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This paper deals with the problem of quadratic minimization subject to linear equality constraints. Contrary to the standard formulation, we assume the most general case of a possibly singular quadratic form. As we explain, the existing formal solution to this problem has several drawbacks. Our new approach is free from most of these drawbacks. It has a simple physical interpretation and is relatively...
In this paper, we deal with cognitive design of the transmit signal and receive filter optimizing the radar detection performance without affecting spectral compatibility with some licensed overlaid electromagnetic radiators. We assume that the radar is embedded in a highly reverberating environment and exploit cognition provided by Radio Environmental Map (REM), to induce spectral constraints on...
In this paper we present a new sparse iterative covariance-based estimation approach, called SPICE, to the direction of arrival estimation problem. SPICE is obtained by the minimization of a statistically well motivated covariance matrix fitting criterion and can be used in both single and multiple-snapshot cases. Some of the unique features enjoyed by SPICE are: it takes account of the noise in the...
In this paper we introduce a new technique for estimating the parameters of the Keplerian model commonly used in radial velocity data analysis for extrasolar planet detection. The unknown parameters in the Keplerian model, namely eccentricity e, orbital frequency f, periastron passage time T, longitude of periastron ω, and radial velocity amplitude K are estimated by a new approach named SPICE (a...
This paper studies optimal input excitation design for parametric frequency response estimation. The objective is to minimize the uncertainty of functions of the frequency response estimate at a specified frequency ω while limiting the power of the input signal. We focus on least-squares estimation of Finite Impulse Response (FIR) models and minimum variance input design. The optimal input problem...
We introduce an apparently original method for moving-average parameter estimation, based on covariance fitting and convex optimization. The proposed method is shown by means of numerical simulation to provide much more accurate parameter estimates, in difficult scenarios, than a related existing method does. We derive the new method via an analogy with a covariance fitting interpretation of the Capon...
Covert communications are conducted at a low received signal-to-noise ratio (SNR) to prevent interception or detection by an eavesdropper, and successful detection in this particular area heavily relies on the processing gain achieved by employing the direct-sequence spread-spectrum (DSSS) technique. If covert communications take place in underwater acoustic (UWA) environments, then additional challenges...
A mathematical model of the human eye smooth pursuit mechanism was constructed by combining a fourth order nonlinear biomechanical model of the eye plant with a dynamic gain controller model. The biomechanical model was derived based on knowledge of the anatomical properties and characteristics of the extraocular motor system. The controller model structure was chosen empirically to agree with experimental...
One of the main objectives of cognitive radar is to adapt the spectrum of transmit waveforms to certain needs, such as avoiding reserved frequency bands or narrowband interferences. Besides spectral requirements, good correlation properties of the transmit waveforms are also desired in specific applications, such as range compression. Moreover, practical hardware constraints usually require the transmit...
Sparse Bayesian learning (SBL) has been used as a signal recovery algorithm for compressed sensing. It has been shown that SBL is easy to use and can recover sparse signals more accurately than the well-known Basis Pursuit (BP) algorithm. However, the computational complexity of SBL is quite high, which limits its use in large-scale problems. We propose herein an efficient Gibbs sampling approach,...
The problem of estimating a spectral representation of damped sinusoidal signals from a gapped data set is of considerable interest in several applications. In this paper, we propose a new iterative adaptive approach, named dIAA, that provides such an estimate also in the case of irregularly sampled data, a common scenario in, for instance, spectroscopical data measurements. Numerical examples illustrate...
Nuclear quadrupole resonance (NQR) is a radio frequency spectroscopic technique that can be used to detect solid-state compounds containing quadrupolar nuclei, a requirement fulfilled by most high explosives (and narcotics). In this paper, we present an overview of recent research in the detection of explosives using this technique. We also present mathematical models for the data for different acquisition...
Through waveform diversity, multiple-input multiple-output (MIMO) radar can achieve higher resolution and better sensitivity to slowly moving targets than phased-array radar systems. Furthermore, with a MIMO antenna array, the linear independence of reflected signals from scattering points allows for the direct application of adaptive array processing techniques. High levels of noise and strong clutter...
A multi-input multi-output (MIMO) radar system that transmits orthogonal waveforms via its antennas can achieve a greatly increased virtual aperture compared with its phased-array counterpart. Practical radar requirements such as unit peak-to-average power ratio and range compression dictate that we use MIMO radar waveforms that have constant modulus and good auto- and cross-correlation properties...
We introduce a missing data recovery methodology based on a weighted least squares iterative adaptive approach (IAA). The proposed method is referred to as the missing-data IAA (MIAA) and it can be used for uniform or non-uniform sampling as well as for arbitrary data missing patterns. MIAA uses the IAA spectrum estimates to retrieve the missing data, based on a spectral least squares criterion similar...
Unimodular (i.e., constant modulus) sequences with good autocorrelation properties are useful in several areas, including communications, radar and sonar. The integrated sidelobe level (ISL) is often used to express the goodness of the autocorrelation properties of a given sequence. In this paper, we present several cyclic algorithms for the local minimization of ISL-related metrics. To illustrate...
We consider using multi-input multi-output (MIMO) radar to improve the ground moving target indication (GMTI) performance, especially for slowly moving targets, for airborne surveillance systems. The increased virtual aperture afforded by MIMO radar systems enables many advantages, including enhanced spatial resolution, improved parameter identifiability and better performance for GMTI. To obviate...
To reduce the need of secondary data and/or accurate prior knowledge of the clutter statistics in space-time adaptive processing (STAP), we present herein a user parameter-free and secondary data-free fully automatic weighted least squares based iterative adaptive approach (IAA) to angle-Doppler imaging for airborne surveillance radar systems.
We begin by revisiting the plain least-squares periodogram (LSP) for real-valued data. Then we introduce a new method for spectral analysis of non-uniformly sampled data by "iteratively weighting LSP", and we name the new method real-valued iterative adaptive approach (RIAA). LSP and RIAA are most suitable for data sequences with discrete spectra. For such type of data, we present a procedure...
This paper provides a comprehensive review of user parameter-free robust adaptive beamforming algorithms, including ridge regression Capon beamformers (RRCBs), the mid-way (MW) algorithm, the shrinkage based approaches, and iterative beamforming algorithms, namely the iterative adaptive approach (IAA), maximum likelihood based IAA (IAA-ML) and M-SBL (multi-snapshot sparse Bayesian learning). The purpose...
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