32 research outputs found
Gridless Evolutionary Approach for Line Spectral Estimation with Unknown Model Order
Gridless methods show great superiority in line spectral estimation. These
methods need to solve an atomic norm (i.e., the continuous analog of
norm) minimization problem to estimate frequencies and model order. Since
this problem is NP-hard to compute, relaxations of atomic norm, such as
nuclear norm and reweighted atomic norm, have been employed for promoting
sparsity. However, the relaxations give rise to a resolution limit,
subsequently leading to biased model order and convergence error. To overcome
the above shortcomings of relaxation, we propose a novel idea of simultaneously
estimating the frequencies and model order by means of the atomic norm.
To accomplish this idea, we build a multiobjective optimization model. The
measurment error and the atomic norm are taken as the two optimization
objectives. The proposed model directly exploits the model order via the atomic
norm, thus breaking the resolution limit. We further design a
variable-length evolutionary algorithm to solve the proposed model, which
includes two innovations. One is a variable-length coding and search strategy.
It flexibly codes and interactively searches diverse solutions with different
model orders. These solutions act as steppingstones that help fully exploring
the variable and open-ended frequency search space and provide extensive
potentials towards the optima. Another innovation is a model order pruning
mechanism, which heuristically prunes less contributive frequencies within the
solutions, thus significantly enhancing convergence and diversity. Simulation
results confirm the superiority of our approach in both frequency estimation
and model order selection.Comment: This work has been submitted to the IEEE for possible publication.
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DOA estimation for coexistence of circular and non-circular signals based on atomic norm minimization
In this paper, a gridless DOA estimation method with coexistence of non-circular and circular signals is proposed by employing an enhanced sparse nested array, whose virtual array has no holes. The virtual signals derived from both sum and difference co-arrays are constructed based on atomic norm minimization. Simulation results are provided to demonstrate the performance of the proposed method
Multi-Antenna Dual-Blind Deconvolution for Joint Radar-Communications via SoMAN Minimization
Joint radar-communications (JRC) has emerged as a promising technology for
efficiently using the limited electromagnetic spectrum. In JRC applications
such as secure military receivers, often the radar and communications signals
are overlaid in the received signal. In these passive listening outposts, the
signals and channels of both radar and communications are unknown to the
receiver. The ill-posed problem of recovering all signal and channel parameters
from the overlaid signal is terms as dual-blind deconvolution (DBD). In this
work, we investigate a more challenging version of DBD with a multi-antenna
receiver. We model the radar and communications channels with a few (sparse)
continuous-valued parameters such as time delays, Doppler velocities, and
directions-of-arrival (DoAs). To solve this highly ill-posed DBD, we propose to
minimize the sum of multivariate atomic norms (SoMAN) that depends on the
unknown parameters. To this end, we devise an exact semidefinite program using
theories of positive hyperoctant trigonometric polynomials (PhTP). Our
theoretical analyses show that the minimum number of samples and antennas
required for perfect recovery is logarithmically dependent on the maximum of
the number of radar targets and communications paths rather than their sum. We
show that our approach is easily generalized to include several practical
issues such as gain/phase errors and additive noise. Numerical experiments show
the exact parameter recovery for different JRCComment: 40 pages, 6 figures. arXiv admin note: text overlap with
arXiv:2208.0438
Sensor Signal and Information Processing II
In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing
Advanced multiparametric optimization and control studies for anaesthesia
Anaesthesia is a reversible pharmacological state of the patient where hypnosis, analgesia and muscle relaxation are guaranteed and maintained throughout the surgery. Analgesics block the sensation of pain; hypnotics produce unconsciousness, while muscle relaxants prevent unwanted movement of muscle tone.
Controlling the depth of anaesthesia is a very challenging task, as one has to deal with nonlinearity, inter- and intra-patient variability, multivariable characteristics, variable time delays, dynamics dependent on the hypnotic agent, model analysis variability, agent and stability issues. The modelling and automatic control of anaesthesia is believed to (i) benefit the safety of the patient undergoing surgery as side-effects may be reduced by optimizing the drug infusion rates, and (ii) support anaesthetists during critical situations by automating the drug delivery systems.
In this work we have developed several advanced explicit/multi-parametric model predictive (mp-MPC) control strategies for the control of depth of anaesthesia. State estimation techniques are developed and used simultaneously with mp-MPC strategies to estimate the state of each individual patient, in an attempt to overcome the challenges of inter- and intra- patient variability, and deal with possible unmeasurable noisy outputs.
Strategies to deal with the nonlinearity have been also developed including local linearization, exact linearization as well as a piece-wise linearization of the Hill curve leading to a hybrid formulation of the patient model and thereby the development of multiparametric hybrid model predictive control methodology. To deal with the inter- and intra- patient variability, as well as the noise on the process output, several robust techniques and a multiparametric moving horizon estimation technique have been design and implemented.
All the studies described in the thesis are performed on clinical data for a set of 12 patients who underwent general anaesthesia.Open Acces