Yogesh Nijsure

An Impulse Radio Ultrawideband System for Contactless Noninvasive Respiratory Monitoring

We design a impulse radio ultrawideband radar monitoring system to track the chest wall movement of a human subject during respiration. Multiple sensors are placed at different locations to ensure that the backscattered signal could be detected by at least one sensor no matter which direction the human subject faces. We design a hidden Markov model to infer the subject facing direction and his or her chest movement. We compare the performance of our proposed scheme on 15 human volunteers with the medical gold standard using respiratory inductive plethysmography (RIP) belts, and show that on average, our estimation is over 81% correlated with the measurements of a RIP belt system. Furthermore, in order to automatically differentiate between periods of normal and abnormal breathing patterns, we develop a change point detection algorithm based on perfect simulation techniques to detect changes in the subjects breathing. The feasibility of our proposed system is verified by both the simulation and experiment results.

Novel System Architecture and Waveform Design for Cognitive Radar Radio Networks

A novel approach to combining communication and radar functionalities in a single waveform design for cognitive radar radio (CRR) networks is proposed. This approach aims at extracting the target parameters from the radar scene, as well as facilitating high-data-rate communications between CRR nodes, by adopting a single waveform optimization solution. The system design technique addresses the coexisting communication and radar detection problems in mission-critical services, where there is a need of integrating the knowledge about the target scene gained from distinct radar entities functioning in tandem with each other. High spatial resolution and immunity to multipath fading make ultrawideband (UWB) signals an appropriate choice for such applications. The proposed solution is achieved by applying the mutual-information-based strategy to design the sequence of UWB transmission pulses and embed into them the communication data with the pulse position modulation scheme. With subsequent iterations of the algorithm, simulation results demonstrate an improvement in extraction of the parameters from the radar scene, such as target position and impulse response, while still maintaining high-throughput radio links with low bit error rates between CRR nodes.

Information-theoretic algorithm for waveform optimization within ultra wideband cognitive radar network

A novel information-theoretic approach for designing the excitation ultra wideband (UWB) waveforms within a cognitive radar network is developed. This method utilizes the mutual information (MI) between subsequent radar returns to extract desired information from the radar scene. With this approach, the radar system constantly learns about its surroundings and adopts its operational mode accordingly based upon the MI minimization criterion. Subsequently, the positioning algorithm makes use of this information about the radar scene to generate more accurate location estimates. Numerical results demonstrate an improvement in the probability of target detection even at low values of receive signal-to-noise ratio (SNR). The proposed algorithm also promises a better delay-Doppler resolution of the target, which can be analyzed through the radar ambiguity function (AF). Simulation data show an improvement in the target discrimination ability in the presence of noise and clutter.

Predictive opportunistic spectrum access using learning based hidden Markov models

To realize opportunistic spectrum access, spectrum sensing is applied to detect the presence of spectrum holes. If secondary radios (SRs) randomly or sequentially sense the channels until a spectrum hole is detected, significant amount of the scarce spectrum resource will be wasted, since SRs transmit only after a decision has been made. On the other hand, with the use of an intelligent predictive method, SRs can learn from the past activities of each channel to predict the next channel state. By prioritizing the order in which channels are sensed according to the channels availability likelihoods, the probability that an SR gets a channel upon its first attempt significantly increases, and thus reduces the possible waste. This paper introduces a learning-based hidden Markov model (HMM) to predict the channel activities. Simulation results show that the proposed HMM can predict the channel activities with high accuracy after sufficient training. Our algorithm predicts the availability of the channels by only making use of the current state of the spectrum. Furthermore, by incorporating the outcome of the actual channel sense, our algorithm is able to make self-regulation before next decision, so that errors will not propagate.

Hidden Markov Model for target tracking with UWB radar systems

In this paper we demonstrate the application of Hidden Markov Models (HMM) for localization and tracking in ultra wide band (UWB) radar networks. To improve localization, a Voronoi region based approach is utilized to form a HMM for detection and tracking of mobile target. The observations used for the HMM localization are obtained from the power delay profile of the received signals. In UWB systems the use of entire power delay profiles instead of the total power only, allows to reach higher localization accuracy. This is due to the power delay profile being a measure of the power as well as the time of arrival. Simulation results suggest a performance gain of 7dB over the maximum likelihood estimation for localization in presence of path loss at intermediate values of signal to noise ratio (SNR).

Minimum Cell Size for Information Capacity Increase in Cellular Wireless Network

In this paper results of mathematical analysis supported by simulation are used to find a theoretical limit for cell size reduction in mobile communication systems. Information capacity approach is used for the analysis. Attention is given to the active co-channel interfering cells. Because at microwave frequencies beyond 2 GHz, co-channel interfering cells beyond the first tier becomes dominant as the cell size reduces. We show that when the cell size limit is reached any further reduction in cell size will not increase the information capacity of the cellular network. A formula is derived for calculating the number of co-channel cells in subsequent tiers.

WSN-UAV Monitoring System with Collaborative Beamforming and ADS-B Based Multilateration

This paper presents wireless sensor network unmanned aerial vehicle (WSN-UAV) system for military remote monitoring and surveillance. Large scale WSN is deployed in a battlefield or wide hostile region to collect information of interest and send it to a UAV. Collaborative beamforming (CB) is used to achieve the ground-to-air transmissions. An automatic dependent surveillance-broadcast (ADS-B) based multilateration is used to obtain the UAV location and tracking information. It is found that a minimum distance between the UAV and the WSN is required for proper operation of the CB due to the precision of the multilateration and the movement of the UAV.

A Bayesian nonparametric approach to tumor detection using UWB imaging

We develop a tumor detection and discrimination algorithm for Ultra-Wideband (UWB) microwave imaging of breast cancer based on a Bayesian nonparametric approach. We model the UWB backscattered signal as a mixture of distinct scatterer contributions, and use a Dirichlet Process mixture model (DPMM) to describe the amplitudes and delays of the backscattered returns. Because of the unbounded complexity afforded by the DPMM, model under-fitting is avoided and parameters like the clutter covariance matrix in other commonly used approaches, need not be estimated. The DPMM allows us to perform discrimination when there are multiple tumor and clutter sources that present as extended radar targets. After performing discrimination, we distinguish the tumor sources from other clutter sources using a generalized likelihood ratio test (GLRT). We perform experiments on a breast phantom with realistic dielectric contrast ratios, and compare the performance of our algorithm with a direct GLRT approach. Our numerical results show performance improvement in terms of tumor detection probability and Signal to Interference and Noise Ratio (SINR) gain of approximately 2.2 dB at a probability of detection of 0.9 over the GLRT method.

Vector Precoding Scheme for Multi-User MIMO Systems

In this paper, the performance of vector precoding in multiple input multiple output broadcast channels (MIMO BC) is investigated and compared with other channel decomposition techniques utilized for implementing zero forcing (ZF) preceding. It is a known result that ZF precoding requires pseudo inversion of the channel matrix, where this operation is only optimum when the transmitter power is unconstrained. The problem when the transmitter is subject to average or maximum power constraints is well known, where results published have indicated that ZF precoding approaches the maximum capacity bound if the dimensionality of the system is greater than the number of transmitter antennas. A vector precoding technique for MIMO BC channels is investigated in this paper where pseudo inversion is circumvented by employing joint co-operation between transmitter and receiver for all users. This technique adopts a time scheduling approach to service the users which facilitates decentralized multi-user detection at the receiver. This approach yields an improvement to the bit error rate probability by approximately an order of magnitude as compared to the ZF approach utilizing other channel decomposition techniques. The scheme also enables an increase in the capacity of the MIMO BC, with less computational complexity as compared to the techniques employing Moore-Penrose pseudo inverse.

A Novel Spectrum Sensing Mechanism Based on Distribution Discontinuity Estimation within Cognitive Radio

A novel spectrum sensing approach based on distribution discontinuity estimation facilitated by change-point (CP) detection algorithm has been presented in this work. Specifically, a Markov Chain Monte Carlo (MCMC) based CP detection algorithm to estimate the variations in the distribution over the received signal is developed. Primary User (PU) activity, autonomous classification of modulation and detection of PU emulation attempts are detected using the CP- Maximum Likelihood Estimation (MLE) framework. Specifically, the CP based approach facilitates PU activity detection and initiates the MLE based mechanism to discern or reveal the underlying modulation scheme within the received signal. Thus, the proposed joint CP-MLE framework not only aims at detecting the discontinuity or the variations in the underlying distribution over the sensed signal, but also helps in attributing those variations to distinct modulation schemes, in an effort to identify PU emulation attempts. This CP- MLE framework has been extensively validated using the Universal Software Radio Peripheral (USRP) devices for various types of scenarios involving real-time modulation classification and identification of PU emulation type attacks.