1,721,072 research outputs found
Reciprocity Evaluation in Heterogeneous Polarimetric SAR Images
Full text linkIn this letter, an automatic method to validate the reciprocity theorem on full-polarimetric heterogeneous synthetic aperture radar (SAR) data is derived. The study extends, to the more general heterogeneous scenario, the work of [1], where the conformity with the reciprocity is studied in the homogeneous case. At the design stage, it is assumed that the pixels in the polarimetric image share the same covariance structure but different power levels. Then, the dependence on nuisance parameters is removed resorting to the Principle of Invariance. The resulting problem is formalized as a binary hypothesis test and is solved through the generalized likelihood ratio test (GLRT). Tests are conducted both on simulated and real-recorded data to show the superiority of the proposed GLRT with respect to its homogeneous counterpart
Polarimetric Covariance Eigenvalues Classification in SAR Images
No full textThis letter proposes a novel technique for automatic classification of the dominant scattering mechanisms associated with the pixels of polarimetric SAR images. Focusing on the heterogeneous scenario wherein the polarimetric image pixels share the same covariance but different power levels, the original data are replaced by a maximal invariant statistic in order to remove the dependence on the scaling factors. Then, the classification problem is formulated as a multiple hypothesis test which is addressed by applying the model order selection rules. The performance analysis is conducted on both simulated and measured data and points out the effectiveness of the proposed approach
Accurate Delay Estimation for Multi-Sensor Passive Locating Systems Exploiting the Cross-Correlation between Signals Cross-Correlations
Full text linkThe availability of accurate estimates of the delay or time of arrival (TOA) of the incoming signals is of paramount importance for the position estimation in passive radars with multiple receivers. This correspondence aims at improving estimation of the delays by multiple detectors exploiting the cross-correlation between the cross-correlation estimates (say cross-cross-correlation) of the received signals. The resulting equation system is formulated as a least squares (LS) minimization problem, whose solution is efficiently found computing the pseudo-inverse of the model matrix. In fact, the cross-cross-correlation implicitly performs a filtering operation on the considered signal, approximating the generalized cross-correlator behavior, without using statistical information about the signal spectra. The proposed method is numerically validated in comparison with classic counterparts and theoretical bounds
Screening Polarimetric SAR Data via Geometric Barycenters for Covariance Symmetry Classification
Full text linkThis letter proposes a robust framework for polarimetric covariance symmetries classification in Synthetic Aperture Radar (SAR) images applying a pre-screening on the data looks before they are used to perform inferences. More specifically, the devised method improves the performance of a previous work based on the exploitation of the special structures assumed by the covariance/coherence matrix when symmetric scattering mechanisms dominate the polarimetric returns. To do this, the algorithm selects first the most homogeneous data through the cancellation of those sharing the highest Generalized Inner Product (GIP) values computed with the use of the geometric barycenters. Then, the procedure based on Model Order Selection (MOS) developed in the homogeneous case is applied on the filtered data. The conducted tests show the potentiality of the proposed method in correctly classifying the observed scene of L-band real-recorded SAR data with respect to its standard counterpart
A Robust Framework for Covariance Classification in Heterogeneous Polarimetric SAR Images and Its Application to L-Band Data
No full textIn this paper, an automatic classification approach for polarimetric covariance structure is derived and assessed. It extends the framework of Pallotta et al. "Detecting Covariance Symmetries in Polarimetric SAR Images" to the heterogeneous environment, where the pixels of the polarimetric image share the same covariance structure but different power levels. The Principle of Invariance is exploited to replace the original data with a suitable statistic whose distribution is independent of the scale factors. Then, the classification problem is formulated in terms of a multiple hypotheses test and solved by means of model order selection rules. The behavior of the newly devised classifiers is first assessed over simulated data also in comparison with the analogous counterparts for a homogeneous environment. Next, the classification performances are evaluated on real measured data corroborating the satisfactory results highlighted in the simulations
On the maximal invariant statistic for adaptive radar detection in partially homogeneous disturbance with persymmetric covariance
No full textThis letter deals with the problem of adaptive signal detection in partially homogeneous and persymmetric Gaussian disturbance within the framework of invariance theory. First, a suitable group of transformations leaving the problem invariant is introduced and the maximal invariant statistic (MIS) is derived. Then, it is shown that the (two-step) generalized-likelihood ratio test, Rao, and Wald tests can be all expressed in terms of the MIS, thus proving that they all ensure a constant false-alarm rate
SAR image registration in the presence of rotation and translation : a constrained least squares approach
Full text linkThis letter proposes a coregistration algorithm to compensate for possible inaccuracy of trajectory sensor during the SAR image acquisition process. Such a misalignment can be modeled as a pure displacement in range and azimuth directions and a rotation effect due to different angle of sight. The approach is formalized as a Constrained Least Squares (CLS) optimization problem enforcing a constraint of absence of a zooming effect between the two SAR images. Moreover, system equations can optionally be weighted according to local properties between the extracted patches within the quoted couple. Interestingly, the solution can be obtained in closed-form, therefore with a low computational cost. The results of the tests conducted on the 9.6GHz Gotcha SAR data demonstrate the capability of the strategy to proper register the imagery
DOA Refinement through Complex Parabolic Interpolation of a Sparse Recovered Signal
Full text linkThis letter considers the design of a two-stage direction of arrival (DOA) scheme for radar systems. Precisely, at the first stage a sparse recovery approach is used to obtain both DOA and complex amplitude estimates of the incoming signal. Since the DOA is evaluated on a predefined grid of bins sampling the antenna azimuth mainbeam, at the second stage, a closed-form complex-valued parabolic interpolation is performed to refine it. By doing so, the angle accuracy is improved, but at the same time maintaining fixed the overall computational complexity. Numerical results show the enhancement provided by the proposed procedure to the initial sparse recovery method
Rule-Based Scheduling for MPARs Performing Sensing and Communications
Full text linkMultifunction phased array radar (MPAR) is capable of performing sensing and communications by functionally grouping a phased array into tailored sub-apertures, each dedicated to a distinct task. Because of limited available resources, such as bandwidth, power aperture product, and time, it is important to properly allocate them to each sub-aperture. This article examines a rule-based task scheduling algorithm wherein communication (COM) looks are employed to fill the vacant time left by the radar tasks that are allocated first (namely, volume and cued search, update and confirmation tracking). The allocation of looks is carried out for each time slot based on task priorities; however, some tasks (i.e., volume, cued, and COM) are executed in parallel when the available bandwidth and power-aperture product (PAP) permit. Simulations conducted in scenarios demonstrate the validity of the proposed allocation strategy in terms of bandwidth utilization and time occupancy
Detecting Sensor Failures in TDOA-based Passive Radars: A Statistical Approach based on Outlier Distribution
Full text linkNon-cooperative target location is accomplished by means of multiple passive radar receivers deployed in the region of interest and that detect the delayed replicas of the signal emitted by the target and estimate the time difference of arrival. However, in realistic scenarios, some of the involved sensors could not correctly work if the sensor is victim of intentional/unintentional interference and/or physical damage of the device or its communication link. Thus, procedures for failure detection become of primary interest to discard the measurements related to the out-of-order sensors. The approach proposed in this paper identifies sensors under failure from the analysis of the errors in the equation system implemented to estimate the delays. More precisely, we first compute the second and fourth order correlations (namely, cross-correlation and cross-cross-correlation, i.e. the cross-correlation between signals' cross-correlations) of the incoming signals to build up the system of equation. Then, we perform a sequential cancellation of the equations that experience the highest errors. A statistical test based on the number of canceled equations related to a specific sensor is used to state whether or not the specific sensor is under failure. Finally, the performance of the entire failure detection architecture is assessed by numerical simulations also in comparison with a heuristic method based on the percentages of canceled equations and its standard counterparts not performing any outlier screening
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