Danilo Orlando

Track-Before-Detect Strategies for STAP Radars

In this correspondence we propose track-before-detect (TBD) strategies for space-time adaptive processing (STAP) radars. As a preliminary step we introduce the target and noise models in discrete-time form. Then, resorting to generalized likelihood ratio test (GLRT)-based and ad hoc procedures we derive detectors for two different scenarios (a point better clarified in the body of the correspondence). The preliminary performance assessment, conducted resorting to Monte Carlo simulation, shows that the proposed procedures might be viable means to implement early detection and track initiation of weak moving targets.

Track-Before-Detect Algorithms for Targets with Kinematic Constraints

We propose and assess new algorithms for adaptive detection and tracking based on space-time data. At design stage we take into account possible spillover of target energy to adjacent range cells and assume a target kinematic model. Then, resorting to the generalized likelihood ratio test (GLRT) we derive track-before-detect (TBD) algorithms that can operate in scan-to-scan varying scenarios and, more important, that ensure the constant false track acceptance rate (CFTAR) property with respect to the covariance matrix of the disturbance. Moreover, we also propose CFTAR versions of the maximum likelihood-probabilistic data association (ML-PDA) algorithm capable of working with data from an array of sensors. The preliminary performance assessment, conducted resorting to Monte Carlo simulation, shows that the proposed TBD structures outperform the ML-PDA implementations especially in terms of probability of track detection (and for low signal-to-noise ratio (SNR) values).

Persymmetric Rao and Wald Tests for Partially Homogeneous Environment

This letter deals with the problem of adaptive detection in partially-homogeneous Gaussian disturbance with unknown but persymmetric structured covariance matrix. Since no uniformly most powerful test exists for the problem at hand, we devise and assess two detection strategies based on the Rao test and the Wald test design criteria. Remarkably, both detectors ensure the constant false alarm rate property with respect to both the structure of the covariance matrix as well as the power level. The preliminary performance assessment, conducted by resorting to simulated data, has confirmed the effectiveness of the newly proposed detectors.

Detection Algorithms to Discriminate Between Radar Targets and ECM Signals

We address adaptive detection of coherent signals backscattered by possible point-like targets or originated from electronic countermeasure (ECM) systems in presence of thermal noise, clutter, and possible noise-like interferers. In order to come up with a class of decision schemes capable of discriminating between targets and ECM signals, we resort to generalized likelihood ratio test (GLRT) implementations of a generalized Neyman-Pearson rule (i.e., for multiple hypotheses). The adaptive detectors rely on secondary data, free of signal components, but sharing the statistical characterization of the noise in the cell under test. The performance assessment focuses on an adaptive beamformer orthogonal rejection test (ABORT)-like detector; analytical expressions for the probability of false alarm, the probability of detection of the target, and the probability of blanking the ECM (coherent) signal are given. More remarkably, it guarantees the constant false alarm rate (CFAR) property. The performance assessment shows that it can outperform the adaptive sidelobe blanker (ASB) in presence of ECM systems.

A Unifying Framework for Adaptive Radar Detection in Homogeneous Plus Structured Interference— Part II: Detectors Design

This paper deals with the problem of adaptive multidimensional/multichannel signal detection in homogeneous Gaussian disturbance with unknown covariance matrix and structured (unknown) deterministic interference. The aforementioned problem extends the well-known Generalized Multivariate Analysis of Variance (GMANOVA) tackled in the open literature. In Part I of this paper, we have obtained the Maximal Invariant Statistic (MIS) for the problem under consideration, as an enabling tool for the design of suitable detectors which possess the Constant False Alarm Rate (CFAR) property. Herein, we focus on the development of several theoretically founded detectors for the problem under consideration. First, all the considered detectors are shown to be function of the MIS, thus proving their CFARness property. Second, coincidence or statistical equivalence among some of them in such a general signal model is proved. Third, strong connections to well-known (simpler) scenarios analyzed in adaptive detection literature are established. Finally, simulation results are provided for a comparison of the proposed receivers.

A Subspace-Based Adaptive Sidelobe Blanker

We propose a modified version of the adaptive sidelobe blanker (ASB) consisting of a generalized likelihood ratio test (GLRT)-based subspace detector followed by the adaptive coherence estimator. The performance analysis shows that it possesses the constant false alarm rate property with respect to the unknown covariance matrix of the noise in homogeneous environment and that it guarantees a wider range of ldquodirectivityrdquo values with respect to the plain ASB. The probability of false alarm and the probability of detection (the latter for matched signals only) have been evaluated in closed form in homogeneous environment and by resorting to Monte Carlo simulation for the other considered cases.

A Rao Test With Enhanced Selectivity Properties in Homogeneous Scenarios

This correspondence focuses on the design of a selective receiver for homogeneous scenarios. To this end, at the design stage it is assumed that the cell under test contains a noise-like interferer in addition to thermal noise, clutter, and to the possible signal of interest; a set of secondary data, free of signal components, is available: such data share a common covariance matrix that is equal to that of thermal noise plus clutter in the cell under test. Under the above assumptions, an adaptive detector implementing the RAO test is designed. A preliminary performance assessment, conducted assuming that the cell under test and the secondary data share one and the same covariance matrix of the overall disturbance (including possible noise-like interferers), shows that the newly proposed detector can provide enhanced rejection capabilities with respect to its natural competitor (the W-ABORT). It can also outperform the W-ABORT for matched signals.

A Unifying Framework for Adaptive Radar Detection in Homogeneous Plus Structured Interference— Part I: On the Maximal Invariant Statistic

This paper deals with the problem of adaptive multidimensional/multichannel signal detection in homogeneous Gaussian disturbance with unknown covariance matrix and structured deterministic interference. The aforementioned problem corresponds to a generalization of the well-known Generalized Multivariate Analysis of Variance (GMANOVA). In this Part I of the paper, we formulate the considered problem in canonical form and, after identifying a desirable group of transformations for the considered hypothesis testing, we derive a Maximal Invariant Statistic (MIS) for the problem at hand. Furthermore, we provide the MIS distribution in the form of a stochastic representation. Finally, strong connections to the MIS obtained in the open literature in simpler scenarios are underlined.

Persymmetric adaptive detection of distributed targets in partially-homogeneous environment

In this paper we deal with the problem of detecting distributed targets in the presence of Gaussian noise with unknown but persymmetric structured covariance matrix. In particular, we consider the so-called partially-homogeneous environment, where the cells under test (primary data) and the training samples (secondary data), which are free of signal components, share the same structure of the interference covariance matrix but different power levels. Under the above assumptions, we derive the generalized likelihood ratio test (GLRT) and the so-called two-step GLRT. Remarkably, the new receivers ensure the constant false alarm rate property with respect to both the structure of the covariance matrix as well as the power level. The performance assessment, conducted by resorting to both simulated data and real recorded dataset, highlights that the proposed detectors can significantly outperform their unstructured counterparts, especially in a severely heterogeneous scenario where a very small number of secondary data is available.

Adaptive Radar Detection and Localization of a Point-Like Target

In the present paper, we focus on the design of adaptive decision schemes for point-like targets; the proposed algorithms can take advantage of the possible spillover of target energy between consecutive matched filter samples. To this end, we assume that the received useful signal is known up to a complex factor modeled as a deterministic parameter; moreover, it is embedded in correlated Gaussian noise with unknown covariance matrix. Finally, for estimation purposes we assume that a set of secondary data, free of signal components, but sharing the same covariance matrix of the noise in the cells containing signal returns, up to a possibly different scale factor, is available. Remarkably, the proposed decision schemes can provide accurate estimates of the target position within the cell under test and ensure the desirable constant false alarm rate property with respect to the unknown noise parameters.