1,721,030 research outputs found
Sensor Failure Detection for TDOA-based Localization Systems
Get PDFThis paper outlines a strategy for identifying sensors in a passive locating system that are not functioning properly. The framework is based on the information extracted from delay estimation errors, obtained from solving a system of equations in which the cross- and cross-cross-correlation methods are both used. Hence, we remove equations with the highest errors and use a statistical test to identify which sensor is experiencing failure. Our approach is analyzed through numerical simulations and real-recorded data, and compared to heuristic and conventional methods to prove its advantages
Adaptive Radar Detection of Dim Moving Targets in Presence of Range Migration
No full textThis letter addresses adaptive radar detection of dim moving targets. To circumvent range migration, the detection problem is formulated as a multiple hypothesis test and solved applying model order selection rules which allow to estimate the "position" of the target within the CPI and eventually detect it. The performance analysis shows that the newly proposed architectures can provide an accurate estimate of the target position along with improved detection performance with respect to existing competitors
A Tracking System Exploiting Interaction Between a Detector With Localization Capabilities and the KF
No full textA Unifying Framework for Adaptive Radar Detection in Homogeneous Plus Structured Interference-Part II: Detectors Design
No full textThis 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
On the Statistical Invariance for Adaptive Radar Detection in Partially Homogeneous Disturbance Plus Structured Interference
No full textThis paper deals with the problem of adaptive vector subspace signal detection in partially homogeneous Gaussian disturbance and structured (unknown) deterministic interference within the framework of invariance theory. It is first proved that the Maximal Invariant Statistic (MIS) for the problem at hand is scalar-valued and coincides with the well-known adaptive normalized matched filter evaluated after data projection in the complementary subspace of the interfering signal. Second, the statistical characterization of the MIS under both hypotheses is derived. Then, it is shown the statistical equivalence of (two-step) generalized-likelihood ratio test, Rao and Wald tests, as well as the more recently considered Durbin and Gradient test, to the above statistic. Finally, simulation results are provided to confirm our findings and analyze the performance trend of the MIS with the relevant parameters
User Localization for Rescue Operations Exploiting the Cross-Cross-Correlations of Signals from Multiple Sensors
Get PDFDelay estimation of incoming signals in passive systems is still nowadays at the base of many signal processing applications ranging from passive radars to underwater acoustics, indoor acoustic positioning, and others. This paper aims at improving the estimation of the delays with respect to multiple sensing nodes for user localization for rescue operations under the unavailability of the base stations in the area of interest. To this end, it suitably exploits a method grounded on the computation of the cross-correlation between the cross-correlation estimates (say cross-cross-correlation) of the received signals. The estimation problem is formulated as a least squares (LS) optimization problem. As a consequence, the proposed method inherits an important feature of the LS approach, namely that is independent of the underlying data distributions. The performance assessment is conducted in comparison with its classic counterpart
Diffuse multipath exploitation for adaptive radar detection
No full textWe deal with the problem of detecting point-like targets in diffuse multipath environments, modeling the target echo as the superposition of a deterministic signal with an unknown scaling factor (due to the direct path) plus a zero-mean complex circular symmetric Gaussian random vector with an unknown covariance matrix (accounting for the echoes from the glistening surface). We devise a constrained Generalized Likelihood Ratio Test (GLRT) for the resulting hypothesis testing problem, enforcing the primary data covariance matrix (due to both interference and multipath echoes) to belong to a neighborhood of the secondary data sample covariance matrix. Remarkably, the proposed decision scheme ensures the desirable Constant False Alarm Rate (CFAR) property with respect to the unknown parameters of the interference. The performance assessment, conducted on simulated data in terms of detection probability also in comparison with existing solutions, highlights the effectiveness of the new approach to cope with diffuse multipath phenomena
Adaptive Radar Detection of a Subspace Signal Embedded in Subspace Structured Plus Gaussian Interference Via Invariance
Get PDFThis paper deals with adaptive radar detection of a subspace signal competing with two sources of interference. The former is Gaussian with unknown covariance matrix and accounts for the joint presence of clutter plus thermal noise. The latter is structured as a subspace signal and models coherent pulsed jammers impinging on the radar antenna. The problem is solved via the Principle of Invariance which is based on the identification of a suitable group of transformations leaving the considered hypothesis testing problem invariant. A maximal invariant statistic, which completely characterizes the class of invariant decision rules and significantly compresses the original data domain, as well as its statistical characterization are determined. Thus, the existence of the optimum invariant detector is addressed together with the design of practically implementable invariant decision rules. At the analysis stage, the performance of some receivers belonging to the new invariant class is established through the use of analytic expressions
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