Pierfrancesco Lombardo

MRC performance for binary signals in Nakagami fading with general branch correlation

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.

A Multistage Processing Algorithm for Disturbance Removal and Target Detection in Passive Bistatic Radar

The paper examines the problem of cancellation of direct signal, multipath and clutter echoes in passive bistatic radar (PBR). This problem is exacerbated as the transmitted waveform is not under control of the radar designer and the sidelobes of the ambiguity function can mask targets including those displaced in either (or both) range and Doppler from the disturbance. A novel multistage approach is developed for disturbance cancellation and target detection based on projections of the received signal in a subspace orthogonal to both the disturbance and previously detected targets. The resulting algorithm is shown to be effective against typical simulated scenarios with a limited number of stages, and a version with computational savings is also introduced. Finally its effectiveness is demonstrated with the application to real data acquired with an experimental VHF PBR system.

Cancellation of clutter and multipath in passive radar using a sequential approach

The paper deals with the cancellation of direct signal, multipath and clutter echoes in passive radar, where the transmitted waveform is not under control of the radar designer and the sidelobes of the ambiguity function mask even targets largely displaced in range and Doppler from the disturbance. A sequential approach is presented for disturbance cancellation and target detection based on projections of the received signals in a subspace orthogonal to both disturbance and previously detected targets. Modeling the clutter spread on few Doppler bins allows complete clutter cancellation. The approach is shown to be effective against typical scenarios with a limited number of iterations.

Comparison of Clutter and Multipath Cancellation Techniques for Passive Radar

In this paper we address the problem of clutter and multipath cancellation in passive radar. Different adaptive techniques are considered based on both well known approaches and innovative strategies proposed by the authors. The performance comparison allows to understand the particular behaviour of each technique in a practical scenario. The proposed innovative strategies are shown to be very appealing solutions for disturbance cancellation in passive radar.

Adaptive polarimetric target detection with coherent radar. II. Detection against non-Gaussian background

The derivation of a completely adaptive polarimetric coherent scheme to detect a radar target against a Gaussian background is presented. A previously proposed Generalized Likelihood Ratio Test (GLRT) polarimetric detector is extended to the case of a general number of channels; this exploits the polarimetric characteristics of the received radar echoes to improve the detection performance. Together with the fully adaptive scheme, a model-based detector is derived that has a lower estimation loss. A complete theoretical expression is derived for the detection performance of both proposed polarimetric detectors. They are shown to have Constant False Alarm Rate (CFAR) when operating against Gaussian clutter, but to be sensitive to deviations from the Gaussian statistic. The application to recorded radar data demonstrates the performance improvement achievable in practice.

WiFi-Based Passive Bistatic Radar: Data Processing Schemes and Experimental Results

The practical feasibility of a WiFi transmissions based passive bistatic radar (PBR) is analyzed here. The required data processing steps are described including the adopted techniques for 1) the control of the signal autocorrelation function (ACF) usually yielding a high sidelobe level, and 2) the removal of the undesired signal contributions which strongly limit the useful dynamic range. The performance of the proposed techniques is firstly evaluated against simulated data generated according to the IEEE 802.11 Standards. Moreover the results are presented against a real data set collected by an experimental setup when using the conventional dual (reference and surveillance) channels PBR receiving scheme. This allows us to demonstrate the potentialities of a WiFi-based PBR for local area surveillance applications, where vehicles and people can be detected and tracked. Based on the digital nature of the exploited signals of opportunity, the attractive possibility is also investigated of avoiding the use of a dedicated receiving channel for the reference signal, by synthesizing it from the surveillance channel. This approach is shown to yield comparable performance with respect to the conventional PBR approach while yielding a remarkable saving in terms of system complexity.

UWB ranging accuracy in high- and low-data-rate applications

The Cramer-Rao lower bound (CRLB) was determined for different ultra-wideband (UWB) signal formats and, in particular, for the two UWB high-data-rate (HDR) signal formats proposed within the IEEE 802.15.3a Task Group, that is, the impulsive direct-sequence UWB (DS-UWB) and the nonimpulsive multiband orthogonal frequency-division multiplexing (MB-OFDM), and an impulsive time hopping (TH) UWB format close to the format for UWB low data rate (LDR) of the forthcoming IEEE 802.15.4a standard. The analysis was carried out for both ideal and multipath channels under power constraints as set by emission masks. Results obtained for HDR formats showed that DS-UWB has better ranging accuracy than does MB-OFDM, thanks to its potentially larger bandwidth and higher frequency of operation. In addition, the degree of multipath strongly affected ranging accuracy, although differently for DS-UWB versus MB-OFDM. When incorporating a correlation receiver structure as well as an Early Late gate synchronizer in the model, ranging performance proved to be related to features of the synchronization sequence. For specific synchronization sequences, in particular, the best ranging accuracy was obtained with MB-OFDM. In the case of LDR, the study analyzed the effect of pulse shape on CRLB. Results showed that a suboptimal choice of the pulse shape reduces the ranging accuracy achievable by TH-UWB signals.

Experimental results for OFDM WiFi-based passive bistatic radar

In this paper the practical feasibility of a WiFi transmissions based passive bistatic radar (PBR) is analyzed. The required data processing steps are described there including the adopted techniques for: (i) the control of the signal Ambiguity Function usually yielding a high sidelobe level and (ii) the removal of the undesired signal contributions which strongly limit the useful dynamic range. The performance of the conceived system is evaluated with reference to typical signals broadcasted by a IEEE 802.11 access point exploiting an OFDM modulation. The achievable results are presented against a real data set collected by an experimental setup. This allowed us to preliminarily demonstrate the potentialities of a WiFi-based PBR for local area surveillance applications.

Multistatic and MIMO Distributed ISAR for Enhanced Cross-Range Resolution of Rotating Targets

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.

Multitemporal/multiband SAR classification of urban areas using spatial analysis: statistical versus neural kernel-based approach

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.