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This paper deals with a symbiotic radar, defined as a passive radar that is an integral part of a communication network. The symbiotic radar is integrated with an IEEE 802.22 Wireless Regional Area Network and linked with the base station. It can work as a purely passive radar or, and this is the novelty in the system, can use the base station to suggest the best customer premise equipment that should be scheduled for transmission to improve tracking performance. This paper defines a cognitive passive tracking algorithm that exploits the feedback information contained in the target state prediction to improve the performance while preserving the communication capabilities of the complete network.

This work focuses on a passive coherent location (PCL) system that exploits the signals emitted by IEEE 802.22 devices, referred hereafter as a white-space PCL (WS-PCL) system. To cope with the very low transmitted equivalent isotropically radiated power of the IEEE 802.22 emitters, we focus on the design of a WS-PCL system that exploits all the useful signals, emitted from both the base station and customer-premises equipment, received in each frame. In this work, we study the feasibility of a WS-PCL system by deriving the receiver operating characteristic and defining the multistatic velocity profiling algorithm for the estimation of the target velocity vector. The performance of the proposed receiver is compared to that of a WS-PCL system that exploits only the signal emitted by the base station.

A coordinated dynamic sensor network of autonomous underwater gliders to estimate three-dimensional time-varying environmental fields is proposed and tested. Integration with a network of surface relay nodes and asynchronous consensus are used to distribute local information and achieve the global field estimate. Field spatial sparsity is considered, and field samples are acquired by compressive sensing devices. Tests on simulated and real data demonstrate the feasibility of the approach with relative error performance within 10%.

In this work, we analyze the sea clutter data collected simultaneously by the bistatic and monostatic nodes of an S-band netted radar system under different out-of-plane geometries. The empirical distribution of sea clutter data are fitted to some known heavy-tailed distributions, and the sea spikes statistics are examined. Our analyses show that spikiness is higher for small bistatic angles and that bistatic data are sometimes less spiky than monostatic ones only for horizontal polarization.

This paper concerns the correction of refracted propagation effects for airborne radar tracking. An algorithm based on the Kalman filter (KF) is developed that compensates for range and elevation refraction errors, which were analytically derived using a simplified tropospheric model. In particular, the refraction error model applies to a spherically stratified troposphere and does not include the effects of azimuthal refraction errors, small-scale fluctuations, or ducting. The effectiveness of the proposed tracking algorithm is assessed in the presence of tropospheric parameter mismatch and measurement noise. Both theoretical and numerical results are described for a medium range scenario, and a quasi-ellipsoidal racetrack is chosen for the air platform that carries the radar.

This correspondence is devoted to the study of the impact of flight disturbances due to the atmospheric turbulence on airborne radar tracking. The performance of a turbulence-ignorant tracking filter is assessed in a turbulent simulated scenario. The turbulence is modelled according to the Dryden model, a correlated, zero-mean, Gaussian random process. A quasi-ellipsoidal racetrack course is chosen for the air platform that carries the radar. The performance of the turbulence-ignorant tracking algorithm is analyzed in terms of mean value and standard deviation of the estimation errors for each component of the position and velocity vectors and compared with the posterior Cramer-Rao lower bound (PCRLB), evaluated for the ideal case of absence of platform vibrations.

This paper deals with the problem of detecting a radar target signal against correlated non-Gaussian clutter, which is modeled by the compound-Gaussian distribution. We prove that if the texture of compound-Gaussian clutter is modeled by an inverse-gamma distribution, the optimum detector is the optimum Gaussian matched filter detector compared to a data-dependent threshold that varies linearly with a quadratic statistic of the data. We call this optimum detector a linear-threshold detector (LTD). Then, we show that the compound-Gaussian model presented here varies parametrically from the Gaussian clutter model to a clutter model whose tails are evidently heavier than any K-distribution model. Moreover, we show that the generalized likelihood ratio test (GLRT), which is a popular suboptimum detector because of its constant false-alarm rate (CFAR) property, is an optimum detector for our clutter model in the limit as the tails get extremely heavy. The GLRT-LTD is tested against simulated high-resolution sea clutter data to investigate the dependence of its performance on the various clutter parameters.

In this paper we deal with the problem of calculating the Cramer-Rao lower bounds (CRLBs) for bistatic radar channels. To this purpose we exploited the relation between the ambiguity function (AF) and the CRLB. The bistatic CRLBs are analyzed and compared with the monostatic counterparts as a function of the bistatic geometric parameters. In the bistatic case both geometry factors and transmitted waveforms play an important role in the shape of the AF, and therefore in the estimation accuracy of the target range and velocity. In particular, the CRLBs depend on the target direction of arrival (DOA), the bistatic baseline length (BBL), and the distance between the target and the receiver. The CRLBs are then used to select the "optimum" bistatic channel (or set of channels) for the tracking of a radar target moving along a trajectory in a multistatic scenario.

This paper deals with the problem of estimating the parameters, i.e., complex amplitudes, directions of arrival (DOAs), and Doppler frequencies, of multiple radar targets present in the same range-azimuth resolution cell of a surveillance radar, by joint processing the sum and delta channel data. The AML-RELAX (asymptotically maximum likelihood) estimator previously derived by the authors is extended to a two-channel system scenario. Both the cases of targets having the same Doppler frequencies and different Doppler frequencies are considered. Performance is numerically investigated through Monte Carlo simulation and compared with that of the single channel estimator, which exploits knowledge of the sum antenna main beam only. Performance of the AML-RELAX estimator is also compared with that of two monopulse estimators for single and multiple target scenarios.

This work presents a single-scan-processing approach to the problem of detecting and preclassifying a radar target that may belong to different target classes. The proposed method is based on a hybrid of the maximum a posteriori (MAP) and Neyman-Pearson (NP) criteria and guarantees the desired constant false alarm rate (CFAR) behavior. The targets are modeled as subspace random signals having zero mean and given covariance matrix. Different target classes are discriminated based on their different signal subspaces, which are specified by their corresponding projection matrices. Performance is investigated by means of numerical analysis and Monte Carlo simulation in terms of probability of false alarm, detection and classification; the extra signal-to-noise power ratio (SNR) necessary to classify once target detection has occurred is also derived.

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).

The effect on radar detection of the shape of the Doppler spectrum of windblown vegetated ground clutter is analyzed. An extensive experimental campaign carried out by MIT Lincoln Laboratory showed the shape of the windblown clutter Doppler spectrum to be exponential, in contrast to two other widely-used spectral models (i.e., Gaussian and power-law). The detection performance of two classes of surveillance radar, ground-based and Airborne Early Warning, is evaluated and compared against windblown ground clutter using the three above-mentioned Doppler spectral shapes. The analysis is first done with closed-form equations, and subsequently validated using a set of measured clutter data. It is demonstrated that the exponentially shaped Doppler spectrum should be used as the reference to model windblown ground clutter for the prediction of radar detection performance.

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.

Adaptive detection of fluctuating radar targets in unknown correlated Gaussian clutter has received considerable attention in the past. On the other hand, the problem of adaptive detection in non-Gaussian environments is still under investigation. Adaptive coherent radar detection of Swerling I targets against compound-Gaussian clutter is addressed. Our contribution is (1) to present a detection algorithm, called the adaptive linear-quadratic (ALQ) detector, with constant false-alarm rate (CFAR) behavior with respect to the clutter amplitude probability density function (apdf) and that is quite insensitive to the clutter correlation structure, and (2) to investigate and compare the performance of the ALQ detector and Kelly's generalized likelihood ratio test (GLRT) against K-distributed 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.

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.

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. >