Fulvio Gini

Frequency offset and symbol timing recovery in flat-fading channels: a cyclostationary approach

Two open-loop algorithms are developed for estimating jointly frequency offset and symbol timing of a linearly modulated waveform transmitted through a frequency-flat fading channel. The methods exploit the received signals second-order cyclostationarity and, with respect to existing solutions: (1) they take into account the presence of time-selective fading effects; (2) they do not need training data; (3) they do not rely on the Gaussian assumption of the complex equivalent low-pass channel process; and (4) they are tolerant to additive stationary noise of any color or distribution. Performance analysis of the proposed methods using Monte Carlo simulations, unifications, and comparisons with existing approaches are also reported.

The modified Cramer-Rao bound in vector parameter estimation

In this paper we extend the scalar modified Cramer-Rao bound (MCRB) to the estimation of a vector of nonrandom parameters in the presence of nuisance parameters. The resulting bound is denoted with the acronym MCRVB, where V stands for vector. As with the scalar bound, the MCRVB is generally looser than the conventional CRVB, but the two bounds are shown to coincide in some situations of practical interest. The MCRVB is applied to the joint estimation of carrier frequency, phase, and symbol epoch of a linearly modulated waveform corrupted by correlated impulsive noise (encompassing white Gaussian noise as a particular case), wherein data symbols and noise power are regarded as nuisance parameters. In this situation, calculation of the conventional CRVB is infeasible, while application of the MCRVB leads to simple useful expressions with moderate analytical effort. When specialized to the case of white Gaussian noise, the MCRVB yields results already available in the literature in fragmentary form and simplified contexts.

Vector subspace detection in compound-Gaussian clutter. Part I: survey and new results

Deals with the problem of detecting subspace random signals against correlated non-Gaussian clutter exploiting different degrees of knowledge on target and clutter statistical characteristics. The clutter process is modeled by the compound-Gaussian distribution. In the first part of the paper, the optimum Neyman-Pearson (NP) detector, the generalized likelihood ratio test (GLRT), and a constant false-alarm rate (CFAR) detector are sequentially derived both for the Gaussian and the compound-Gaussian scenarios. Different interpretations of the various detectors are provided to highlight the relationships and the differences among them. In particular, we show how the GLRT detector may be recast into an estimator-correlator form and into another form, namely a generalized whitening-matched filter (GWMF), which is the GLRT detector against Gaussian disturbance, compared with a data-dependent threshold. In the second part of this paper, the proposed detectors are tested against both simulated data and measured high resolution sea clutter data to investigate the dependence of their performance on the various clutter and signal parameters.

On the use of Cramer-Rao-like bounds in the presence of random nuisance parameters

We focus on the use of Cramer-Rao-like bounds for the estimation of a deterministic parameter in the presence of random nuisance parameters. As a working example, we consider the problem of estimating the carrier frequency offset affecting a linearly modulated waveform received through a Rician fading channel. We calculate the relevant Cramer-Rao bound (CRB) and also a set of Cramer-Rao-like bounds proposed in the literature, among which we mention the hybrid CRB, the modified CRB and the Miller-Chang (1978) bound. An extension of the latter bound is also provided. The relative merits of these bounds are discussed, both in terms of their tightness and ease of calculation. A few useful inequalities involving the above bounds are also established.

Vector subspace detection in compound-Gaussian clutter. Part II: performance analysis

For pt. I see ibid., vol. 38, no. 4, p. 1295 (2002). In this second part we deal with the problem of detecting subspace random signals against correlated non-Gaussian clutter modeled by the compound-Gaussian distribution. In the first part of the paper, we derived the optimum Neyman-Pearson (NP) detector, the generalized likelihood ratio test (GLRT), and a constant false-alarm rate (CFAR) detector; we also provided some interesting interpretations of them. In this second part, these detectors are tested against both simulated data and measured high resolution sea clutter data to investigate the dependence of their performance on the various clutter and signal parameters. Numerical examples concern a space-time adaptive processing (STAP) scenario and a ground-based surveillance radar system scenario.

Cramer-Rao bounds in the parametric estimation of fading radiotransmission channels

The context of this paper is parameter estimation for linearly modulated digital data signals observed on a frequency-flat time-selective fading channel affected by additive white Gaussian noise. The aim is the derivation of Cramer-Rao lower bounds for the joint estimation of all those channel parameters that impact signal detection, namely, carrier phase, carrier frequency offset (Doppler shift), frequency rate of change (Doppler rate), signal amplitude, fading power, and Gaussian noise power. Time-selective frequency-flat fading is modeled as a low-pass autoregressive multiplicative distortion process. In particular, the important case of slow fading, with the multiplicative process remaining constant over the whole data burst, is specifically discussed. Asymptotic expressions of the bounds, valid for a large observed sample or for high signal-to-noise ratio (SNR), are also derived in closed form. A few charts with numerical results are finally reported to highlight the dependence of the bounds on channel status (SNR, fading bandwidth, etc.).

Time-averaged subspace methods for radar clutter texture retrieval

Subspace approaches have become popular in the last two decades for retrieving constant amplitude harmonics observed in white additive noise because they may exhibit superior resolution over the FFT-based methods, especially with short data records and closely spaced harmonics. We demonstrate that MUSIC and ESPRIT methods can also be applied when the harmonics are corrupted by white or wideband multiplicative noise. The application context is the retrieval of texture information from high resolution and low grazing angle radar clutter data affected by wideband colored speckle that is modeled as complex multiplicative noise. Texture information is fundamental for clutter cancellation and constant false alarm rate (CFAR) radar detection. A thorough numerical analysis compares the two subspace methods and validates the theoretical findings.

Vessel detection and classification: An integrated maritime surveillance system in the Tyrrhenian sea

In recent years a number of organizations, both national and international, have put significant efforts in developing knowledge-based integrated maritime surveillance (IMS) systems. The final aim is to have a clear picture of the position, classification, identification and movement of cooperative and non-cooperative targets entering and leaving the 200 nautical miles limit of the Exclusive Economic Zone (EEZ). Each sensor (i.e. satellite-based, ground-based, shipborne or airborne) has its own task and, in such a context, high frequency (HF) surface wave (SW) radars are inexpensive tools for long range early warning applications in open waters. They allow maximizing the effectiveness in dealing with fisheries protection, drug interdiction, illegal immigration, terrorist threats, search and rescue tasks. This paper focuses on the possibility of combining automatic identification system (AIS) data with HFSWR data for vessel detection and classification purposes. Three algorithms for target detection in compound Gaussian HF sea clutter are presented and their performance evaluated. The combined use of AIS plots provided by cooperative targets can allow the operator to discriminate non-cooperative targets and possible threats. The concurrent exploitation of AIS and HFSWR data is presented and discussed by means of real data recorded during the NURC experiment in the northern Tyrrhenian Sea in May 2009.

Weighted subspace fitting of interferometric phases for multibaseline SAR interferometry

In this paper, we deal with the problem of exploiting baseline diversity of a multichannel interferometric SAR system to overcome the layover problem. The problem arises when different height contributions collapse in the same range-azimuth resolution cell, due to the presence of strong terrain slopes or discontinuities in the sensed scene. The multilook framework is adopted to counteract the presence of the space varying distortion, termed speckle, which is due to the extended nature of natural targets; to this purpose we introduce a weighted subspace fitting approach to estimate the interferometric phases. Although the approach does not take the speckle into account, it is found to yield estimates superior to previously examined methods.

Parameter estimation of hybrid hyperbolic FM and polynomial phase signals using the multi-lag high-order ambiguity function

Parameter estimation for a combination of a polynomial phase signal (PPS) and a hyperbolic frequency modulation (FM) is addressed. A novel approach is proposed that allows one to decouple estimation of the FM parameters from that of the PPS, exploiting the properties of the multi-lag high-order ambiguity function (ml-HAF). The accuracy achievable by any unbiased estimator of the hybrid FM-PPS parameters is investigated by means of the Cramer-Rao lower bounds (CRLB). Performance analysis is carried out and the CRLBs are compared with simulation results.