Until recently, a lot of work has been done to develop algorithms that utilize the distributed structure of an ad hoc wireless sensor network to estimate a certain parameter of interest. However, all these algorithms assume that the input regressor data is available to the sensors, but this data is not always available to the sensors. In such cases, blind estimation of the required parameter is...

Though Blind Source Separation (BSS) techniques have typically been applied within Operational Modal Analysis (OMA) framework, there joint diagonalization property can also be utilized within traditional input-output Experimental Modal Analysis (EMA) for the purpose of modal parameter estimation. This paper explores this possibility and suggests an approach to achieve the same. The paper presen...

In orthogonal frequency-division multiplexing (OFDM) communications, carrier frequency offset must be estimated and compensated at the receiver to maintain orthogonality. In this paper, we examine the performance of a blind carrier frequency offset estimation algorithm for OFDM communications. Experiments were conducted on a system operating at close to 2 MBits/s. Insights obtained from these e...

Blind system identification (BSI) is a fundamental signal processing technology aimed at retrieving a system’s unknown information from its output only. This technology has a wide range of possible applications such as mobile communications, speech reverberation cancellation, and blind image restoration. This paper reviews a number of recently developed concepts and techniques for BSI, which in...

We analyze a blind channel impulse response identification scheme based on the cross correlation of blind symbol estimates with the received signal. The symbol estimates specified are those minimizing the Godard (or constant modulus) criterion, for which mean-squared symbol estimation error bounds have recently been derived. In this paper, we derive upper bounds for the average squared paramete...

This paper addresses the problems of blind channel estimation and symbol detection with second order statistics methods from the received data. It can be shown that this problem is similar to Direction Of Arrival (DOA) estimation, where many solutions like the MUSIC algorithm or “weighted” techniques (as Deterministic Maximum Likelihood or Weighted Subspace Fitting method) have been developed. ...

Given a set of images, whose pixel values can be considered as the components of a vector, it is interesting to estimate the modulus of such a vector in some localised areas corresponding to a compact signal. For instance, the detection/estimation of a polarized signal in compact sources immersed in a background is relevant in some fields like astrophysics. We develop two different techniques, ...

Blind deconvolution is a challenging problem, but in low-light it even more difficult. Existing algorithms, both classical and deep-learning based, are not designed for this condition. When the photon shot noise strong, conventional methods fail because (1) image does have enough signal-to-noise ratio to perform blur estimation; (2) While deep neural networks powerful, many of them do consider ...