In this dissertation, we propose a two-stage estimator to estimate chirp rate and initial frequency of the chirp signals in the presence of additive white Gaussian noise. Chirp or LFM signals are widely used in many applications such as radar, sonar, electric ground vehicle and etc. For instance, they can be applied in tracking the trajectories of moving targets. These signals are also the most commonly used signals in high resolution radars such as synthetic aperture radars. The estimation steps in the proposed method are as follows: In the first stage, the chirp rate estimation problem was reformulated as a single tone frequency estimation problem. Then, the frequency of single tone was estimated through a linear prediction approach. Using the chirp rate estimate in the first stage, we can convert the linear frequency modulated (LFM) signal to a single tone. Similar to the first stage, the initial frequency was estimated via the linear prediction approach. The performance of the present method was assessed by comparison with Cramer-Rao lower bound (CRLB) and other existing methods through computer simulations. The proposed algorithm estimates well for different values of the chirp rate and initial frequency, as well as for different number of samples. In other words, this algorithm has uniform performance for various values of the signal parameters. Simulation results show that the performance of the proposed method is close to CRLB. 

In wireless communications, Orthogonal Frequency Division Multiplexing (OFDM) is used as a useful and efficient method for transferring high data rate through simultaneous use of orthogonal subcarriers. One of the problems for this type of modulation is the sensitivity of orthogonal subcarriers to the frequency offset. Regarding the sensitivity of OFDM systems to the frequency offset and the relevant problems in ICI and ISI subcarriers, this study aimed to discuss the fundamentals of carrier frequency offset estimation methods. Different types of estimators such as cyclic prefix-based, pilot-tones and blind estimators were presented by researchers. The proposed estimators which were based on linear prediction and used DFT bins have been employed in this study, which were found precise and valuable. Performance of the proposed estimators have been compared with Cramer-Rao bound and the advantages of these methods have been investigated.

Using signals received by the array, charactistics of target such as DOA,
distance and speed can be estimated. In this thesis, DOA estimation methods are described and
then are compared with each other. DOA Estimation methods are generally divided in two
categories: conventional and subspace-based methods. Subspace-based methods are based on
covariance matrix of data received by the array. Subspace-based methods compared to the other
methods of estimation, provide a better accuracy and resolution. Afterward the MIMO radar is
introduced and then the benefits of this radar compared to the other array radar is expressed. The
proposed method which is introduced in the thesis, received signal has been modeled two
dimentional. In that proposed method DOA is in one dimention and doppler frequency is in the
other dimention. Using formation of characteristic equations, Problem has turned into the
estimation of second-order AR model coefficients. Estimating AR model coefficients and
conversion, the charastristics of target are reached. Results were investigated through simulating
of some mentioned DOA estimation methods. The simulation is investigated with different
parameters and methods are compared with eachother. Finally it has been concluded that the
proposed algorithm provides effective and sufficent performance for estimating charactristics of
target compared to other methods.

 Abstract : Traditionally uniform arrays are used to implement beamformers. However, in order to avoid grating lobes the maximum adjacent sensor separation is half of the operating wavelength. To obtain high antenna gain and high angular measurement accuracy, an antenna array with a large number of elements should be used. So the high cost is a major drawback for a large-scale array. Meanwhile, the computational burden and is bottleneck in the implementation of an adaptive beamforming algorithm .To reduce the number of array elements without reducing the antenna aperture, we can use sparse arrays.
This thesis investigates methods to optimise the sensor locations to reach a desirable array response. Sparse antenna array design for location optimization is highly nonlinear and it is conventionally solved by genetic algorithms, simulated annealing and other similar optimization methods. However, these algorithms have a high comutional complexity and it need too use efficient computional algorithms. In this thesis, this problemis studied from the viewpoint of compressive sensing. The original formulation of the compressive sensing problem can be converted to a modifed norm minimisation for the design of wideband array. It also examines the adaptive problem solving sparse algorithm of LMS L0- for adaptive sparse of the space array's elements. In order to reduce the computational complexity, beamforming is applied in the frequency domain and for a three-dimensional space scan, this method is applied on a two-dimensional L- shape sparse array.

The passive radars do not transmit signal. In order to detect and track targets, they can use transmitted waves from the opportunity illuminators in the invironment. The emitters which are used by passive radars include TV signal, FM broadcast signal, GSM and GPS. In this thesis, the FM radio based passive radar is investigated. First, we explain the signals which are utilized in FM radio passive radar systems. Next, the interference, including direct path signal and multipath/clutter, cancellation methods in the surveillance channel are addressed. For this, the well-known adaptive algorithms such as LMS, NLMS, RLS and ECA, which is a subspace based method, are used. Moreover as a new idea, we propose sparsity and variable step size based adaptive algorithms to eliminate interference in surveillance channel of passive radars.

 The Fractional Fourier Transform (FRFT) comparing to Fourier Transform is more flexible for processing non-stationary signals due to benefiting of one additional degree.Therefore, that is employed in filtering, detection, analysis and processing of time series and time-varying systems application.The conventional method of signal analysis perform based on time and frequency analysis as they couldn't represent appropriate information about signal with time changes in a specific frequency. so, they aren't eficient for analysis non-stationary signals such as linear frequency modulation(LFM).In this thesis a new signal processing approach is investigated based on the optimal order of fractional Fourier transform.The estimation of signal parameters and detection performance of FRFT is compared by matched filter in the present gaussian white noise.The simulation confirms that the statistical performance of FRFT detector is close to the optimum detector (Matched-Filter),as the FRFT kernel is chirp signal that is expected to reaching the sidelobe and mainlobe reducing and consiquently improving signal resolution comparing to matched filter And the detection of the presence of clutter were also noted.

The CFAR detection of ground moving targets in multichannel synthetic aperture radar (SAR) images has high importance in military and civilian applications; But due to unavailability of radar equipment or its data, accessibility of a fast and accurate multichannel synthetic aperture radar raw data generator of stationary clutter and moving targets is necessary. In this thesis, a fast four-stage algorithm for generating the raw data of each channel stationary clutter and moving targets has been proposed. Using this simulator, in different conditions in terms of target motion speed, acceleration and direction, for each of the channels, after generating the raw data, its final image has been extracted by range-Doppler algorithm. Then, using clutter suppression techniques such as DPCA, ATI and hybrid DPCA-ATI, the multichannel SAR final image has been obtained in ideal and nonideal conditions. In order to ensure the accuracy of the simulator, the obtained images of the first channel have been studied using the extracted formulae for predicting the effects of target motion parameters on the SAR images as well as analyzing the multichannel SAR final image. Finally, using this simulator, the most important ground moving target CFAR detection algorithms in multichannel SAR-GMTI systems, the CFAR detector based on the interferometric phase and the second eigenvalue of the sample covariance matrix, IMP detector and adaptive detector based on DPCA amplitude have been analyzed and compared. The results show that the proposed algorithm for generating the raw data of each channel stationary clutter and moving targets has better performance in terms of speed and accuracy than the other existing simulators. In addition, the obtained ROC curvatures show the superiority of IMP detector over other detection algorithms.

Adaptive radar detectors need to estimate the statistical properties of disturbance (i.e., clutter and noise) to be used in their structure. In order to accurately estimate the unknown statistical properties (usually covariance matrix) from a limited data set it is necessary to parameterize the covariance. A popular model for the clutter which accomplishes this goal is the autoregressive (AR). It can be shown that there is a one-to-one relationship between parameters of an AR process and its covariance function. Moreover, In many situations of practical interest, the clutter samples of one range cell can be approximately modeled as an AR process of relatively low order. To further improve the detection performance, many works have exploited the structural Information of the clutter covariance matrix.

In wireless communications Orthogonal Frequency Division Multiplexing (OFDM) can turn frequency selective multipath channel to a set of parallel flat fading channels. A statistical model, which is called Jakes model, is used to model the Rayleigh flat fading channel. In this model, the auto-correlation function (ACF) changes with the channel Doppler rate. Estimating of the fading channel, in the receiver, is an important challenge to coherently detect of the received symbols. One method to estimate the fading channel is based on noisy AR model. The received training sequence is modeled by a noisy AR model then using the existing estimation methods the AR parameters are estimated. Using the AR estimates we can estimate the Doppler frequency. In this thesis, a method is proposed to improve the channel parameters estimates. The proposed method includes a serial connection of a noisy AR parameter estimation algorithm and Kalman filter. To performance comparison several computer simulations are performed. Finally, performance of the proposed method is compared with the other existing estimation algorithms in term of bit error rate