characteristics of the functioning any remote sensing systems and/or, naturally, wireless sensor network systems, are defined by an application area and are often specific for distinctive types of these systems. In the majority of cases, the main characteristics of any remote sensing systems and/or wireless sensor network systems are defined by some initial characteristics describing a quality of signal processing in the presence of noise: the precision of signal parameter measurement, the definition of resolution intervals of the signal parameters, and the probability of error.

    Our main idea is to use the generalized approach to signal processing in noise in remote sensing systems and/ or wireless sensor network systems. The generalized approach is based on a seemingly abstract idea: the introduction of an additional noise source that does not carry any information about the signal and signal parameters in order to improve the qualitative performance of remote sensing systems and/or wireless sensor network systems. In other words, we compare statistical data defining the statistical parameters of the probability distribution densities of the observed input stochastic samples from two independent frequency time regions – a "yes" signal is possible in the first region and it is known a priori that a "no" signal is obtained in the second region. The proposed generalized approach to signal processing in noise allows us to formulate a decision-making rule based on the determination of the jointly sufficient statistics of the mean and variance of the likelihood function (or functional). Classical and modern signal processing theories allow us to define only the mean of the likelihood function (or functional). Additional information about the statistical characteristics of the likelihood function (or functional) leads to better quality signal detection and definition of signal parameters in compared with the optimal signal processing algorithms of classical or modern theories.

    Thus, for any remote sensing systems and/or wireless sensor network systems, we have to consider two problems – analysis and synthesis. The first problem (analysis) – the problem of study of the stimulus of additive and multiplicative noise on the main principles and characteristics under the use of the generalized approach to signal processing in noise – is an analysis of the impact of additive and multiplicative noise on the main characteristics of remote sensing systems and/or wireless sensor network systems, the receivers in which are constructed on the basis of the generalized approach to signal processing in noise. This problem is very important in practice. Analysis of the stimulus of additive and multiplicative noise allows us to define limitations on the use of remote sensing systems and/or wireless sensor network systems and to quantify the impact of additive and multiplicative noise relative to other noise and interference present in these systems. If we are able to conclude that the presence of additive and multiplicative noise is the main factor or one of the main factors limiting the performance of any remote sensing systems and/or wireless sensor network system, then the second problem – a definition of structure and main parameters and characteristics of the generalized detector or receiver under a dual stimulus of additive and multiplicative noise (the problem of synthesis) – arises.

    The generalized approach to signal processing in noise allows us to extend the well-known boundaries of the potential noise immunity set by classical and modern signal processing theories. Employment of remote sensing systems and/or wireless sensor network systems, the receivers of which are constructed on the basis of the generalized approach to signal processing in noise, allows us to obtain high detection of signals and high accuracy of definition of signal parameters with noise components present compared with that systems, the receivers of which are constructed on the basis of classical and modern signal processing theories. The optimal and asymptotic optimal signal processing algorithms (of classical and modern theories), for signals with amplitude-frequency-phase structure characteristics that can be known and unknown a priori, are components of the signal processing algorithms that are designed on the basis of the generalized approach to signal processing in noise.

    In the present time, the most widely used in remote sensing systems and/or wireless sensor networks systems signal processing algorithms are based on the Direct-Sequence Spread-Spectrum (DS/SS) Code-Division Multiple Access (CDMA) approach and its modifications. The target data rates for wideband CDMA remote sensing system are: 144kb/s for wide area users who be in motor vehicles, 384kb/s for small area users at pedestrian speeds, and 2.048 Mb/s for stationary users within offices. The chip rates for some third and future generations of CDMA remote sensing systems include 4.096 –16.384Mb/s, corresponding to bandwidths of 5 and 20 MHz, respectively.

    The modern signal processing algorithms used in remote sensing systems and/or wireless sensor network systems can guarantee: low power, potential for high capacity and capacity increasing; antijamming, antimultipath characteristics, soft hand-off, soft capacity control, and information security. The modern signal processing algorithms used in remote sensing systems and/or wireless sensor network systems cannot guarantee low bit error rate (BER) and high-speed data transmission, simultaneously.

    Under the use of the generalized approach to signal processing in noise in remote sensing systems and/or wireless sensor network systems, we expect to obtain the following benefits in comparison with the modern signal processing algorithms: the low power, low bit error rate, more high noise immunity, high-speed data transmission, and approximately the same cost of production.

    The application area of remote sensing systems and/or wireless sensor network systems, the receivers in which are constructed based on the generalized approach to signal processing in noise, is the same as, for example, the application area of these systems employed the modern signal processing algorithms, i.e., for instance, health, military, vehicles, home and so on.
 

Brief Biography of the Speaker:
Vyacheslav P. tuzlukov is currently Full Professor of the School of Electronic Engineering, Communications Engineering and Computer Science at the Yeungnam University, Gyeongsan, South Korea. His research emphasis is on signal processing in wireless communications, wireless sensor networks, radar, remote sensing, sonar, and mobile communications. Prior to this, he was with Electrical and Computer Engineering Department of Ajou University, Suwon, South Korea, where he defined, led and managed research teams in the area of signal processing in CDMA wireless communications, particularly, in wireless sensor networks, serving as Invited Full Professor.

He has published more than 150 scientific journal and conference papers, five books in signal processing area published by Springer-Verlag and CRC Press, and has also contributed chapters “Underwater Acoustical Signal Processing” and “Satellite Communications Systems: Applications” to Electrical Engineering Handbook: 3rd Edition, 2005. He has been keynote speaker, has organized sessions, and has served as Tutorial Instructor and Speaker at major International Conferences on Signal Processing.

Dr. Tuzlukov was highly recommended by U.S. experts of Defense Research and Engineering (DDR&E) of the United States Department of Defense as a recognized expert in the field of humanitarian demining and minefield sensing technologies and had been awarded by Special Prize of the United States Department of Defense in 1999. Dr. Tuzlukov is distinguished as one of the leading achievers from around the world by Marquis Who’s Who and his name and biography have been included in the Who’s Who in the World, 2006 (23) Edition, Marquis Publisher, NJ, USA; Who’s Who in World, 25th Silver Anniversary Edition, 2008, Marquis Publisher, NJ, USA; Who’s Who in Science and Engineering, 2006-2007 (9) Edition, Marquis Publisher, NJ, USA; and Who’s Who in Science and Engineering, 10th Anniversary Edition, 2008-2009, Marquis Publisher, NJ, USA.