Rockfall monitoring based on multichannel synthetic aperture radar

Rockfall influences the safety of infrastructure and transportation lines normal operation, application of SAR technology to monitor the rockfall could predict rockfall disaster. One of the most important factors in rockfall target detection is the signal-to-clutter-plus noise ratio (SCNR) after clutter suppression, which should be maximized before rockfall target detection. Through analyzing remainder rockfall target characteristic after clutter suppression, the method of removing quadratic FM component introduced by platform velocity and along track velocity of rockfall target is proposed. After removing quadratic FM component by Dechirp technology, remainder rockfall target is focused in Doppler region image and the SCNR of remainder rockfall target is maximized. So, it has a preferable result in rockfall target detection. To resolve the contradiction between calculated amount and the accuracy of along track velocity, this paper adopts the technology of gradual approach. The effectiveness of the presented method is demonstrated by both theoretic analysis and simulated data

Investigation of ground moving target indication techniques for a multi-channel synthetic aperture radar

Synthetic Aperture Radar (SAR) is an imaging technique that creates two dimensional images of the scattering objects in the illuminated ground scene. The objects in the illuminated ground scene may be truly stationary, e.g. buildings etc. or in motion relative to these stationary objects, e.g. cars on a highway. In SAR, the radar platform is moving during the imaging period, hence everything that the radar illuminates has motion relative to the radar platform. In order to specifically detect objects on the ground that are moving relative to stationary ground objects (often termed clutter), processing techniques called Ground Moving Target Indication (GMTI) techniques are required. This is especially required for targets that are moving at relative velocities lower than the stationary clutter's relative velocity to the radar platform (endo-clutter detection). This dissertation investigates five multichannel GMTI techniques being Displaced Phase Centre Antenna (DPCA), Along Track Interferometry (ATI), Iterative Adaptive Approach (IAA), Space Time Adaptive Processing (STAP) and Velocity SAR (VSAR) in literature and assesses the performance of two selected GMTI techniques (ATI and DPCA) on simulated and measured radar data to compare them and identify their strengths and weaknesses. The radar data were measured with a C-band FMCW radar in a controlled environment with known parameters and cooperating targets. The performances of the techniques were assessed in terms of moving target detection within clutter and sensitivity to inaccuracies in the physical system setup. The DPCA technique exhibited some attractive characteristics over the ATI technique. These included its robustness against false alarm in noise dominated cells - ATI exhibited large phase residuals in noise dominated cells, due to the random nature of the phase in these cells. Furthermore, DPCA seem to not suffer from false alarms due to volumetric scattering of vegetation to the extent that was observed with ATI. Lastly, DPCA exhibited more robustness against temporal misalignment errors introduced between the measurement channels, compared to ATI. These observations lead to the conclusion that DPCA would be a practically better choice to implement for the purpose of moving target detection, compared to ATI. However, a double threshold approach, which used DPCA as a pre-processing step to ATI, proved to be superior to DPCA alone in terms of moving target indication within clutter and noise. This approach was verified through implementation on the measured radar data in this study

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

Facing channel calibration issues affecting passive radar DPCA and STAP for GMTI

This paper addresses the problem of clutter cancellation for ground moving target indication (GMTI) in multi-channel passive radar on mobile platforms. Specifically, the advantages of a space-time adaptive processing (STAP) approach are presented, compared to a displaced phase centre antenna (DPCA) approach, in the case of an angle-dependent imbalance affecting the receiving channels. The schemes are tested against simulated clutter data. Finally, a space-time GLRT detection scheme is proposed, where steering vector is not specified in the spatial domain, resulting in a non-coherent integration of target echoes across the receiving channels. Such solution offers comparable clutter cancellation capability and is more robust against significant calibration errors compared to a conventional GLRT detector, which suffers from spatial steering vector mismatches

Study of the effects of moving targets in SAR images for their detection.

Synthetic Aperture Radar’s (SAR) are systems designed in the early 50’s that are capable of obtaining images of the ground using electromagnetic signals. Thus, its activity is not interrupted by adverse meteorological conditions or during the night, as it occurs in optical systems. The name of the system comes from the creation of a synthetic aperture, larger than the real one, by moving the platform that carries the radar (typically a plane or a satellite). It provides the same resolution as a static radar equipped with a larger antenna. As it moves, the radar keeps emitting pulses every 1/PRF seconds —the PRF is the pulse repetition frequency—, whose echoes are stored and processed to obtain the image of the ground. To carry out this process, the algorithm needs to make the assumption that the targets in the illuminated scene are not moving. If that is the case, the algorithm is able to extract a focused image from the signal. However, if the targets are moving, they get unfocused and/or shifted from their position in the final image. There are applications in which it is especially useful to have information about moving targets (military, rescue tasks,studyoftheflowsofwater,surveillanceofmaritimeroutes...).Thisfeatureiscalled Ground Moving Target Indicator (GMTI). That is why the study and the development of techniques capable of detecting these targets and placing them correctly in the scene is convenient. In this document, some of the principal GMTI algorithms used in SAR systems are detailed. A simulator has been created to test the features of each implemented algorithm on a general situation with moving targets. Finally Monte Carlo tests have been performed, allowing us to extract conclusions and statistics of each algorithm

Remote Vibration Estimation Using Displaced-Phase-Center Antenna SAR for Strong Clutter Environments

It has been previously demonstrated that it is possible to perform remote vibrometry using synthetic aperture radar (SAR) in conjunction with the discrete fractional Fourier transform (DFrFT). Specifically, the DFrFT estimates the chirp parameters (related to the instantaneous acceleration of a vibrating object) of a slow-time signal associated with the SAR image. However, ground clutter surrounding a vibrating object introduces uncertainties in the estimate of the chirp parameter retrieved via the DFrFT method. To overcome this shortcoming, various techniques based on subspace decomposition of the SAR slow-time signal have been developed. Nonetheless, the effectiveness of these techniques is limited to values of signal-to-clutter ratio ≥5 dB. In this paper, a new vibrometry technique based on displaced-phase-center antenna (DPCA) SAR is proposed. The main characteristic of a DPCA-SAR is that the clutter signal can be canceled, ideally, while retaining information on the instantaneous position and velocity of a target. In this paper, a novel method based on the extended Kalman filter (EKF) is introduced for performing vibrometry using the slow-time signal of a DPCA-SAR. The DPCA-SAR signal model for a vibrating target, the mathematical characterization of the EKF technique, and vibration estimation results for various types of vibration dynamics are presented

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

Novel post-Doppler STAP with a priori knowledge information for traffic monitoring applications

The road traffic has worsened over time in most cities, and the methods employed for monitoring and counting the vehicles on the roads (e.g., cameras, induction loops, or even people manually counting) are expensive and limited in spatial coverage. Synthetic aperture radars (SAR) provide an effective solution for this problem due to the wide-area coverage and the independence from daylight and weather conditions. Special attention is given in case of large scale events or catastrophes, when mobile internet is unavailable and phone communication is impossible. In this particular scenario, the traffic monitoring with real-time information ensures the safety of the road users and can even save lives. For that reason, this paper presents a novel a priori knowledge-based algorithm for traffic monitoring, where the powerful post-Doppler space-time adaptive processing (PD STAP) is combined with a road network obtained from the freely available OpenStreetMap (OSM) database. The incorporation of a known road network into the processing chain presents great potential for real-time processing, since only the acquired data related to the roads need to be processed. As a result, decreased processing hardware complexity and low costs compared to state-of-the-art systems can be achieved. In addition, it is a promising solution for detecting effectively the road vehicles and estimating their positions, velocities and moving directions with high accuracy. The PD STAP is well-known for its very good clutter suppression, its sensitivity also to low vehicle velocities, and its accurate target position estimation capabilities. The road information is applied after the PD STAP, where the OSM database fused with a digital elevation model (DEM) is applied in order to recognize and to reject false detections, and moreover, to reposition the vehicles detected in the vicinity of the roads. In other words, the distance between the estimated position of the target and its closest road point is measured and compared to a relocation threshold for deciding whether the target corresponds to a true road vehicle or to a false detection. If the first condition is fulfilled, the target is repositioned to its closest road point; otherwise it is discarded. The relocation threshold is computed adaptively for each detection by using an appropriate performance model. The proposed algorithm was tested using real 4-channel aperture switching data acquired by DLR’s airborne system F-SAR. In the radar data takes examined so far, the PD STAP detected vehicles as slow as 7 km/h, with an overall position estimation accuracy better than 10 m. Besides, the estimated velocities of the vehicles were in very good agreement with the differential GPS reference data. To sum up, the experimental results revealed a powerful algorithm that detects even slow vehicles and discards most of the false detections, being suitable for many traffic monitoring applications. We will not limit our further investigations to the data takes whose results are shown in this paper. We have a large pool of multi-channel F-SAR data takes containing real highway traffic scenarios with dozens or even hundreds of vehicles