This paper first proposes a computationally efficient pilot-aided linear minimum mean square error (MMSE) batch channel estimation algorithm for OFDM systems in unknown wireless fading channels. The proposed approach employs a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve (KL) orthogonal expansion and finds MMSE estimates of the uncorrelated KL series expansion coefficients. Based on such an expansion no matrix inversion is required in the proposed ...

Considering the sparse structure of ultra-wideband (UWB) channels compressive sensing (CS) is suitable for UWB channel estimation. Among various implementations of CS the inclusion of Bayesian framework has shown potential to improve signal recovery as statistical information related to signal parameters is considered. In this paper we study the channel estimation performance of Bayesian CS (BCS) for various UWB channel models and noise conditions. Specifically we investigate the effects of (i) ...

In order to meet future communication system requirements channel estimation over fast fading and frequency selective channels is crucial. In this paper Space Alternated Generalized Expectation Maximization Maximum a Posteriori (SAGE-MAP) based channel estimation algorithm is proposed for Orthogonal Frequency Division Multiplexing (OFDM) systems for Autoregressive (AR) modeled time-varying sparse channels. Also an initialization algorithm has been developed from the widely used sparse approximation ...

The quality of multiple access interference (MAI) which can be improved by using channel estimation and data estimation of all active users effects considerably the performance of PIC detector. Therefore data and channel estimation performance is obtained in the initial stage has a significant relationship with the performance of PIC. So obviously it is necessary to make excellent joint data and channel estimation for initialization of PIC detector. In this work to initiate PIC module efficiently ...

In this work maximum likelihood (ML) channel estimation for uplink multicarrier code-division multiple-access (MC-CDMA) systems is considered in the presence of frequency fading channel. The expectation-maximization (EM)- and a space-alternating generalized expectation-maximization (SAGE) algorithm are introduced to avoid matrix inversion for the ML channel estimation problem. We compare the both algorithms in terms of the number of used iteration and show that the proposed algorithms converge the ...

In this paper we present an efficient iterative receiver structure of tractable complexity for joint multiuser detection and multichannel estimation (JDE) ofdirect-sequence code-division multiple-access systems operating in the presence of timevarying flat fading channel. The tilne-varying channel is assumed to be modeled according to a piece-wise constant channel. An optimality criterion is defined and analytical expressions for the corresponding optimized weight coefficients are given. Monte-Carlo ...

In this paper maximum likelihood (ML) estimator is proposed for the joint estimation of the direction of arrival (DOA) and range parameters of moving sources in the near-field of the antenna array. ML estimation algorithm is presented for deterministic signal model. Recursive form of the expectation maximization (REM) algoritm is suggested for the estimation of the near-field parameters because there is not closed form solutions for the maximum likelihood functions. Moreover simulation results of ...

This paper proposes a computationally efficient pilot-aided minimum mean square error (MMSE) channel estimation algorithm for OFDM systems. The proposed approach employs a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve (KL) orthogonal expansion and estimates uncorrelated series expansion coefficients. Moreover optimal rank reduction is achieved in the proposed approach by exploiting the optimal truncation property of the KL expansion resulting in a ...

This paper addresses the problem of aplying powerful statistical pattern classification algorithms based on kernels to target tracking. Rather than directly adapting a recognizer we develop a localizer directly using the regression form of the Support Vector Machines (SVM). The proposed approach considers using dynamic model together as feature vectors and makes the hyperplane and the support vectors follow the changes in these features. The performance of the tracker is demostrated in a sensor ...

Ultra short pulses are transmitted in Ultra-Wideband (UWB) communications. As a result of this pulses at the receiver can be resolved individually due to the sparse structure of the channel. Due to these properties the UWB channel can be estimated by using a compressed sensing based channel estimation method. The performance of channel estimation depends on the sparsity of the channel. In this work the effect of channel resolution which directly affects the sparsity is examined. It is shown that ...