3 research outputs found
Efficient and Accurate Frequency Estimation of Multiple Superimposed Exponentials in Noise
The estimation of the frequencies of multiple superimposed exponentials in
noise is an important research problem due to its various applications from
engineering to chemistry. In this paper, we propose an efficient and accurate
algorithm that estimates the frequency of each component iteratively and
consecutively by combining an estimator with a leakage subtraction scheme.
During the iterative process, the proposed method gradually reduces estimation
error and improves the frequency estimation accuracy. We give theoretical
analysis where we derive the theoretical bias and variance of the frequency
estimates and discuss the convergence behaviour of the estimator. We show that
the algorithm converges to the asymptotic fixed point where the estimation is
asymptotically unbiased and the variance is just slightly above the Cramer-Rao
lower bound. We then verify the theoretical results and estimation performance
using extensive simulation. The simulation results show that the proposed
algorithm is capable of obtaining more accurate estimates than state-of-art
methods with only a few iterations.Comment: 10 pages, 10 figure
Electronics for Sensors
The aim of this Special Issue is to explore new advanced solutions in electronic systems and interfaces to be employed in sensors, describing best practices, implementations, and applications. The selected papers in particular concern photomultiplier tubes (PMTs) and silicon photomultipliers (SiPMs) interfaces and applications, techniques for monitoring radiation levels, electronics for biomedical applications, design and applications of time-to-digital converters, interfaces for image sensors, and general-purpose theory and topologies for electronic interfaces
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