Multichannel space-based SAR systems that exploit receiving antenna sub-arrays displaced in the along track-direction have been shown to provide the capability to detect ground moving targets and potentially estimate their radial motion velocity, thus making possible their correct relocation inside the SAR image of the background. However, the use of only two receiving channels to contain cost, mass and data-rate, imposes limitations in terms of potentiality of joint clutter cancellation and target velocity estimation. To overcome this problem we propose a full MIMO SAR scheme obtained by transmitting nearly orthogonal waveforms with the two sub-arrays achieved from a single antenna aperture, and receiving with the two sub-arrays. A complete processing chain for the joint detection, imaging and radial speed estimation is presented and the performance of the proposed MIMO system is deeply investigated and compared to the performance of conventional multichannel systems to assess the relative merits and drawbacks.

This paper presents an effective signal processing scheme to track moving vehicles and to obtain their cross-range profiles with a passive bistatic radar (PBR) based on the signals of opportunity emitted by a WiFi router. While the target detection using WiFi-based PBR has already been studied by the authors, this paper focuses on the targets moving with a low radial velocity component. These are especially interesting since they might have a reasonable cross-range velocity component, which allows us to apply inverse synthetic aperture radar (ISAR) techniques to provide a high-resolution cross-range profile. A specific problem for these targets is the presence of possibly strong echoes from the stationary background (clutter), which tend to mask their contributions. In such cases, the standard Doppler processing does not help in separating the targets from this clutter. Therefore, appropriate clutter cancellation schemes are applied, and their effectiveness and impact are analyzed both on the tracking and on the ISAR profiling. An appropriate ISAR scheme for cross-range profiling is introduced, tailored for the typical short-range and possibly bistatic surveillance scenarios of the WiFi-based PBR; this scheme comprises the automatic estimation from the data of the target motion components up to a higher order than in usual long-range imaging and their compensation. The reliability of the obtained profiles is also investigated, for both the monostatic and bistatic cases, which is essential both for the vehicle size/characteristics estimation and for the automatic recognition schemes based on vehicle databases. The results obtained using an experimental setup developed and fielded at the University of Rome “La Sapienza,” Rome, Italy, show that the considered approach is effective and that the obtained cross-range profiles achieved by ISAR processing with WiFi-based passive radar are quite reliable both in the monostatic and bistatic cases.

This electronic document is a "live" template. The various Conventional civil Air Traffic Control (ATC) systems are able to detect targets and identify collaborative aircrafts in the air space but they don't assure full coverage at low altitudes, in presence of non cooperative targets (NCTs) and aircraft (A/C) with a low value of radar cross section (RCS). This paper describes a new architecture that aims at addressing these limitations, developed in the frame of the ARGUS 3D (AiR GUidance and Surveillance 3D) project funded by the European Union (FP7). This project intends to improve the current ATC systems for civil applications, extending their coverage and making them able to detect, recognize and track NCTs, by means of innovative sensors, such as a new enhanced Primary Surveillance Radar (PSR), passive and bistatic radar network. In this paper a description of the proposed architecture is reported together with the details of the analysis made on simulated and real data and the opinion of the final users summarized.

An innovative scheme is presented for moving target detection and high-resolution focusing that exploits a bank of chirp scaling algorithms (CSA), each one matched to a different along track target velocity component. The new scheme is thought for multichannel (MC) synthetic aperture radar systems, to provide a high-resolution focusing of the moving targets. Adequate target detection capability is ensured by integrating the aforementioned bank of CSA with a post-Doppler space-time adaptive processing clutter cancellation step. The presented scheme is very efficient from a computational point of view and is able to achieve sub-clutter visibility for the moving targets. The effectiveness of the proposed techniques is shown with reference to an emulated MC data set.

Advanced satellite SAR systems, such as Cosmo SkyMed, provide high resolution images with reasonably short revisiting time, allowing for a number of applications in areas such as Homeland Security and Maritime Surveillance. Especially in the case of maritime surveillance, non-ccoperative moving targets imaging represent a challenge for SAR systems as the complex target motions produce evident image defocusing. In the present paper, we show evidence of the effectiveness of the applications of ISAR processing to obtain well focused images of such type of targets. It will be also shown that super-resolution techniques are applicable to targets affected by complex motions after ISAR processing is used.

This paper concerns the definition of efficient configurations of acquisition for improving the features of tomographic imaging using a constellation of SAR sensors. MIMO SAR measurements done in monostatic and bistatic configurations permit to enhance the spectral diversity of the acquisition in the vertical direction, yielding higher performance in terms of ambiguity height and resolution. Different cases are investigated, and in particular a configuration with tracks that would reveal ambiguous for systems operating in monostatic mode only, and another one consisting of sparse groups of acquisitions. The validity of the proposed approach is demonstrated using data sets acquired by a versatile multi channel Ground-Based SAR system, operating in alternating emit-receive mode, over coherent scatterers and distributed volumetric media.

In this paper, an innovative technique is presented for synthetic aperture radar (SAR) ground range resolution improvement using multiple transmit and receive platforms with adequate cross-track displacement. Using orthogonal waveforms, which occupy the same bandwidth in the range frequency domain, for the different platforms of the constellation, a multiple-input-multiple-output SAR system is configured. The proposed technique allows one to achieve a maximum theoretical range resolution improvement factor that is significantly greater than the number of operating SAR sensors by jointly exploiting both mono- and bistatic acquisitions. This can be exploited to obtain a ground range resolution that is much higher than the resolution corresponding to the frequency bandwidth transmitted by the single platform. After illustrating the proposed technique and its system requirements, a simulated data set is used to show the effectiveness of the proposed approach.

In this paper, we present a new technique to exploit the data acquired simultaneously by multiple radar sensors carried by multiple air platforms to increase the cross-range resolution of inverse synthetic aperture radar (ISAR) images of rotating targets. This distributed ISAR technique is devised for two different cases: 1) multiple-input-multiple-output (MIMO) case with each platform carrying an active radar that transmits and receives RF waveforms and 2) multistatic case with a single platform carrying an active radar (transmitting and receiving) and the remaining platforms equipped with passive sensors (namely, receiving only). The processing chain proposed for the distributed ISAR is shown, together with the results obtained against simulated ISAR data for both the MIMO and the multistatic cases. The performance analysis shows that the proposed technique is able to provide an increase of the cross-range resolution up to the number of platforms in the multistatic case and even higher in the MIMO case, if the platforms are properly located. This is of great benefit in applications where the target rotation angle is insufficient to guarantee the desired resolution. A typical case is the imaging of ship targets with rotation induced by the sea swell structure under low sea state conditions. To make the results appealing for practical application, the performance degradation is also analyzed arising from errors in the knowledge of both the target rotation motion and the acquisition geometry. Experimental data collected by a ground-based radar operating together with a rotating platform are processed by following the presented distributed ISAR technique to validate the proposed approach.

A moving target detection and high-resolution focusing scheme is presented for single-channel SAR systems. The moving target focusing is made of a bank of Chirp Scaling Algorithms (CSA), each one matched to a different along-track target velocity component, thus allowing to produce a high-resolution image of the target itself. The effectiveness of the proposed technique is shown with reference to a sample dataset obtained from a SAREX-92 image.

In this paper an innovative technique for ground range resolution improvement in Multiple Input Multiple Output (MIMO) SAR systems is presented. The technique allows to achieve a maximum theoretical range resolution improvement factor greater than the number of operating SAR sensors, by exploiting both the monostatic and the bistatic acquisitions. The effectiveness of the proposed technique is validated over a simulated dataset.

This paper investigates the effect of apodization on the statistical properties of synthetic aperture radar (SAR) images and its impact on the capability of extracting information from homogeneous regions of apodized SAR images. The statistical model for the pixel complex amplitude of the apodized image is derived in terms of both probability density function and statistical moments. Knowledge of the statistical properties is then used to develop appropriate schemes for parameter estimation and supervised classification of homogeneous regions with different radar cross sections in apodized SAR images. The performance analysis shows that the new techniques (properly derived for the apodized case) provide information extraction capabilities only slightly worse than those provided by the conventional techniques applied to the nonapodized case. This allows us to conclude that the use of nonlinear apodization yields sidelobe level reduction and main lobe resolution preservation that can be traded with the small losses above. A full characterization of the estimation and classification performance of these new techniques shows that nonlinear apodization globally introduces a performance degradation comparable to a reduction of the number of looks of a factor of 1.455 for a homogeneous region.

In this paper we analyze the potentialities of two different representations commonly used for polarimetric SAR images, for segmentation and classification purposes. The first representation is based on the covariance matrix of the considered polarimetric channels, while the second is based on the "H-A-alpha" decomposition. For both representations, we devise a segmentation + classification scheme, and we evaluate their performance on a set of C-and L- band polarimetric AirSAR images. On the basis of the achieved results, we devise a hybrid scheme, that uses a segmentation algorithm based on the full covariance matrix, then jointly classifies the pixels of the identified homogeneous regions considering the "H-A-alpha" representation. Results show that the hybrid scheme provides a significant performance improvement with respect to the approaches based on a single representation.

In this paper we present and compare different techniques for the fusion of multitemporal SAR and multiband optical images. We consider both neuro-fuzzy and statistical approaches for the exploitation of the contextual information and the classification, and different schemes for the multisensor fusion. The proposed techniques are applied to a set of two multitemporal SAR and a Landsat multiband image of an urban area. Results show that it is possible to fully exploit the potentialities of the two sensors, by appropriately fusing their information. In particular, the proposed schemes are useful to retain at the same time the change detection capability and the best possible classification accuracy, thus they are of practical interest for civil protection applications.

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.

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).

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.

In this paper we derive two techniques for the classification of multipolarimetric/multifrequency SAR images, based respectively on a statistical and on a neural approach. Both techniques are especially designed to exploit of the spatial structure of the observed scene, thus identifying homogeneous regions that can be jointly classified. Such techniques are useful when looking at medium to large scale features, like the boundaries between urban and non-urban areas. They are applied to a set of multipolarimetric/multifrequency SIRC images of a urban area, to test their effectiveness in the identification of built up areas. A quantitative comparison of the results achievable with the two techniques is carried out, showing a similar behavior, even if the statistical approach tends to achieve better performance.

In this paper we devise a new fusion technique for a sequence of multitemporal single-channel 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 the corresponding joint maximum likelihood (ML) classifier is derived, and lower and upper bounds to classification performance are introduced. An optimized technique for ML joint image segmentation and classification is proposed, showing results close to the upper bound. Finally the effectiveness of fusion of SAR and optical images is investigated quantitatively, showing a consistent performance improvement with respect to using either sensor alone.

This paper deals with the derivation of a fully automatic and reliable ship wake detector (SWD), which can operate in conjunction with a ship detector scheme (SD) for ship traffic control against non-homogeneous sea background in SAR images. A novel segmentation-based technique is introduced for ship wake detection, which overcomes the drawbacks suffered by standard techniques operating in non-homogeneous background. This is composed of a preliminary segmentation stage to extract context information, followed by a modified wake detector. The new SWD scheme is shown to yield a large performance improvement on both simulated and real SAR data.

Ship detection in polarimetric SAR images of non-homogeneous sea areas is considered. We demonstrate the impact of non-homogeneous sea features on the false alarm rate and the need of extracting the image structure. We thus introduce polarimetric segmentation procedures that cooperate with high-resolution polarimetric detection features for the control of the false alarms. Different polarimetric segmentation-plus-detection procedures are tested over real data to devise the appropriate method to exploit the polarimetric information.

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-1 SAR images of forest and agricultural areas, which is also used to validate the theoretical results.

Fast closed-loop-power-control (CLPC) plays a key role in direct-sequence (DS) code division multiple access (CDMA) systems, aiming at achieving acceptable carrier-to-interference (C/I) ratios in all active links. Assuming a terrestrial mobile access, where the round-trip delay may be smaller than the channel time-coherence, fast CLPC can compensate for fading and reduce the error burstiness, as well. This paper provides analytical expressions for the bit error probability given: the CLPC algorithm specified in terms of loop delay and updating rate, the propagation power delay-profile and the terminal speed. A binary PSK DS spread spectrum radio interface, with RAKE processing of multipath or diversity, is considered. Our link-level analysis, being conditioned to a specified average value of C/I, is focused on the inverse update algorithm, which is aimed at keeping the received power at a constant level. Applications of the derived method, aimed at getting insight about CLPC performance in 3rd generation radio interfaces, is also provided and discussed.

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.