Iterative CZT-based frequency offset estimation for frequency-selective channels

In this paper, we present an accurate frequency offset estimation method for frequency-selective channels. Through the iterative use of the chirp z-transform (CZT) algorithm, an accurate frequency offset estimator is proposed, approaching the Cramer-Rao Bound (CRB) even at low signal-to-noise ratio (SNR). Further, the estimation can be achieved within one block of training sequence, thus avoiding the transmission of repetitive known blocks as is usually required in many conventional methods. Meanwhile, the overall complexity is acceptable. More importantly, the CZT operation can utilize the fast Fourier transform (FFT) structure that is favourable for digital signal processor (DSP) implementation. Simulation results show that two or at most three iterations of the CZT computation are sufficient for an accurate frequency offset estimation in the SNR range from 0 dB to 30 dB. © 2005 IEEE.published_or_final_versio

Estimating Frequency by Interpolation Using Least Squares Support Vector Regression

Discrete Fourier transform-(DFT-) based maximum likelihood (ML) algorithm is an important part of single sinusoid frequency estimation. As signal to noise ratio (SNR) increases and is above the threshold value, it will lie very close to Cramer-Rao lower bound (CRLB), which is dependent on the number of DFT points. However, its mean square error (MSE) performance is directly proportional to its calculation cost. As a modified version of support vector regression (SVR), least squares SVR (LS-SVR) can not only still keep excellent capabilities for generalizing and fitting but also exhibit lower computational complexity. In this paper, therefore, LS-SVR is employed to interpolate on Fourier coefficients of received signals and attain high frequency estimation accuracy. Our results show that the proposed algorithm can make a good compromise between calculation cost and MSE performance under the assumption that the sample size, number of DFT points, and resampling points are already known

FMCW Signals for Radar Imaging and Channel Sounding

A linear / stepped frequency modulated continuous wave (FMCW) signal has for a long time been used in radar and channel sounding. A novel FMCW waveform known as “Gated FMCW” signal is proposed in this thesis for the suppression of strong undesired signals in microwave radar applications, such as: through-the-wall, ground penetrating, and medical imaging radar. In these applications the crosstalk signal between antennas and the reflections form the early interface (wall, ground surface, or skin respectively) are much stronger in magnitude compared to the backscattered signal from the target. Consequently, if not suppressed they overshadow the target’s return making detection a difficult task. Moreover, these strong unwanted reflections limit the radar’s dynamic range and might saturate or block the receiver causing the reflection from actual targets (especially targets with low radar cross section) to appear as noise. The effectiveness of the proposed waveform as a suppression technique was investigated in various radar scenarios, through numerical simulations and experiments. Comparisons of the radar images obtained for the radar system operating with the standard linear FMCW signal and with the proposed Gated FMCW waveform are also made. In addition to the radar work the application of FMCW signals to radio propagation measurements and channel characterisation in the 60 GHz and 2-6 GHz frequency bands in indoor and outdoor environments is described. The data are used to predict the bit error rate performance of the in-house built measurement based channel simulator and the results are compared with the theoretical multipath channel simulator available in Matlab

Colocated multiple-input multiple-output radars for smart mobility

In recent years, radars have been used in many applications such as precision agriculture and advanced driver assistant systems. Optimal techniques for the estimation of the number of targets and of their coordinates require solving multidimensional optimization problems entailing huge computational efforts. This has motivated the development of sub-optimal estimation techniques able to achieve good accuracy at a manageable computational cost. Another technical issue in advanced driver assistant systems is the tracking of multiple targets. Even if various filtering techniques have been developed, new efficient and robust algorithms for target tracking can be devised exploiting a probabilistic approach, based on the use of the factor graph and the sum-product algorithm. The two contributions provided by this dissertation are the investigation of the filtering and smoothing problems from a factor graph perspective and the development of efficient algorithms for two and three-dimensional radar imaging. Concerning the first contribution, a new factor graph for filtering is derived and the sum-product rule is applied to this graphical model; this allows to interpret known algorithms and to develop new filtering techniques. Then, a general method, based on graphical modelling, is proposed to derive filtering algorithms that involve a network of interconnected Bayesian filters. Finally, the proposed graphical approach is exploited to devise a new smoothing algorithm. Numerical results for dynamic systems evidence that our algorithms can achieve a better complexity-accuracy tradeoff and tracking capability than other techniques in the literature. Regarding radar imaging, various algorithms are developed for frequency modulated continuous wave radars; these algorithms rely on novel and efficient methods for the detection and estimation of multiple superimposed tones in noise. The accuracy achieved in the presence of multiple closely spaced targets is assessed on the basis of both synthetically generated data and of the measurements acquired through two commercial multiple-input multiple-output radars