David Blanco

Radar-Target Identification via Exponential Extinction-Pulse Synthesis

New contributions to noncooperative radar-target discrimination using only the scattered response of conductive objects are presented in this paper. The technique studied is the extinction-pulse (E-pulse), which makes use of natural resonances as discrimination features. The E-pulse expansion using complex exponential functions as basis functions is proposed, obtaining new E-pulses with characteristics completely different from those in the literature. Specifically, a weighting factor is added to modulate the exponential frequency, providing E-pulses with better discrimination capability. Numerical results achieved in the discrimination between thin straight wires of different lengths show that the proposed exponential E-pulses improve the discrimination results with respect to other types of E-pulses in the literature.

An asymptotically unbiased E-pulse-based scheme for radar target discrimination

This communication proposes an E-pulse-based scheme for radar target discrimination that provides asymptotically correct results for any level of additive white noise contaminating the radar signal. After multiple sampling of the signal dispersed by the target, it is analytically shown that the cross correlation between the output signals of the E-pulse designed for the standard target, corresponding to two different sampling periods, is asymptotically null, regardless of the amount of contaminating noise. The results obtained by simulation have allowed us to propose a discrimination criterion that produces better results than the original E-pulse technique at very low signal-to-noise ratio (SNR) levels.

ICA in signals with multiplicative noise

Independent component analysis (ICA) has been shown in the last few years to be a very useful tool in blind separation of sources and feature extraction. However, at least in its simpler form, its utility is reduced to the case that the outputs are linear mixtures of independent sources. This excludes signals with multiplicative noise. In this paper, ICA is extended to this situation. In order to do this, the special structure that appears in this new model is first studied, and then, the multiplicative ICA method is designed such that it uses this structure to find the mixture of the sources in the noisy environment. The local and global convergence properties of the method are also studied and its performance compared with standard ICA methods.

Extinction pulses synthesis for radar target discrimination using /spl beta/-splines, new E-pulse conditions

The extinction pulse method has been proven to be a suitable method for radar target discrimination using the natural resonance annihilation concept. The standard procedure for extinction pulse (E-pulse) construction is based on an expansion on subsectional polynomials. In this paper it is proposed a new formalism for E-pulse construction using beta-splines. This formalism allows the E-pulse polynomial basis expansion to be treated in a unified theoretical framework and leads to a simplification of the original problem due to the linear nature of all the involved parameters. This new formulation has been also used to impose new conditions over the spectral contents of the E-pulses. These E-pulses constructed using the new conditions annihilate better the natural modes in the late-time radar target response, and provide a better discrimination rates than the classical scheme

Independent component analysis in signals with multiplicative noise using fourth-order statistics

The existence of multiplicative noise greatly limits the applicability of independent component analysis (ICA), because it does not take into account the existence of the noise. This paper proposes a method to extend ICA to this kind of noisy environment, without any limitation in the nature of the sources or the noise. In order to do this, the statistical structure of a linear transformation of the noisy data is studied up to fourth order, and then this structure is used to find the inverse of the mixing matrix through the minimization of a cost function. The method designed is able to extract the mixing matrix and some statistical features of the noise and the sources, notably improving the performance of the standard ICA methods when the mixture is contaminated by multiplicative noise.

The use of ICA in multiplicative noise

When a linear mixture of independent sources is contaminated by multiplicative noise, the problems of blind source separation and feature extraction are highly complex. Specifically, the approach followed by the independent component analysis does not produce proper results. This is because the output of a linear transformation of the noisy data cannot be independent. However, the statistic of this output possesses a special structure that can be used to obtain the original mixture. In this paper, this statistical structure is studied and a general approach to solving the problem is stated, studying how the strategies followed by the standard ICA methods can be adapted in this case. To illustrate the analysis, the results of two different methods that follow the general approach are presented, where the improvement with respect the standard ICA method is clear.

The Averaged, Overdetermined, and Generalized LMS Algorithm

This paper provides and exploits one possible formal framework in which to compare and contrast the two most important families of adaptive algorithms: the least-mean square (LMS) family and the recursive least squares (RLS) family. Existing and well-known algorithms, belonging to any of these two families, like the LMS algorithm and the RLS algorithm, have a natural position within the proposed formal framework. The proposed formal framework also includes - among others - the LMS/overdetermined recursive instrumental variable (ORIV) algorithm and the generalized LMS (GLMS) algorithm, which is an instrumental variable (IV) enable LMS algorithm. Furthermore, this formal framework allows a straightforward derivation of new algorithms, with enhanced properties respect to the existing ones: specifically, the ORIV algorithm is exported to the LMS family, resulting in the derivation of the averaged, overdetermined, and generalized LMS (AOGLMS) algorithm, an overdetermined LMS algorithm able to work with an IV. The proposed AOGLMS algorithm overcomes - as we analytically show here - the limitations of GLMS and possesses a much lower computational burden than LMS/ORIV, being in this way a better alternative to both algorithms. Simulations verify the analysis.

A fourth-order stationarity and ergodicity conditions for harmonic processes

This paper considers the problem of estimating the fourth-order cumulant sequence of a harmonic process. Both stationarity and ergodicity conditions for this kind of signal are derived through the imposition of restrictions to the frequencies and amplitudes that define the signals. Statistical properties for the mean value and the variance of the sample mean estimator are deduced as well. These conditions are applied to cubic-phase coupling detection, stressing the study of nonergodic signals and possible strategies to study them. Numerical examples to illustrate these conditions for cubic phase coupling detection and separation of mixed signals using independent component analysis (ICA) methods are also showed in this paper.

Non Cooperative Radar Target Identification Using Exponential Single-Mode Extraction Pulse

We present a new contribution to the single-mode extraction pulse (S-pulse) technique. The S-pulse technique uses the natural frequencies of the target as discrimination features in such a way that the field scattered by a target after it passes through a S-pulse designed for this specific target has the form of a damped sinusoid. The S-pulses are usually constructed as a linear combination of cosine or polynomial basis functions, expressing a S-pulse function as a linear combination of these functions and fixing the linear parameters with conditions on the Laplace transform. The S-pulses are synthesized using complex exponential basis functions within which are extra parameters that can be used to tune their discrimination capabilities. The proposed S-pulses are compared with the ones in the literature using thin conductive wires as simulation targets and in this way we show that there is a distinct improvement in their discriminatory capacity.

ICA method for speckle signals [blind source separation application]

Independent component analysis (ICA) has shown success in the separation of sources in lots of applications. Almost all of them assume that a set of recorded signals is the result of a linear mixture of independent sources. Although ICA methods were firstly designed to apply only to free-noise signals, numerous methods have extended it to deal with additive noise, using only higher order statistics. However, in speckle environment signals the noise is multiplicative, so the applicability of ICA is seriously reduced. This paper proposes an ICA method for speckle signals, taking into account the multiplicative nature of the noise and improving the results obtained by standard ICA methods.