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The statistical behavior of the eigenvalues of the sample covariance matrix (SCM) plays a key role in determining the performance of adaptive beamformers (ABF) in presence of noise. This paper presents a method to compute the approximate eigenvalue density function (EDF) for the SCM of a cylindrically isotropic noise field when only a finite number of shapshots are available. The EDF of the ensemble...
A novel framework is developed that decomposes a matrix into sparse factors. The sparse matrix decomposition scheme is utilized to determine in a distributed fashion which sensors, in a sensor network, acquire informative data about phenomena of interest. A setting, where the sensor data covariance matrix consists of hidden sparse factors, is considered. The proposed sparsity-cognizant algorithm is...
Blind source separation problem has recently drawn a lot of attention in unsupervised neural learning. In the current approaches, the additive noise is typically negligible so that it can be omitted from the consideration. To be applicable in realistic scenarios, blind source separation approaches should deal evenly with the presence of noise. In this contribution, a noisy multiple channels blind...
This note gives a brief survey on discrete-time stochastic iterative learning control (SILC) from three aspects, namely, SILC for linear system, nonlinear system and system with other stochastic signal. Two major approaches, stochastic Kalman filtering approach and stochastic approximation approach, for SILC are proposed. Some open questions are also included.
Performance of the Unscented Kalman Filter, UKF, for nonlinear stochastic discrete-time systems is investigated. It is proved that under certain conditions, the estimation error of the UKF remains bounded. Furthermore, it is shown that the design of noise covariance matrix plays an important role in improving the stability of the UKF algorithm. It is further shown the estimation error remains bounded...
This paper describes an unknown input filtering framework for the state estimation of nonlinear systems with arbitrary unknown inputs. It is known that the celebrated extended Kalman filter (EKF) may have poor performance due to the lack of the true dynamics of the unknown input. A possible remedy to improve the performance is to apply an EKF-like nonlinear version of the recently developed ERTSF...
In order to solve the problem of inaccurate state estimation and divergent outputs of the filter of the low-cost integrated navigation system, a strong tracking augmented unscented kalman filter is proposed in this paper. This method extends the strong tracking filter principle into the augmented unscented kalman filter, which improves the strong tracking ability of the system states mutation. Using...
The interactions between subsystems are important for large-scale systems. We introduce a local strongly coupled system which coupled by random communication between subsystems.Due to the intermittent communication, it is difficult to apply the standard Kalman or robust filter to design procedures to such systems. In this paper, we addressed the distributed robust filter design method for this kind...
A novel feature extraction method is proposed in this paper. Dislike contour-based or region-based approaches, an object is first converted to a closed curve by extended central projection (ECP). The derived curve not only keeps the affine transform information, but also is very robust to noise. Then whitening transform is performed to the curve such that the affine transformation is simplified to...
This paper focuses on de Bruijn identity, a fundamental result that relates two important concepts: Fisher information and differential entropy. A novel relationship with Stein identity and extensions of de Bruijn identity are first presented. Then several applications of de Bruijn identity in deriving the Bayesian Cramér-Rao lower bound (BCRLB), Cramér-Rao lower bound (CRLB), and a new lower bound,...
Many subspace estimation techniques assume either that the system has a calibrated array or that the noise covariance matrix is known. If the noise covariance matrix is unknown, training or other calibration techniques are used to find it. In this paper another approach to the problem of unknown noise covariance is presented. The complex factor analysis (FA) and a new extended version of this model...
In many statistical signal processing applications, the quality of the estimation of parameters of interest plays an important role. We focus in this paper, on the estimation of the covariance matrix. In the classical Gaussian context, the Sample Covariance Matrix (SCM) is the most often used, since it is the Maximum Likelihood estimate. It is easy to manage and has a lot of well-known statistical...
In this paper, we consider the detection of a deterministic signal with an unknown scaling amplitude in the presence of a colored noise, when there is a covariance mismatch between the null and alternative hypotheses. Specifically, we consider a scenario where the target incurs an additional subspace interference that is orthogonal to the target steering vector and only present under the alternative...
In this paper we derive a generic signal processing model for oversampled linear antenna arrays based on network theory and Nyquist sampling theory. The theoretical model is verified with experimental data collected on an HF OTHR receive array.
This paper investigates bias compensation for improving the performance of target tracking using range or range difference measurements. We obtain the Maximum Likelihood estimate of the target position at the current instant and pass it to the Kalman filter as observation to obtain the target track. The nonlinear relationship between the target position and measurements creates bias that can degrade...
In this paper, the problem of multiantenna spectrum sensing in cognitive radio (CR) is addressed within a Bayesian framework. Unlike previous works, our Bayesian model places priors directly on the spatial covariance matrices under both hypotheses, as well as on the probability of channel occupancy. Specifically, we use inverse-gamma and complex inverse-Wishart distributions as conjugate priors for...
Space-time adaptive processing (STAP) is used in radar to adaptively suppress both ground clutter returns and radio frequency interference (RFI). However, RFI suppression utilizes degrees-of-freedom that would otherwise be applied to clutter suppression. This paper considers designing the transmit pulse so that fast-time correlated (i.e., colored) RFI is suppressed in the pulse-compression stage preceding...
The wide-sense stationary assumption has been frequently employed in array processing, since it results in uncorrelated frequency bins and consequently major simplifications arise. However, unlike stationary signals, significant interfrequency correlations are observable in nonstationary signals like speech. Here, we drop the stationarity assumption and will show that taking interfrequency correlations...
Electronic interconnects may encounter damage under exposure to vibration and mechanical shock. The damage may manifest itself as an open circuit after a finite period of operation. The traditional methods for damage detection such as microscopy and x-ray are destructive in nature and provide limited information offline. In this paper, resistance spectroscopy and phase-sensitive detection based state...
In decentralised target tracking, a set of sensors observes moving targets. When the sensors are static but steerable, each sensor must dynamically choose which target to observe in a decentralised manner. We show that the information exchanged by the sensors to synchronise their beliefs can be exploited to learn a model of the utility function that drives each others' decisions. Instead of communicating...
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