In this thesis, we considered the deconvolution problem when the channel is known a priori. LSE and MSE optimum solutions are investigated with deterministic and statistical approaches. We derived closed form LSE expressions and investigated the factors that affect the FIR inverse filters. It turns out that, minimum LSE can be obtained when the system zeros are distributed homogeneously on the z-plane. We proposed partition-based FIR-IIR inverse filters. The selection of FIR and IIR parts is based on partitioning the channel zeros into two regions and using the specified channel zeros to design the best delay FIR and all pole IIR inverse filters. Three methods for partitioning are presented, namely unit circle-based, ring-based and optimum-partitioning. It turns out that ring-based and optimum-partitioning FIR-IIR inverse filter performs better than the best delay FIR inverse filter for the same complexity by about 4-5 dB. For noisy observations, it is shown that, noise should also be considered in the delay selection and partitioning. We extended our results for the design of MSE optimum statistical inverse filters. It is shown that best delay FIR-IIR inverse filters are less sensitive to the estimation errors compared to the IIR Wiener filters and they perform better than the FIR Wiener filters. Furthermore, they are always causal and stable making them suitable for real-time implementations. When the statistical and deterministic filters are compared, it is shown that for low SNR statistical filters perform better by about 1-2 dB, while deterministic filters perform better by about 0.5-1 dB for high SNR

This thesis investigates the problem of interaction of electromagnetic radiation with human tissues. A Finite Difference Time Domain (FDTD) code has been developed to model a cellular phone radiating in the presence of a human head. In order to implement the code, FDTD difference equations have been solved in a computational domain truncated by a Perfectly Matched Layer (PML). Specific Absorption Rate (SAR) calculations have been carried out to study safety issues in mobile communication.

Data association problem for multitarget tracking is determination of the relationship between targets and the incoming measurements from sensors of the target tracking system. Performance of a multitarget tracking system is strongly related to the chosen method for data association and target tracking algorithm. Incorrect data association effects state estimation of targets. In this thesis, we propose a new multi-scan data association algorithm for multitarget tracking systems. This algorithm was implemented by using MATLAB programming tool. Performances of the new algorithm and JPDA method for multiple targets tracking are compared. During simulations linear models are used and the uncertainties in the sensor and motion models are modeled by Gaussian density. Simulation results are presented. Results show that the new algorithm&#039 / s performance is better than that of JPDA method. Moreover, a survey over target tracking literature is presented including basics of multitarget tracking systems and existing data association methods.

In this thesis, we considered the problem of frame synchronization and channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems. Since framing error may cause severe ISI and may disturb the orthogonality of the subcarriers, frame synchronization must be accomplished at the OFDM receiver. Furthermore, the effects of channel must be compensated to obtain the symbols accurately. We investigated several frame synchronization algorithms including a maximum likelihood (ML) synchronizer which relies on the periodicity induced in the OFDM structure, and a robust synchronizer which uses a special training symbol. These algorithms are evaluated in AWGN and Rayleigh fading multipath channels and performances are compared in terms of percentage of ISI-free synchronization, mean squared error and symbol error rate. The IEEE 802.11a framework is used to compare these algorithms with the standard system which utilizes training symbols dedicated for synchronization. It is shown that an adjustment for the frame start estimates must be done to avoid the effects of the channel delay spread. It is also pointed that ideal synchronization is not necessary unless symbol boundaries are detected inside an ISI-free region and the error aroused in ISI-free synchronization can be compensated by applying channel estimation and equalization regarding the same symbol boundaries.

Sampling and reconstruction is used as a fundamental signal processing operation since the history of signal theory. Classically uniform sampling is treated so that the resulting mathematics is simple. However there are various instances that nonuniform sampling and reconstruction of signals from their nonuniform samples are required. There exist two broad classes of reconstruction methods. They are the reconstruction according to a deterministic, and according to a stochastic model. In this thesis, the most fundamental aspects of nonuniform sampling and reconstruction, according to a deterministic model, is analyzed, implemented and tested by considering specific nonuniform reconstruction algorithms. Accuracy of reconstruction, computational efficiency and noise stability are the three criteria that nonuniform reconstruction algorithms are tested for. Specifically, four classical closed form interpolation algorithms proposed by Yen are discussed and implemented. These algorithms are tested, according to the proposed criteria, in a variety of conditions in order to identify their performances for reconstruction quality and robustness to noise and signal conditions. Furthermore, a filter bank approach is discussed for the interpolation from nonuniform samples in a computationally efficient manner. This approach is implemented and the efficiency as well as resulting filter characteristics is observed. In addition to Yen&#039 / s classical algorithms, a trade off algorithm, which claims to find an optimal balance between reconstruction accuracy and noise stability is analyzed and simulated for comparison between all discussed interpolators. At the end of the stability tests, Yen&#039 / s third algorithm, known as the classical recurrent nonuniform sampling, is found to be superior over the remaining interpolators, from both an accuracy and stability point of view.

In this thesis, reduction of the Peak-to-Average Power Ratio (PAPR) of Orthogonal Frequency Division Multiplexing (OFDM) is studied. A new PAPR reduction method is proposed that is based on block coding the input data and modifying the codeword until the PAPR is reduced below a certain threshold. The method makes use of the error correction capability of the block code employed. The performance of the algorithm has been investigated through theoretical models and computer simulations. For performance evaluation, a new gain parameter is defined. The gain parameter considers the SNR loss caused by modification of the codeword together with the PAPR reduction achieved. The gain parameter is used to compare a plain OFDM system with the system employing the PAPR reduction algorithm. The algorithm performance is examined through computer simulations and it is found that power reductions around 2-3 dB are obtained especially for low to moderate number of channels and relatively strong codes.

Characterization of nonlinear systems is a challenging task as the output can not be expressed simply in terms of input signal. Therefore, a universal analysis method is essential to simplify this procedure. Modeling of the input signal is a crucial part of such analysis. In this thesis, multi-tone representation is employed to model arbitrary, stochastically not well-defined input signals and thereafter characterize nonlinear systems. In order to verify the validity of multitone representation, multi-tone modeling concept is primarily applied to real life amplifier characterization in single amplifier configuration. This experiment demonstrated potential of multi-tone modeling concept in nonlinear system characterization and encouraged application of the concept to analysis of feedforward linearizers, which are complicated systems due to the presence of two nonlinear amplifiers and the requirement of strict amplitude, phase and delay matching within two loops of the circuit. It has been assumed that main and error amplifiers can be modeled with third order AM/AM nonlinearities and there exists no delay mismatch within the loops. Hence, closed form expressions relating the main and adjacent channel power at the output of the feedforward system to the system parameters are obtained. The developed model is verified by RF and system simulations. As a result, a mathematical handy tool to specify circuit parameters rapidly for optimum linearity performance and efficiency is achieved.

In this work interception and spreading sequence estimation of Direct Sequence Spread Spectrum (DSSS) signals, in low signal to noise power ratios is discussed. For interception an approach based on autocorrelation fluctuations is employed and also symbol period is estimated from this interception process. Spreading sequence estimation is performed by using eigenanalysis of the correlation matrix and estimated symbol period. These approaches are applied on different kinds of DSSS signals including 3rd generation UMTS signal and LPI radar signals. In order to examine the channel effects such as / multipath, interference and colored noise, on the performance of applied techniques, detailed analysis results are obtained. The results are compared with the performances of alternative interception techniques.

Frequency modulated continuous wave (FM-CW) radar altimeters are used in civil and military applications. Proximity fuses, automatic cruise control systems of cars, radar altimeter of planes are examples to these applications. The goal of this thesis is to present a method for altitude determination using an FM-CW radar. For this purpose principles of radars and FM-CW systems are studied and related subjects are inspected. After this inspection, algorithms for altitude determination are evaluated. Consequently signal detection and processing methods are proposed to build an altitude determining algorithm. Also an analytical test environment for altitudes between 100 m and 4000 m is developed in computer as a result of researches. Test environment simulated the performance of altitude determining algorithm and FM-CW Radar Altimeter. The hardware is designed and implemented accordingly.

In this thesis, the target tracking problem with input estimation is investigated. The estimation performance of the optimum decoding based smoothing algorithm and a target tracking scheme based on the Kalman filter is compared by performing simulations. The advantages and the disadvantages of these algorithms are presented.