Thanks to its improved capabilities, the Multiple Input Multiple Output (MIMO) radar is attracting the attention of researchers and practitioners alike. Because it transmits orthogonal or partially correlated waveforms, this emerging technology outperformed the phased array radar by providing better parametric identifiability, achieving higher spatial resolution, and designing complex beampatterns. To avoid jamming and enhance the signal to noise ratio, it is often interesting to maximize the transmitted power in a given region of interest and minimize it elsewhere. This problem is known as the transmit beampattern design and is usually tackled as a two-step process: a transmit covariance matrix is firstly designed by minimizing a convex optimization problem, which is then used to generate practical waveforms. In this work, we propose simple novel methods to generate correlated waveforms using finite alphabet constant and non-constant-envelope symbols. To generate finite alphabet waveforms, the proposed method maps easily generated Gaussian random variables onto the phase-shift-keying, pulse-amplitude, and quadrature-amplitude modulation schemes. For such mapping, the probability density function of Gaussian random variables is divided into M regions, where M is the number of alphabets in the corresponding modulation scheme. By exploiting the mapping function, the relationship between the cross-correlation of Gaussian and finite alphabet symbols is derived. The second part of this thesis covers the topic of target parameter estimation. To determine the reflection coefficient, spatial location, and Doppler shift of a target, maximum likelihood estimation yields the best performance. However, it requires a two dimensional search problem. Therefore, its computational complexity is prohibitively high. So, we proposed a reduced complexity and optimum performance algorithm which allows the two dimensional fast Fourier transform to jointly estimate the spatial location and Doppler shift. To assess the performance of the proposed estimators, the Cramér-Rao Lower Bound (CRLB) is derived. Simulation results show that the mean square estimation error of the proposed estimators achieve the CRLB. Keywords: Collocate antennas, multiple-input multiple-output (MIMO) radar, Finite alphabet waveforms, Hermite polynomials, Reflection coefficient, Doppler, Spatial location, Cramér-Rao Lower Bound.