Frequency estimation for single-carrier and OFDM signals in communication and radar systems

Estimating the frequency of a signal embedded in additive white Gaussian noise is one of the classical problems in signal processing. It is of fundamental importance in various applications such as in communications, Doppler radar, synthetic aperture radar (SAR), array processing, radio frequency identification (RFID), resonance sensor, etc. The requirement on the performance of the frequency estimator varies with the application. The performance is often defined using four indexes: i). estimation accuracy, ii). estimation range, iii). estimation threshold, and iv). implementation complexity. These indexes may be in contrast with each other. For example, achieving a low threshold usually implies a high complexity. Likewise, good estimation accuracy is often obtained at the price of a narrow estimation range. The estimation becomes even more difficult in the presence of fading-induced multiplicative noise which is considered to be the general case of the frequency estimation problem. There have been a lot of efforts in deriving the estimator for the general case, however, a generalized estimator that fulfills all indexes can be hardly obtained. Focusing on communications and radar applications, this thesis proposes a new generalized closed-form frequency estimator that compromises all performance indexes. The derivation of the proposed estimator relies on the nonlinear least-squares principle in conjunction with the well known summationby-parts formula. In addition to this, several modified frequency estimators suitable for non-fading or very slow fading scenarios, are also introduced in this thesis.