Onur Ozdemir

Channel Aware Target Localization With Quantized Data in Wireless Sensor Networks

In this paper, we propose a new maximum-likelihood (ML) target localization approach which uses quantized sensor data as well as wireless channel statistics in a wireless sensor network. The novelty of our approach comes from the fact that statistics of imperfect wireless channels between sensors and the fusion center along with some physical layer design parameters are incorporated in the localization algorithm. We call this approach ldquochannel-aware target localization.rdquo ML target location estimators are derived for different wireless channel models and receiver architectures. Furthermore, we derive the Cramer-Rao lower bounds (CRLBs) for our proposed channel-aware ML location estimators. Simulation results are presented to show that the performance of the channel-aware ML location estimators are quite close to their theoretical performance bounds even with relatively small number of sensors and their performance is superior compared to that of the channel-unaware ML estimators.

Tracking in Wireless Sensor Networks Using Particle Filtering: Physical Layer Considerations

In this paper, a new framework for target tracking in a wireless sensor network using particle filters is proposed. Under this framework, the imperfect nature of the wireless communication channels between sensors and the fusion center along with some physical layer design parameters of the network are incorporated in the tracking algorithm based on particle filters. We call this approach ldquochannel-aware particle filtering.rdquo Channel-aware particle filtering schemes are derived for different wireless channel models and receiver architectures. Furthermore, we derive the posterior Cramer-Rao lower bounds (PCRLBs) for our proposed channel-aware particle filters. Simulation results are presented to demonstrate that the tracking performance of the channel-aware particle filters can reach their theoretical performance bounds even with relatively small number of sensors and they have superior performance compared to channel-unaware particle filters.

Hybrid Maximum Likelihood Modulation Classification Using Multiple Radios

In this paper, we focus on amplitude-phase modulations and propose a modulation classification framework based on centralized data fusion using multiple radios and the hybrid maximum likelihood (ML) approach. In order to alleviate the computational complexity associated with ML estimation, we adopt the Expectation Maximization (EM) algorithm. Due to SNR diversity, the proposed multi-radio framework provides robustness to channel SNR. Numerical results show the superiority of the proposed approach with respect to single radio approaches as well as to modulation classifiers using moments based estimators.

New Conditional Posterior Cramér-Rao Lower Bounds for Nonlinear Sequential Bayesian Estimation

The posterior CramérRao lower bound (PCRLB) for sequential Bayesian estimators, which was derived by Tichavsky in 1998, provides a performance bound for a general nonlinear filtering problem. However, it is an offline bound whose corresponding Fisher information matrix (FIM) is obtained by taking the expectation with respect to all the random variables, namely the measurements and the system states. As a result, this unconditional PCRLB is not well suited for adaptive resource management for dynamic systems. The new concept of conditional PCRLB is proposed and derived in this paper, which is dependent on the actual observation data up to the current time, and is implicitly dependent on the underlying system state. Therefore, it is adaptive to the particular realization of the underlying system state and provides a more accurate and effective online indication of the estimation performance than the unconditional PCRLB. Both the exact conditional PCRLB and its recursive evaluation approach including an approximation are derived. Further, a general sequential Monte Carlo solution is proposed to compute the conditional PCRLB recursively for nonlinear non-Gaussian sequential Bayesian estimation problems. The differences between this new bound and existing measurement dependent PCRLBs are investigated and discussed. Illustrative examples are also provided to show the performance of the proposed conditional PCRLB.

Narrowband Interference Resilient Receiver Design for Unknown UWB Signal Detection

In this paper, we design a novel energy-detection based receiver architecture to detect unknown UWB signals in a strong narrowband interference (NBI) environment. Designed receiver is capable of suppressing NBI at low cost without any need for searching its frequency location. This is made possible by preprocessing the received signal using a cascaded nonlinear energy operator followed by a high-pass filter before regular energy detection.

Adaptive local quantizer design for tracking in a wireless sensor network

We investigate the problem of tracking a target using a wireless sensor network, where quantized sensor measurements are utilized because of inherent communication and energy constraints. Due to the severe nonlinearity of the measurement model, we resort to sequential Monte Carlo methods for tracking, i.e., particle filters. The tracking performance is a function of local sensor quantizer thresholds. We propose a new dynamic adaptive local quantizer design approach along with some practical implementation considerations. Simulation results are presented to demonstrate the significant performance improvement achieved by our quantizer design technique.

Distributed Maximum Likelihood Classification of Linear Modulations Over Nonidentical Flat Block-Fading Gaussian Channels

In this paper, we consider distributed maximum likelihood (ML) classification of digital amplitude-phase modulated signals using multiple sensors that observe the same sequence of unknown symbol transmissions over nonidentical flat blockfading Gaussian noise channels. A variant of the expectation-maximization (EM) algorithm is employed to obtain the ML estimates of the unknown channel parameters and compute the global log-likelihood of the observations received by all the sensors in a distributed manner by means of an average consensus filter. This procedure is repeated for all candidate modulation formats in the reference library, and a classification decision, which is available at any of the sensors in the network, is declared in favor of the modulation with the highest log-likelihood score. The proposed scheme improves the classification accuracy by exploiting the signal-to-noise ratio (SNR) diversity in the network while restricting the communication to a small neighborhood of each sensor. Numerical examples show that the proposed distributed EM-based classifier can achieve the same classification performance as that of a centralized classifier, which has all the sensor measurements, for a wide range of SNR values.

Channel Aware Particle Filtering for Tracking in Sensor Networks

Due to recent advances in computation power, particle filtering has become a very promising tool for nonlinear non- Gaussian tracking applications. In this paper, a new framework for target tracking in a wireless sensor network using particle filtering is proposed. Under this framework, the imperfect nature of the communication channels between sensors and the fusion center is taken into account, and the channel information as well as inherent limitations of wireless sensors are incorporated in the tracking algorithm based on particle filters. Simulation results are presented to demonstrate the superior performance of the proposed algorithm.

Asynchronous Linear Modulation Classification With Multiple Sensors via Generalized EM Algorithm

In this paper, we consider the problem of automatic modulation classification with multiple sensors in the presence of unknown time offset, phase offset and received signal amplitude. We develop a novel hybrid maximum likelihood (HML) classification scheme based on a generalized expectation maximization (GEM) algorithm. GEM is capable of finding ML estimates numerically that are extremely hard to obtain otherwise. Assuming a good initialization technique is available for GEM, we show that the classification performance (in terms of the probability of error) can be greatly improved with multiple sensors compared to that with a single sensor, especially when the signal-to-noise ratio (SNR) is low. We further demonstrate the superior performance of our approach when simulated annealing (SA) with uniform as well as nonuniform grids is employed for initialization of GEM in low SNR regions. The proposed GEM based approach employs only a small number of samples (in the order of hundreds) at a given sensor node to perform both time and phase synchronization, signal power estimation, followed by modulation classification. We provide simulation results to show the efficiency and effectiveness of the proposed algorithm.

A novel approach to dictionary construction for automatic modulation classification

Abstract In almost all the work carried out in the area of automatic modulation classification (AMC), the dictionary of all possible modulations that can occur is assumed to be fixed and given. In this paper, we consider the problem of discovering the unknown digital amplitude-phase modulations when the dictionary is not given. A deconvolution based framework is proposed to estimate the distribution of the transmitted symbols, which completely characterizes the underlying signal constellation. The method involves computation of the empirical characteristic function (ECF) from the received signal samples, and employing constrained least squares (CLS) filtering in the frequency domain to reveal the unknown symbol set. The decoding of the received signals can then be carried out based on the estimate of the signal constellation. The proposed method can be implemented efficiently using fast Fourier transform (FFT) algorithms. In addition, we show that the distribution estimate of the transmitted symbols can be refined if the signal constellation is known to satisfy certain symmetry and independence properties.