In this letter, a new and accurate frequency estimation method of complex exponential signals is proposed. The proposed method divides the signal samples into several identical segments and sums up the samples belonging to the same segment respectively. Then it utilizes fast Fourier transform (FFT) algorithm with zero-padding to obtain a coarse estimation, and exploits three Fourier coefficients to interpolate a fine estimation based on least square error (LSE) criterion. Numerical results show that the proposed method can closely approach the Cramer-Rao bound (CRB) at low signal-to-noise ratios (SNRs) with different estimation ranges. Furthermore, the computational complexity of the proposed method is proportional to the estimation range, showing its practical-oriented ability. The proposed method can be useful in several applications involving carrier frequency offset (CFO) estimation for burst-mode satellite communications.

In this paper, we consider the problem of waveform optimization for multi-input multi-output (MIMO) radar in the presence of signal-dependent noise. A novel diagonal loading (DL) based method is proposed to optimize the waveform covariance matrix (WCM) for minimizing the Cramer-Rao bound (CRB) which improves the performance of parameter estimation. The resulting nonlinear optimization problem is solved by resorting to a convex relaxation that belongs to the semidefinite programming (SDP) class. An optimal solution to the initial problem is then constructed through a suitable approximation to an optimal solution of the relaxed one (in a least squares (LS) sense). Numerical results show that the performance of parameter estimation can be improved considerably by the proposed method compared to uncorrelated waveforms.

In this paper, challenges regarding the provision of channel state information (CSI) and carrier frequency synchronization for orthogonal frequency division multiplexing (OFDM) systems with null subcarriers are addressed. We propose novel maximum likelihood (ML) based schemes that estimate the aggregate effects of the CFO and channel by using two successive OFDM preambles. In the presented scheme, CFO is estimated by considering the phase rotation between two consecutive received OFDM preambles. Both single input single output (SISO) as well as multiple input multiple output (MIMO) OFDM systems are considered. The mean squared errors (MSE) of the channel and CFO are used to evaluate the performance of our proposed scheme. By using two successive OFDM preambles, the estimation of channel and the estimation of CFO are decoupled, which leads to a simple estimation method. Simulation results show that the BER performance of the proposed estimators is comparable to that of known channel state information and the CFO MSE performance achieves the Cramer-Rao bound (CRB) of the fully loaded OFDM system.

This paper focuses on the development of Cramer-Rao Bound (CRB) expressions for passive source location estimation in various Gaussian noise environments. The scenarios considered involve an unknown deterministic source signal with a short time duration, and additive general Gaussian noise. The mathematical derivation procedure presented is applicable to non-stationary Gaussian noise problems. Specifically, explicit closed-form CRB expressions are presented using the spectrum representation of the signal and noise for stationary Gaussian noise cases.