Structures for radar detection in compound Gaussian clutter

The problem of coherent radar target detection in a background of non-Gaussian clutter modeled by a compound Gaussian distribution is studied here. We show how the likelihood ratio may be recast into an estimator-correlator form that shows that an essential feature of the optimal detector is to compute an optimum estimate of the reciprocal of the unknown random local power level. We then proceed to show that the optimal detector may be recast into yet another form, namely a matched filter compared with a data-dependent threshold. With these reformulations of the optimal detector, the problem of obtaining suboptimal detectors may be systematically studied by either approximating the likelihood ratio directly, utilizing a suboptimal estimate in the estimator-correlator structure or utilizing a suboptimal function to model the data-dependent threshold in the matched filter interpretation. Each of these approaches is studied to obtain suboptimal detectors. The results indicate that for processing small numbers of pulses, a suboptimal detector that utilizes information about the nature of the non-Gaussian clutter can be implemented to obtain quasi-optimal performance. As the number of pulses to be processed increases, a suboptimal detector that does not require information about the specific nature of the non-Gaussian clutter may be implemented to obtain quasi-optimal performance     

Cramer-Rao bounds and estimation of the parameters of the Gumbeldistribution

Maximum Likelihood (ML) algorithms and Cramer-Rao (CR) bounds for the location and scale parameters of the Gumbel distribution are discussed. First we consider the case in which the scale parameter is known, obtaining the estimator of the location parameter by solving the likelihood equation and then evaluating its performance. We next consider the case where both the location parameter and the scale parameter are unknown and need to be estimated simultaneously from the reference samples. For this case, performance is analyzed by means of Monte Carlo simulation and compared with the asymptotic CR bound     

Magnetic Turbulence in the Geospace Environment

Magnetic turbulence is found in most space plasmas, including the Earth’s magnetosphere, and the interaction region between the magnetosphere and the solar wind. Recent spacecraft observations of magnetic turbulence in the ion foreshock, in the magnetosheath, in the polar cusp regions, in the magnetotail, and in the high latitude ionosphere are reviewed. It is found that: 1. A large share of magnetic turbulence in the geospace environment is generated locally, as due for instance to the reflected ion beams in the ion foreshock, to temperature anisotropy in the magnetosheath and the polar cusp regions, to velocity shear in the magnetosheath and magnetotail, and to magnetic reconnection at the magnetopause and in the magnetotail. 2. Spectral indices close to the Kolmogorov value can be recovered for low frequency turbulence when long enough intervals at relatively constant flow speed are analyzed in the magnetotail, or when fluctuations in the magnetosheath are considered far downstream from the bow shock. 3. For high frequency turbulence, a spectral index α?2.3 or larger is observed in most geospace regions, in agreement with what is observed in the solar wind. 4. More studies are needed to gain an understanding of turbulence dissipation in the geospace environment, also keeping in mind that the strong temperature anisotropies which are observed show that wave particle interactions can be a source of wave emission rather than of turbulence dissipation. 5. Several spacecraft observations show the existence of vortices in the magnetosheath, on the magnetopause, in the magnetotail, and in the ionosphere, so that they may have a primary role in the turbulent injection and evolution. The influence of such a turbulence on the plasma transport, dynamics, and energization will be described, also using the results of numerical simulations.     

Validation of windblown radar ground clutter spectral shape

We investigate the robustness of the linear matched filter (MF) operating in a Gaussian environment in the presence of a mismatch between the design clutter-power spectral density (PSD) shape and the actual one. The Gaussian, the power-law (PL), and the double-exponential spectral models have been considered with the goal of investigating which one fits best for windblown foliage. We analyze the MF performance in terms of improvement factor, probability of false alarm, and probability of detection by making use of the theoretical models and measured X-band ground clutter data. The numerical results validate the double-exponential spectral model for windblown foliage by showing that the differences in performance prediction between using measured clutter data and modeled clutter data of various spectral shapes (viz., Gaussian, FL, and double-exponential) are minimized when the spectral model employed is of double-exponential shape     

Particle acceleration by stochastic fluctuations and dawn-dusk electric field in the Earth’s magnetotail

This work is devoted to investigate the interaction between protons and stochastic time-dependent electromagnetic fields generated by oscillating clouds of finite size, randomly positioned in the x–y  -plane. The geometry of the system is two-dimensional and, beside the time-dependent electromagnetic fluctuations, a steady-state, dawn-dusk electric field, _Ey$E_y$, has been added along the y-direction. The simultaneous presence of the stochastic time-dependent fluctuations and of the constant electric field component in the same system gives rise to two types of acceleration mechanisms operating on test particles: a second order Fermi-like process and a direct acceleration. By performing a parametric study, we extensively study the contribution of the two processes to proton acceleration. The energy values reached by test particles in this simple model are in good agreement with those observed in the Earth’s magnetotail region. Possible applications to the problem of particle acceleration in the terrestrial magnetosphere are widely discussed, and guidelines for future works are drawn.     

Statistical analyses of measured radar ground clutter data

The performance of ground-based surveillance radars strongly depends on the distribution and spectral characteristics of ground clutter. To design signal processing algorithms that exploit the knowledge of clutter characteristics, a preliminary statistical analysis of ground-clutter data is necessary. We report the results of a statistical analysis of X-band ground-clutter data from the MIT Lincoln Laboratory Phase One program. Data non-Gaussianity of the in-phase and quadrature components was revealed, first by means of histogram and moments analysis, and then by means of a Gaussianity test based on cumulants of order higher than the second; to this purpose parametric autoregressive (AR) modeling of the clutter process was developed. The test is computationally attractive and has constant false alarm rate (CFAR). Incoherent analysis has also been carried out by checking the fitting to Rayleigh, Weibull, log-normal, and K-distribution models. Finally, a new modified Kolmogorov-Smirnoff (KS) goodness-of-fit test is proposed; this modified test guarantees good fitting in the distribution tails, which is of fundamental importance for a correct design of CFAR processors     

Optimum and mismatched detection against K-distributed plusGaussian clutter

We derive the optimum radar receiver to detect fluctuating and non-fluctuating targets against a disturbance which is modeled as a mixture of coherent K-distributed and Gaussian-distributed clutter. In addition, thermal noise, which is always present in the radar receiver, is considered. We discuss the implementation of the optimum coherent detector, which derives from the likelihood ratio test under the assumption of perfectly known disturbance statistics, and evaluate its performance via a numerical procedure, when possible, and via Monte Carlo simulation otherwise. Moreover, we compare the performance of the optimum detector with those of two detectors which are optimum for totally Gaussian and totally K-distributed clutter respectively, when they are fed with such a mixed disturbance. We conclude that, though the optimum detector has a larger computational cost, it provides sensibly better detection performance than the mismatched detectors in a number of operational situations. Thus, there is a need to derive suboptimum target detectors against the mixture of disturbances which trade-off the detection performance and the implementation complexity     

A mission concept for the low-cost large-scale exploration and characterisation of near earth objects

This paper presents the preliminary mission and science analysis of a new mission concept for the large scale, low-cost exploration of Near Earth Objects (NEOs). The concept is to enable close range observations of NEOs by performing close flybys of a series of NEOs at one of their nodal points, with pairs of small spacecraft flying in formation. The paper presents a preliminary assessment of accessible asteroids and multi-target tour trajectories from data available in the JPL small-body database.The main instruments on board each spacecraft are a camera and a LIDAR which together can be used for orbit determination, surface imaging, direct asteroid ranging and asteroid mass estimation via intersatellite ranging. The paper provides a qualitative and quantitative assessment of the measurable quantities during each flyby. In particular, the feasibility of a novel method of NEO mass estimation is assessed.     

DOA estimation by exploiting the amplitude modulation induced by antenna scanning

We propose a beamsplitting-like approach to estimate the directions of arrival (DOA) of multiple radar targets present in the mainlobe of a rotating antenna. The proposed method is based on the maximum likelihood (ML) technique and it avoids the need for a difference channel by exploiting knowledge of the antenna main beam pattern. Two scenarios are considered: multiple targets with unknown deterministic complex amplitudes and multiple targets with Gaussian distributed random complex amplitudes. The performance of the proposed estimator is investigated through Monte Carlo simulation and it is compared with the Cramer-Rao lower bound (CRLB).     

Multiple radar targets estimation by exploiting induced amplitude modulation

This work deals with the problem of estimating complex amplitudes, Doppler frequencies, and directions of arrival (DOA) of multiple targets present in the same range-azimuth resolution cell of a surveillance radar. The maximum likelihood (ML) and the asymptotic (large sample size) ML (AML) estimators are derived. To reduce the computational complexity of the maximization of the nonlinear two-dimensional criterion function of the AML estimator, we propose a computationally efficient algorithm based on the RELAXation method. It allows decoupling the problem of jointly estimating the parameters of the signal components into a sequence of simpler problems, where the parameters of each component are separately and iteratively estimated. The proposed method overcomes the resolution limitation of the classical monopulse technique and resolves multiple targets exhibiting an arbitrarily small difference in azimuth as long as their Doppler frequencies differ at least by the inverse of the number of integrated pulses, provided that enough signal-to-noise ratio (SNR) per pulse is available. The performance of the proposed AML-RELAX estimator is numerically investigated through Monte Carlo simulation and Cramer-Rao lower bound (CRLB) calculation.