Qualification: 
M.Tech
p_sudheesh[at]cb[dot]amrita[dot]edu
Phone: 
9443193913, +91 422 2685000 Ext. 5728

Sudheesh P. currently serves as Assistant Professor at Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore Campus. He obtained his B.Tech degree from CET Trivandrum, India and masters from REC Calicut, India. Currently, he is pursuing his Ph.D in the area of wireless communication from Amrita Vishwa Vidyapeetham. His areas of research include Signal Processing and Wireless Communication for high mobility systems. He has guided several B.Tech. and M.Tech. projects and has published around 15 papers in international journals and international conferences.

Education

  • Pursuing: Ph. D. in Wireless Communication
    Amrita Vishwa Vidyapeetham
  • 2002: M. Tech. in Digital Systems and Communication Engineering
    Calicut university/ REC Calicut

Professional Experience

Year Affiliation
May, 1997 - Till present Assistant Professor, Amrita Vishwa Vidyapeetham
Domain : Signal Processing and Communication

Academic Responsibilities

SNo Position Class / Batch
1. Batch Coordinator 2014-18,
2. UG Project Coordinator 2018, 2014
3. Class Adviser 2014 – 18, 2010 - 14
4. Time-Table Coordinator 1998 - 2013

Undergraduate Courses Handled

  1. Analog communication
  2. Digital communication
  3. Communication theory
  4. Wireless communication
  5. Adaptive signal processing
  6. Digital signal processing
  7. Image processing
  8. Electronic Circuits Lab
  9. Digital Systems Lab
  10. Digital Communication Lab
  11. Signal Processing Lab

Post-Graduate / PhD Courses Handled

  1. Biostatistics (BME)
  2. Digital communication (CSP)
  3. Wireless communication theory (CSP)

Academic Research – PG Projects

SNo Name of the Scholar Programme Specialization Duration Status
1. Muthukrishnan MG CSP Wireless communication 2015-16 Completed
2. Athira K. CSP Wireless communication 2017-18 Completed
3. Abhirami BME Biomedical signal processing 2018-19 Ongoing
4. Krishnatulasi CSP Wireless communication 2018-19 Ongoing

Instructional Materials Developed

Name & Description Outcome
Lab Manuals  

Research Expertise

  • UKF for channel estimation for high mobility system. (UG)
  • EKF for channel estimation combined with decision feedback equalizer for high mobility system. (PG)
  • Low complex fuzzy based kalman filter for fast time varying MIMO-OFDM systems. (UG)
  • Joint CFO and Channel estimation for fast time varying channels in MIMO-OFDM systems. (UG)

Teaching

  • Analog communication
  • Digital communication
  • Wireless communication
  • Communication theory
  • Signal processing

Publications

Publication Type: Journal Article

Year of Publication Title

2018

P. Sudheesh and Dr. Jayakumar M., “Non linear Tracking Using Unscented Kalman Filter”, Advances in Intelligent Systems and Computing, vol. 678, pp. 38-46, 2018.[Abstract]


Accurate localization of mobile robots to locate its position and orientation is of key importance since it enables a mobile robot to navigate properly in any given environment. Various techniques of localization used are such as GPS/GNSS, IMU sensors or by using odometric measurements. However each of these techniques suffers from various drawbacks. Dead-reckoning (DR) is a popular client to get precise localization information. DR estimates the current position based on the previous positions observed over a span of time. However DR depends on encoder and odometric information which are subject to major errors due to surface roughness, wheel slippage and tolerance rate of the machine which leads to an accumulation of errors. Many researchers have addressed this problem by adding certain external sources such as encoded magnetic compass, rate-gyros etc., However addition of these sensors has led to various new errors. In this paper, the use of unscented Kalman filter (UKF) is proposed along with the DR to get accurate localization information. UKF uses a deterministic sampling approach that captures the estimates of mean and covariance with a set of sigma points. The simulation results show that the proposed method is able to track the desired path with least error when compared to DR used alone. The localization of a mobile robot with the proposed system is also highly reliable.

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2017

P. Sudheesh and .M, J., “Non-linear Channel Tracking of a High Mobility Wireless Communication Systems”, Int. J. of Advanced Intelligence Paradigms, Inderscience, 2017.

2017

P. Sudheesh and Jayakumar, M., “Nonlinear Signal Processing Applications of Variants of Particle Filter: A Survey”, Lecture Notes in Electrical Engineering(accepted for publication), vol. 521, pp. 91-97, 2017.[Abstract]


Many applications of engineering require the state estimation of the real-time systems. The real-time dynamic systems are normally modeled as discrete time state space equations. The behaviors of the state space equations of many of the dynamic systems are nonlinear and non-Gaussian. Particle filter is one of the methods used for the analysis of these dynamic systems. In this review paper, many modified variants of particle filter algorithms and its application to different dynamic systems are discussed. State vector estimation using modified variants of particle filter was discussed and compared with the other standard algorithms.

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2017

J. Ramnarayan, Anita, J. P., and Sudheesh, P., “Estimation and Tracking of a Ballistic Target Using Sequential Importance Sampling Method”, Communications in Computer and Information Science, vol. 746, pp. 387-398, 2017.[Abstract]


This paper deals with an efficient tracking of a ballistic target by using certain measurements from radar. An efficient non-linear model for the target along with observed error is developed. Since different targets need different models, a specific target with known properties is chosen. Here the target chosen is 9000 mm air launched ballistic missile. This generally weigh more than 5000 kg and its velocity is 2000 m/s. Since these missiles are highly accurate, a 2-D space is chosen as its path. The radar gives the range and the angle of elevation of the missile. The input data processed by state approximation is called as state estimation. Particle filter is used for this non-linear model. Here the observed noise, the processed noise and the radar noise are taken into account. The performance of particle filter is tested and verified with the simulation. By using this particle filter, the range and altitude of this ballistic target can be predicted in advance. The main reason of particle filter’s popularity is that it is very flexible and adaptive. In practical, all non-linear systems has accurate filters.

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2017

M. Nishanth, Anita, J. P., and Sudheesh, P., “Tracking of GPS Parameters Using Particle Filter”, Communications in Computer and Information Science, vol. 746, pp. 411-421, 2017.[Abstract]


For proper functioning of the GPS system, tracking the code and carrier effectively in GPS receivers is important. The time taken for a signal to propagate from a satellite is calculated by a GPS receiver by analyzing the “pseudo random code” it generates, to that of code generated in the signal from the satellite. So it is important to effectively track the code before they become out of phase. The tracking medium synchronizes consecutively, the acquired satellite signal with the code and carrier frequencies that are locally generated. To track these parameters Kalman filter is used. To improve the efficiency of estimation and to obtain faster and accurate results particle filter (PF) is proposed, which further reduces the complexity as compared to that of the Kalman filter.

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2016

S. Vikranth, Sudheesh, P., and Dr. Jayakumar M., “Nonlinear Tracking of Target Submarine using Extended Kalman filter (EKF)”, Communications in Computer and Information Science, vol. 625, pp. 258-268, 2016.[Abstract]


This paper presents the effective method for submarine tracking using EKF. EKF is a Bayesian recursive filter based on the linearization of nonlinearities in the state and the measurement system. Here the sonar system is used to determine the position and velocity of the target submarine which is moving with respect to non moving submarine, and sonar is the most effective methods in finding the completely immersed submarine in deep waters. When the target submarines position and velocity is located from the reflected sonar, an extended Kalman filter is used as smoothening filters that describes the position and velocity of the ship with the noisy measurements given by sonar that is reflected back. By using the algorithm of extended Kalman filter we derived to estimate the position and velocity. Here the target motion is defined in Cartesian coordinates, while the measurements are specified in spherical coordinates with respect to sonar location. When the target submarine is located, the alert signal is sent to the own ship. This can be excessively used in military applications for tracking the state of the target submarine. Prediction of the state of the submarine is possible, with Gaussian noise to the input data. The simulation results show that proposed method is able to track the state estimate of the target, this was validated by plotting SNR vs MSE of state estimates. Here in this algorithm regressive iteration method is used to converge to the actual values from the data received.

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2016

Dr. Anita J. P. and Sudheesh, P., “Test power reduction and test pattern generation for multiple faults using zero suppressed decision diagrams”, International Journal of High Performance Systems Architecture, vol. 6, pp. 51-60, 2016.[Abstract]


An algorithm of test pattern generation for multiple faults is proposed using the zero suppressed decision diagrams (ZBDDs). Test pattern generation plays a major role in the design and testing of any chip. The proposed ZBDD is generated from its corresponding binary decision diagram (BDD). A test ZBDD is obtained from the true and faulty ZBDDs and the test patterns are generated from the test ZBDD. The obtained patterns are reordered because the order in which these patterns are used to test the chip is immaterial as far as the faults are concerned but the transitions between the test patterns affect the test power. Hence, the primary objective of the proposed work is the generation of test patterns for a given set of multiple faults. The next objective is to reduce the test power which is the power consumed during testing. © 2016 Inderscience Enterprises Ltd.

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2015

H. Damodaran, Sudheesh, P., and Dr. Jayakumar M., “Extended kalman filter for channel estimation combined with decision feedback equalizer for very high mobility system”, International Journal of Applied Engineering Research, vol. 10, pp. 33914-33918, 2015.[Abstract]


Training sequence based channel estimation in combination with Decision Feedback Equalizer (DFE) is used for OFDM based communication receivers. Channel estimation is performed based on Extended Kalman Filter (EKF) algorithm where a two-step predictor corrector mechanism is carried out. In very high mobility environment for LTE downlink usual channel estimation algorithms are incapable due to its nonlinear nature. So an EKF is used for the estimation of complex-valued channel impulse response from the received signal in the non linear environment where the velocity is very large. EKF jointly estimates both time varying channel parameters as well as time correlation coefficients.. Furthermore a DFE is also modeled for eliminate the Inter Symbol Interference (ISI) and for better performance. Performance is evaluated by plotting Mean Square Error (MSE) as well as Bit Error rate (BER). © Research India Publications.

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Publication Type: Conference Proceedings

Year of Publication Title

2017

J. Badrinath, Anita, J. P., and Sudheesh, P., “Lateral Prediction in Adaptive Cruise Control using Adaptive Particle Filter”, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). 2017.[Abstract]


As of recently, there are more than half a billion cars on the road throughout the world and hence arises the necessity for making safety a higher priority in vehicle technologies. Modern automobiles contain various functions that assist the driver and enhance safety. Anti-lock breaking systems and vehicle stability control systems are few of the technologies that are used to implement vehicular safety and one such technology is cruise control. Ordinary cruise control has been used in high-end premium cars for some years now; adaptive cruise control is an upgraded version. Adaptive cruise control (ACC) is an automotive feature that helps a vehicle's cruise control system to adapt the vehicle's speed according to the traffic environment. This paper discusses the advantages of adaptive particle filter compared to the existing methods in lateral prediction of a vehicle in an ACC.

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2017

P. Sudheesh, ,, and JP, A., “Nonlinear State Estimation of Wing Turbine”, In Advances in Computing, Communications and Informatics (ICACCI), 2017 International Conference . pp. 354-358, 2017.

2017

J. S. Gopal, Anita, J. P., and Sudheesh, P., “Particle Filtering Technique for Fast Fading Shadow Power Estimation in Wireless Communication”, 3rd international symposium on signal processing and intelligent recognition systems (SIRS17). Springer International Publishing, Cham, pp. 105-115, 2017.[Abstract]


There is a crucial importance of estimation of fading power in a mobile wireless communication system. This estimation is used for many handoff algorithms, power control, and adaptive transmission methods. This estimation of power loss can be used to reduce discrepancies and provide better wireless communication service to the user. Until now the window based weighted sample average estimator was used because of its simplicity. But it has its own disadvantages and hence use of Kalman filtering and adaptive Kalman filtering was proposed. Based on an autoregressive model of shadow fading power, particle filter algorithm is proposed in this paper in order to increase the efficiency of estimation and to obtain accurate results. The simulation and analysis presented in this paper have provided promising and supporting results.

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2016

T. S. Gokkul Nath, Sudheesh, P., and Jayakumar, M., “Tracking Inbound Enemy Missile for Interception from Target Aircraft Using Extended Kalman Filter”, Security in Computing and Communications, vol. 625. Springer Singapore, Singapore, pp. 269-279, 2016.[Abstract]


Breakthrough developments in missile guidance technology have made interception of inbound enemy missiles very difficult. Thus, it poses a huge risk and critically puts defensive capability of fighter aircrafts under test. This paper addresses the usage of Extended Kalman Filter (EKF) algorithm to estimate and track the location of inbound missile for interception by firing countermeasures. In this respect, prediction of the missile's location and trajectory is essential to enable the countermeasures fired to intercept the Enemy's missile accurately. Further, the proposed method can be used to alert the pilot regarding the inbound enemy missile and can be guided with various approaches to avoid or intercept it. EKF has been the best forecaster of the missile's location and trajectory since it has been extensively used to track objects in 3-Dimensions and in Missile guidance. EKF developed in this paper provides satisfactory results with a miss rate of 2.1 % and with localization error of 1.2 %. Thus the proposed method can be used in fighter jets for interception of inbound enemy missiles. It can further be used to track enemy aircraft's activity within the observed range.

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2014

L. Narayanan V, Sellappan, D. Kumar, Kodakandla, V. Kumar, V, A., M, P. K., and Sudheesh, P., “Basis Expansion Models for Low Complex Parametric Type Channel Estimation for MIMO-OFDM Systems”, Proceedings of the 5th National Conference on Recent Trends in Communication Computation and Signal Processing RTCSP-2014. pp. pp. 35- 37, 2014.

Publication Type: Conference Paper

Year of Publication Title

2016

M. M.G., Sudheesh, P., and Dr. Jayakumar M., “Channel Estimation for a high mobility MIMO system using Particle filter”, in International Conference on Recent Trends in Information Technology 2016 (ICRTIT 2016), 2016.[Abstract]


Channel estimation is an important part in a coherent detection of MIMO system. This paper proposes tracking of fast time varying channel coefficients for a MIMO system using particle filter algorithm. As the system is highly mobile, MIMO channel is fast time-varying, non-linear and non-Gaussian, particle filter is the best candidate to track the swiftly varying channel co-efficient. Even though there exist Kalman filter, the BLUE (Best Linear Unbiased Estimator), it is not suitable for nonlinear and non-Gaussian problems. Moreover Kalman filter variants namely EKF (Extended Kalman Filter) and UKF (Unscented Kalman Filter) are limited to Gaussian noise.

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2016

N. R. Nair, Sudheesh, P., and Dr. Jayakumar M., “2-D Airborne Vehicle Tracking using Kalman Filter”, in Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2016, 2016.[Abstract]


This paper focuses on linear Kalman Filter and its application in 2-D tracking of airborne vehicles. Kalman filter is a powerful computation device which uses recursive computation to attain solution of discrete linear filtering. Being an adaptive filter, Kalman filter analysis the relation between its estimated value and measured value, through a feedback loop and tries to attain the result after minimising the noises in the measured value. A system based on control systems, its estimation required can be of the past, presentor the future. In this paper, application of Kalman filter for tracking has been validated with tracking of an air-borne vehicle with constant velocity and constant deceleration. The model was validated with the SNR v/s NMSE graph. Kalman Filter is provided with the (x, y) coordinates and the velocity in each coordinate based on which the next set of coordinates are estimated by the Kalman Filter. Based on the accuracy of the modelling, Kalman Filter might require several estimations to adapt and give more precise estimations. The code has been written with iterations within estimations. Further, the identification of state equations and their relation to this application has been studied.

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2016

V. Seshadri, Sudheesh, P., Dr. Jeyakumar G., and Dr. Jayakumar M., “Tracking the Variation of Tidal Stature using Kalman Filter”, in Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2016, 2016.[Abstract]


The intent of this paper is to track the height of a tidal wave, using the Kalman filter. By using the Kalman filter algorithm, mathematical expressions are derived to determine the height of a tidal wave. By placing buoy sensors at specific locations in the sea, the real tidal wave height is measured. The buoy sensor is placed at a particular distance from the shore. The sensors continuously record data at that particular position at different time intervals and then transmit the data to the receiver on the shoreline. By continuously evaluating this data, the height of the next wave is being estimated. Since a buoy cannot be placed at every point of the wave, this method provides an easy estimation of replicating the process. These sensors are used to simulate the proposed method of tracking the height of a tidal wave and hence giving a warning in advance in case of a wave height which is more than normal. This warning helps people living in coastal areas to vacate the place in advance, therefore avoiding fatality. This tracking of the tidal wave height is useful particularly in the case of a tsunami. By adding Gaussian white noise to the input data from the buoy sensors, a prediction of the next wave height is possible.

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2015

V. Gutta, Anand, K. K. T., Movva, T. S. V. S., Korivi, B. R., Killamsetty, S., and Sudheesh, P., “Low complexity channel estimation using fuzzy Kalman Filter for fast time varying MIMO-OFDM systems”, in 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, 2015, pp. 1771-1774.[Abstract]


Estimation of channel is a significant issue in wireless communication. In this paper, TS fuzzy Kalman Filter based channel impulse response(CIR) estimation, for the time varying velocity of the receiver in a Multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) system is being proposed. The channel is being modeled using second order auto regressive (AR) random model. Linearization of channel estimation is done using fuzzy logic and Kalman filter is used to estimate the channel. For fast time varying channel, fuzzy based channel impulse response estimation is a low complex technique when compared to conventional filters. © 2015 IEEE.

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2012

P. Sudheesh, Jayakumar, A., Siddharth, R., Srikanth, M. S., Bhaskar, N. H., V, S., and Sudhakar, C. K., “Cyclic prefix assisted sparse channel estimation for OFDM systems”, in 2012 International Conference on Computing, Communication and Applications, Dindigul, Tamilnadu, 2012.[Abstract]


In this paper an efficient algorithm is presented for the estimation of a channel modelled as sparse for an OFDM system. Conventional Pilot-Based techniques and blind estimation techniques require a large number of pilot tones and complex mathematical computations respectively to estimate the channel vector. This drawback is particularly pronounced in sparse systems where the effective channel vector has a very few number of taps. The proposed method uses a modification made to the Cyclic Prefix to detect the position of the most significant taps (MST) for a sparse channel. Least Square estimation method is then used to effectively estimate the channel vector. Prior knowledge of the most significant tap positions obtained from the cyclic prefix ensures spectral and computational efficiencies. More »»

2012

G. Ignatius, U. Varma, M. Krishna, Krishna, N. S., Sachin, P. V., and Sudheesh, P., “Extended Kalman filter based estimation for fast fading MIMO channels”, in 2012 International Conference on Devices, Circuits and Systems, ICDCS 2012, Coimbatore, 2012, pp. 466-469.[Abstract]


This paper presents an algorithm for performing effective channel estimation for multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems when they encounter a fast fading environment. The algorithm models the parameters to be estimated using an auto-regressive model which is implemented using Burg Method. The channel estimation is performed using an Extended Kalman Filter (EKF). The effect of intercarrier interference (ICI) is removed by QR decomposing the channel matrix, which effectively leads to estimation of the data symbol. The channel is modeled as L-path parametric Rayleigh flat fading. The Rayleigh complex amplitudes (CA) and carrier frequency offset are jointly estimated for this channel. © 2012 IEEE.

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2012

G. Ignatius, Murali, K. V. U., Krishna, N. S., Sachin, P. V., and Sudheesh, P., “Extended Kalman filter based estimation for fast fading MIMO channels”, in 2012 International Conference on Devices, Circuits and Systems, ICDCS 2012, Coimbatore, 2012, pp. 157-161.[Abstract]


This paper presents an algorithm for performing effective channel estimation for multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems when they encounter a fast fading environment. The algorithm models the parameters to be estimated using an auto-regressive model which is implemented using Burg Method. The channel estimation is performed using an Extended Kalman Filter (EKF). The effect of intercarrier interference (ICI) is removed by QR decomposing the channel matrix, which effectively leads to estimation of the data symbol. The channel is modeled as L-path parametric Rayleigh flat fading. The Rayleigh complex amplitudes(CA) and carrier frequency offset are jointly estimated for this channel. © 2012 IEEE.

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2011

S. Bharadwaj, Krishna, B. M. Nithin, Sutharshun, V., Sudheesh, P., and Jayakumar, M., “Low complexity detection scheme for NOFDM systems based on ML detection over hyperspheres”, in 2011 International Conference on Devices and Communications, ICDeCom 2011 - Proceedings, Mesra, 2011.[Abstract]


Nonorthogonal Frequency Division Multiplexing (NOFDM) is a digital modulation technique that promises to provide extremely high spectral efficiencies. However, this modulation scheme is seldom used in practice due to the high computational complexity involved in decoding the received signal in the presence of noise. The basic aim of this paper is to reduce this decoding complexity. Here, we propose a low complexity detection algorithm which makes use of maximum likelihood (ML) decoding not over the entire signal constellation but over a proper subset of the constellation that lies on a hypersphere thereby reducing the computational complexity for decoding. Computational complexity has been evaluated for various values of transmitted power and the result has been plotted for both ML detection algorithm and the proposed algorithm at a fixed data rate. The BER performance for both the algorithms has also been compared at a fixed data rate and the result has been plotted. The results show that the proposed algorithm is far superior to ML detection algorithm in terms of computational complexity. © 2011 IEEE.

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