Classification Schemes for the Radar Reference Window: Design and Comparisons

In this paper, we address the problem of classifying data within the radar reference window in terms of statistical properties. Specifically, we partition these data into statistically homogeneous subsets by identifying possible clutter power variations with respect to the cells under test (accounting for possible range-spread targets) and/or clutter edges. To this end, we consider different situations of practical interest and formulate the classification problem as multiple hypothesis tests comprising several models for the operating scenario. Then, we solve the hypothesis testing problems by resorting to suitable approximations of the model order selection rules due to the intractable mathematics associated with the maximum likelihood estimation of some parameters. Remarkably, the classification results provided by the proposed architectures represent an advanced clutter map since, besides the estimation of the clutter parameters, they contain a clustering of the range bins in terms of homogeneous subsets. In fact, such information can drive the conventional detectors towards more reliable estimates of the clutter covariance matrix according to the position of the cells under test. The performance analysis confirms that the conceived architectures represent a viable means to recognize the scenario wherein the radar is operating at least for the considered simulation parameters.Comment: Accepted by IEEE Transactions on Aerospace and Electronic System

Mismatched Processing for Radar Interference Cancellation

Matched processing is a fundamental filtering operation within radar signal processing to estimate scattering in the radar scene based on the transmit signal. Although matched processing maximizes the signal-to-noise ratio (SNR), the filtering operation is ineffective when interference is captured in the receive measurement. Adaptive interference mitigation combined with matched processing has proven to mitigate interference and estimate the radar scene. A known caveat of matched processing is the resulting sidelobes that may mask other scatterers. The sidelobes can be efficiently addressed by windowing but this approach also comes with limited suppression capabilities, loss in resolution, and loss in SNR. The recent emergence of mismatch processing has shown to optimally reduce sidelobes while maintaining nominal resolution and signal estimation performance. Throughout this work, re-iterative minimum-mean square error (RMMSE) adaptive and least-squares (LS) optimal mismatch processing are proposed for enhanced signal estimation in unison with adaptive interference mitigation for various radar applications including random pulse repetition interval (PRI) staggering pulse-Doppler radar, airborne ground moving target indication, and radar & communication spectrum sharing. Mismatch processing and adaptive interference cancellation each can be computationally complex for practical implementation. Sub-optimal RMMSE and LS approaches are also introduced to address computational limitations. The efficacy of these algorithms is presented using various high-fidelity Monte Carlo simulations and open-air experimental datasets

Passive radar DPCA schemes with adaptive channel calibration

This paper addresses the problem of direct signal interference (DSI) and clutter cancellation for passive radar systems on moving platforms employing displaced phase centre antenna (DPCA) approach. Attention is focused on the development of signal processing strategies able to compensate for the limitations deriving from amplitude and phase imbalances that affect the two channels employed on receive. First, we show that using the signal received from the illuminator of opportunity as a source for channels calibration might be ineffective when DSI and clutter echoes have different directions of arrival, due to the effect of angle-dependent channel imbalance. Then, a two-stage strategy is proposed, consisting of a preliminary DSI removal stage at each receive channel, followed by a clutter-based calibration approach that basically enables an effective DPCA clutter suppression. Different strategies for channel calibration are proposed, aimed at compensating for potential angle and range dependent channel errors, based on the maximization of the cancellation performance. Effectiveness of this scheme is shown against experimental data from a DVB-T based moving passive radar, in the presence of both real and synthetic moving targets

Space-time adaptive processing techniques for multichannel mobile passive radar

Passive radar technology has reached a level of maturity for stationary sensor operations, widely proving the ability to detect, localize and track targets, by exploiting different kinds of illuminators of opportunity. In recent years, a renewed interest from both the scientific community and the industry has opened new perspectives and research areas. One of the most interesting and challenging ones is the use of passive radar sensors onboard moving platforms. This may offer a number of strategic advantages and extend the functionalities of passive radar to applications like synthetic aperture radar (SAR) imaging and ground moving target indication (GMTI). However, these benefits are paid in terms of motion-induced Doppler distortions of the received signals, which can adversely affect the system performance. In the case of surveillance applications, the detection of slowly moving targets is hindered by the Doppler-spread clutter returns, due to platform motion, and requires the use of space-time processing techniques, applied on signals collected by multiple receiving channels. Although in recent technical literature the feasibility of this concept has been preliminarily demonstrated, mobile passive radar is still far from being a mature technology and several issues still need to be addressed, mostly connected to the peculiar characteristics of the passive bistatic scenario. Specifically, significant limitations may come from the continuous and time-varying nature of the typical waveforms of opportunity, not suitable for conventional space-time processing techniques. Moreover, the low directivity of the practical receiving antennas, paired with a bistatic omni-directional illumination, further increases the clutter Doppler bandwidth and results in the simultaneous reception of non-negligible clutter contributions from a very wide angular sector. Such contributions are likely to undergo an angle-dependent imbalance across the receiving channels, exacerbated by the use of low-cost hardware. This thesis takes research on mobile passive radar for surveillance applications one step further, finding solutions to tackle the main limitations deriving from the passive bistatic framework, while preserving the paradigm of a simple system architecture. Attention is devoted to the development of signal processing algorithms and operational strategies for multichannel mobile passive radar, focusing on space-time processing techniques aimed at clutter cancellation and slowly moving target detection and localization. First, a processing scheme based on the displaced phase centre antenna (DPCA) approach is considered, for dual-channel systems. The scheme offers a simple and effective solution for passive radar GMTI, but its cancellation performance can be severely compromised by the presence of angle-dependent imbalances affecting the receiving channels. Therefore, it is paired with adaptive clutter-based calibration techniques, specifically devised for mobile passive radar. By exploiting the fine Doppler resolution offered by the typical long integration times and the one-to-one relationship between angle of arrival and Doppler frequency of the stationary scatterers, the devised techniques compensate for the angle-dependent imbalances and prove largely necessary to guarantee an effective clutter cancellation. Then, the attention is focused on space-time adaptive processing (STAP) techniques for multichannel mobile passive radar. In this case, the clutter cancellation capability relies on the adaptivity of the space-time filter, by resorting to an adjacent-bin post-Doppler (ABPD) approach. This allows to significantly reduce the size of the adaptive problem and intrinsically compensate for potential angle-dependent channel errors, by operating on a clutter subspace accounting for a limited angular sector. Therefore, ad hoc strategies are devised to counteract the effects of channel imbalance on the moving target detection and localization performance. By exploiting the clutter echoes to correct the spatial steering vector mismatch, the proposed STAP scheme is shown to enable an accurate estimation of target direction of arrival (DOA), which represents a critical task in system featuring few wide beam antennas. Finally, a dual cancelled channel STAP scheme is proposed, aimed at further reducing the system computational complexity and the number of required training data, compared to a conventional full-array solution. The proposed scheme simplifies the DOA estimation process and proves to be robust against the adaptivity losses commonly arising in a real bistatic clutter scenario, allowing effective operation even in the case of a limited sample support. The effectiveness of the techniques proposed in this work is validated by means of extensive simulated analyses and applications to real data, collected by an experimental multichannel passive radar installed on a moving platform and based on DVB-T transmission

A comparison of processing approaches for distributed radar sensing

Radar networks received increasing attention in recent years as they can outperform single monostatic or bistatic systems. Further attention is being dedicated to these systems as an application of the MIMO concept, well know in communications for increasing the capacity of the channel and improving the overall quality of the connection. However, it is here shown that radar network can take advantage not only from the angular diversity in observing the target, but also from a variety of ways of processing the received signals. The number of devices comprising the network has also been taken into the analysis. Detection and false alarm are evaluated in noise only and clutter from a theoretical and simulated point of view. Particular attention is dedicated to the statistics behind the processing. Experiments have been performed to evaluate practical applications of the proposed processing approaches and to validate assumptions made in the theoretical analysis. In particular, the radar network used for gathering real data is made up of two transmitters and three receivers. More than two transmitters are well known to generate mutual interference and therefore require additional e�fforts to mitigate the system self-interference. However, this allowed studying aspects of multistatic clutter, such as correlation, which represent a first and novel insight in this topic. Moreover, two approaches for localizing targets have been developed. Whilst the first is a graphic approach, the second is hybrid numerical (partially decentralized, partially centralized) which is clearly shown to improve dramatically the single radar accuracy. Finally the e�ects of exchanging angular with frequency diversity are shown as well in some particular cases. This led to develop the Frequency MIMO and the Frequency Diverse Array, according to the separation of two consecutive frequencies. The latter is a brand new topic in technical literature, which is attracting the interest of the technical community because of its potential to generate range-dependant patterns. Both the latter systems can be used in radar-designing to improve the agility and the effciency of the radar

Pixel-level Image Fusion Algorithms for Multi-camera Imaging System

This thesis work is motivated by the potential and promise of image fusion technologies in the multi sensor image fusion system and applications. With specific focus on pixel level image fusion, the process after the image registration is processed, we develop graphic user interface for multi-sensor image fusion software using Microsoft visual studio and Microsoft Foundation Class library. In this thesis, we proposed and presented some image fusion algorithms with low computational cost, based upon spatial mixture analysis. The segment weighted average image fusion combines several low spatial resolution data source from different sensors to create high resolution and large size of fused image. This research includes developing a segment-based step, based upon stepwise divide and combine process. In the second stage of the process, the linear interpolation optimization is used to sharpen the image resolution. Implementation of these image fusion algorithms are completed based on the graphic user interface we developed. Multiple sensor image fusion is easily accommodated by the algorithm, and the results are demonstrated at multiple scales. By using quantitative estimation such as mutual information, we obtain the experiment quantifiable results. We also use the image morphing technique to generate fused image sequence, to simulate the results of image fusion. While deploying our pixel level image fusion algorithm approaches, we observe several challenges from the popular image fusion methods. While high computational cost and complex processing steps of image fusion algorithms provide accurate fused results, they also makes it hard to become deployed in system and applications that require real-time feedback, high flexibility and low computation abilit

Estimation of biophysical parameters in boreal forests from ERS and JERS SAR interferometry

The thesis describes investigations concerning the evaluation of ERS and JERS SAR images and repeat-pass interferometric SAR images for the retrieval of biophysical parameters in boreal forests. The availability of extensive data sets of images over several test sites located in Sweden, Finland and Siberia has allowed analysis of temporal dynamics of ERS and JERS backscatter and coherence, and of ERS interferometric phase. Modelling of backscatter, coherence and InSAR phase has been performed by means of the Water Cloud Model (WCM) and the Interferometric Water Cloud Model (IWCM); sensitivity analysis and implications for the retrieval of forest biophysical parameters have been thoroughly discussed. Model inversion has been carried out for stem volume retrieval using ERS coherence, ERS backscatter and JERS backscatter, whereas for tree height estimation the ERS interferometric phase has been used. Multi-temporal combination of ERS coherence images, and to a lesser extent of JERS backscatter images, can provide stem volume estimates comparable to stand-wise ground-based measurements. Since the information content of the interferometric phase is strongly degraded by phase noise and uncorrected atmospheric artefacts, the retrieved tree height shows large errors