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This paper proposes a simple non‐adaptive approach for the suppression of direct signal and clutter contributions in a passive radar system based on orthogonal frequency division multiplexing transmissions, exploiting the properties of a reciprocal filter (RF). The use of a RF allows to circumvent the limits posed by the waveform ambiguity function, producing a data‐independent time‐invariant response to a stationary point‐like target echo at the output of the range compression stage. This feature enables simple clutter cancellation strategies, based on the subtraction of delayed portions of the surveillance signal, according to the conventional moving target indication methodologies. The proposed processing scheme consists of a range compression stage based on a RF, followed by a Single Canceller. Its performance is investigated against a simulated and an experimental data set, for the case of a digital video broadcasting‐terrestial‐based passive radar scenario, and it is compared to an alternative scheme based on a conventional matched filter and adaptive cancellation techniques. The proposed approach is shown to yield a perfect cancellation of the clutter returns from a stationary scene under ideal conditions. Moreover, it proves to be robust to real‐world conditions, despite its considerably limited computational load compared to adaptive clutter cancellation techniques. [ABSTRACT FROM AUTHOR]

In this paper, we devise a new technique for the fusion of a sequence of multitemporal single-channel synthetic aperture radar (SAR) images of a given area with a single multiband optical image. Unlike for SAR, the availability of optical images is largely affected by atmospheric conditions, so that this is a case of practical interest. First, a statistical model for the joint distribution of SAR and optical data is provided. Then, a split-merge test based on this model is derived, and its performance is evaluated both analytically and using a Monte Carlo simulation. A new segmentation technique is introduced (OPT MUM), based on the test and on a region-growing scheme. The effectiveness of the proposed technique for the fusion of multitemporal SAR and multiband optical images is tested on synthetic and real images. Results show that the proposed scheme allows to both 1) discriminate characteristics that would be impossible to distinguish using only a single sensor and 2) increase the overall discrimination performance, even when each sensor has its own discrimination capability. Index Terms--Data fusion, multiband optical, multitemporal synthetic aperture radar (SAR), unsupervised segmentation.

In this paper, we derive two techniques for the classification of multifrequency/multitemporal polarimetric SAR images, based respectively on a statistical and on a neural approach. Both techniques are especially designed to exploit the spatial structure of the observed scene, thus allowing more stable classification results. Such techniques are useful when looking at medium- to large-scale features, like the boundaries between urban and nonurban areas. They are applied to a set of SIR-C images of a urban area, to test their effectiveness in the identification of the different classes that compose the observed scene. A lower and an upper bound to the classification performance are introduced to characterize their limits. They correspond respectively to pixel-by-pixel classification and to the joint classification of the pixels belonging to the different classes identified in the ground truth. The results achieved with the two approaches are quantitatively analyzed by comparing them to the ground truth. Moreover, a hybrid approach is presented, where the homogeneous regions identified through statistical segmentation are classified using a neurofuzzy technique. Finally, a quantitative analysis of the results achieved with all the proposed techniques is carried out, showing that their classification performance is much higher than the lower bound and reasonably close to the upper bound. This is a consequence of their effectiveness in the exploitation of the spatial information. Index Terms--Image processing, synthetic aperture radar (SAR), spatial analysis, urban areas.

A new technique, named diagonal polarimetric merge-using-moments (DPOL MUM), is proposed for the segmentation of multifrequency polarimetric synthetic aperture radar (SAR) images that exploits the characteristic block diagonal structure of their covariance matrix. This technique is based on the newly introduced split-merge test, which has a reduced fluctuation error than the straight extension of the polarimetric test (POL MUM) and is shown to yield a more accurate segmentation on simulated SAR images. DPOL MUM is especially useful in the extraction of information from urban areas that are characterized by the presence of different spectral and polarimetric characteristics. Its effectiveness is demonstrated by applying it to segment a set of SIR-C images of the town of Pavia. The classification of the image segmented with DPOL MUM shows higher probability of correct classification compared to POL MUM and to a similar technique that does not use the correlation properties (MT MUM). Index Terms--Polarimetry, multifrequency, unsupervised segmentation, urban areas.

This paper deals with multitemporal sequences of synthetic aperture radar (SAR) images with regions possibly affected by step reflectivity patterns of change. Specifically, it addresses the problems of detecting a temporal step pattern with small reflectivity change against a constant pattern and of estimating the transition instant for the step. The statistically optimized signal processing techniques proposed in this work are most appropriate for the new generation of SAR systems with high revisit time that are currently under development. We propose two different techniques, based on the maximum likelihood (ML) approach, that make different use of prior knowledge on the radar cross section (RCS) levels of the searched pattern. They process the whole sequence to achieve optimal discrimination capability between regions affected and not affected by a step change and optimal estimation accuracy for the step transition instant. The first technique (known step pattern (KSP)-detector) assumes complete knowledge of the RCS levels of the searched pattern of change, while the second one (USP-detector) is based on the assumption of totally unknown step pattern. Both analytical and simulated evaluation of the detection and estimation performance of the techniques is obtained, which shows the large improvement achievable using longer sequences instead of only two images. The practical effectiveness of the technique on real data is shown by applying the USP-detector to a sequence of 10 ERS-1 SAR images of forest and agricultural areas.

In this paper, we address the problem of deriving adequate detection and classification schemes to fully exploit the information available in a sequence of SAR images. In particular, we address the case of detecting a step reflectivity change pattern against a constant pattern. Initially we propose two different techniques, based on a Maximum Likelihood approach, that make different use of prior knowledge on the searched pattern. They process the whole sequence to achieve optimal discrimination capability between regions affected and not affected by a step change, The first technique (KSP-detector) assumes a complete knowledge of the pattern of change, while the second one (USP-detector) is based on the assumption of a totally unknown pattern. A fully analytical expression of the detection performances of both techniques is obtained, which shows the large improvement achievable using longer sequences instead of only two images. By comparing the two techniques it is also apparent that KSP achieves better performance, but the USP-detector is more robust. As a compromise solution, a third technique is then developed, assuming a partial knowledge of the pattern of change, and its performance is compared to the previous ones. The practical effectiveness of the technique on real data is shown by applying the USP-detector to a sequence of 10 ERS-SAR images of forest and agricultural areas, which is also used to validate the theoretical results. Index Terms--Change detection, multitemporal. SAR.

Exact expressions are derived for the performance of predetection maximal ratio combiner diversity reception with L correlated branches in Nakagami fading. Bit error rates are evaluated for both coherent and noncoherent binary phase-shift-keying and frequency-shift-keying signals, starting from the L-variate moment generating function of the random input power vector. The new formulation presented for the bit error rate, in which the covariance matrix of the fading at the L branches explicitly appears, allows arbitrary branch correlation to be taken into account for any diversity order in the case of identical fading severity on the branches. Results are presented for evaluation of the outage probability, for integer values of fading severity, as well as for the effect of the presence of unbalanced channels with arbitrary correlation. Index Terms - Digital communication, diversity methods, fading channels.