A Nonparametric Estimator for Coherent Change Detection: The Permutational Change Detection

Nowadays, synthetic aperture radar (SAR) is widely used in heterogeneous fields with aims strictly dependent on the objectives of the application. One of the most common is the exploitation of the interferometric-SAR (InSAR) to measure millimeter movements on the Earth's surface, aiming to monitor failures (e.g., landslides) or to measure the health state of infrastructures (e.g., mining assets, bridges, and buildings). In this article, developing algorithms to detect temporal and spatial changes in the radar targets becomes very important. This article focuses on the temporal change detection framework, proposing a nonparametric coherent change detection (CCD) algorithm called permutational change detection (PCD), a purely statistical algorithm whose core is the permutational test. The PCD estimates the temporal change points (CPs) of a radar target recognizing blocks structure in the coherence matrix, namely, new radar objects. The algorithm has been fine-tuned for small SAR datasets, with the specific aim of prioritizing the analysis of the latest changes. A rigorous mathematical derivation of the algorithm is carried out, explaining how some limits have been addressed. Then, the performance analysis on the simulated data is deeply accomplished, carried out for the stand-alone PCD and the PCD compared with a parametric CCD algorithm based on the generalized likelihood ratio test (GLRT), and with the Omnibus and REACTIV detectors. The comparison with these other algorithms and the stand-alone performance analysis point out the robustness of the PCD in dealing with very noisy environments, even in the case of a single block. Finally, the PCD is validated by processing two Sentinel I data stacks, ascending and descending geometries, of the 2016 Central Italy earthquake

Cooperative Coherent Multistatic Imaging and Phase Synchronization in Networked Sensing

Coherent multistatic radio imaging represents a pivotal opportunity for forthcoming wireless networks, which involves distributed nodes cooperating to achieve accurate sensing resolution and robustness. This paper delves into cooperative coherent imaging for vehicular radar networks. Herein, multiple radar-equipped vehicles cooperate to improve collective sensing capabilities and address the fundamental issue of distinguishing weak targets in close proximity to strong ones, a critical challenge for vulnerable road users protection. We prove the significant benefits of cooperative coherent imaging in the considered automotive scenario in terms of both probability of correct detection, evaluated considering several system parameters, as well as resolution capabilities, showcased by a dedicated experimental campaign wherein the collaboration between two vehicles enables the detection of the legs of a pedestrian close to a parked car. Moreover, as \textit{coherent} processing of several sensors' data requires very tight accuracy on clock synchronization and sensor's positioning -- referred to as \textit{phase synchronization} -- (such that to predict sensor-target distances up to a fraction of the carrier wavelength), we present a general three-step cooperative multistatic phase synchronization procedure, detailing the required information exchange among vehicles in the specific automotive radar context and assessing its feasibility and performance by hybrid Cram\'er-Rao bound.Comment: 13 page

Motion Estimation and Compensation in Automotive MIMO SAR

With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle's ego-motion, requiring a very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this regard, this paper deals with the analysis, estimation and compensation of trajectory estimation errors in automotive SAR systems, proposing a complete residual motion estimation and compensation workflow. We start by defining the geometry of the acquisition and the basic processing steps of Multiple-Input Multiple-Output (MIMO) SAR systems. Then, we analytically derive the effects of typical motion errors in automotive SAR imaging. Based on the derived models, the procedure is detailed, outlining the guidelines for its practical implementation. We show the effectiveness of the proposed technique by means of experimental data gathered by a 77 GHz radar mounted in a forward looking configuration.Comment: 14 page

Distributed Scatterer Interferometry With the Refinement of Spatiotemporal Coherence

The state-of-the-art techniques have demonstrated that coherence error degrades the performance of synthetic aperture radar (SAR) interferometry (InSAR) for distributed scatterers (DSs). This article aims at fully evaluating the influence of coherence error on DS InSAR time-series analysis. In particular, we present a methodology to increase the estimation accuracy of DS interferometry, with emphasis on spatiotemporal coherence refinement. The motive behind this is that bias removal and variance mitigation of sample coherence matrix impose optimum weighting for estimating phase series and geophysical parameters of interest, whereas maximization of temporal coherence in a reference network can avoid spatial error propagation during the least-squares adjustment. Rather than developing independent processing chains, we integrate this method into SqueeSAR technique and simultaneously take the advantage of StaMPS into consideration. Using simulation and real data over southwestern China, comprehensive comparisons before and after spatiotemporal coherence refinement are performed over various coherence scenarios. The results tested from different phase and displacement rate estimators validate the effectiveness of the presented method

Wide-Angle Azimuth Antenna Pattern Estimation in SAR Images

We propose a novel technique to estimate the Azimuth Antenna Pattern (AAP) from SAR images. The technique first perform azimuth focusing at enhanced resolution, then selects those scatterers that are less affected by ambiguous returns and finally derive the AAP by spectral analysis. Results achieved by processing ENVISAT-ASAR data are presented

Channel phase estimate in time variant SIMO systems

This paper introduces a novel ML based approach to channel identification for time variant SIMO (single input multiple output) systems fed by a stochastic process. We focus on the particular case where the unknowns are represented by the channels phases, that find applications in radar interferometry. Starting from the rigorous formulation of the ML estimator, we derive an approximation that makes use of mixers and FIR filters only. The computational efficiency and the robustness versus model errors of the resulting estimator make it suitable for its implementation is an adaptive framework. An application in topography reconstruction from real SAR (synthetic aperture radar) data is presente

A PS-based approach for the calilbration of spaceborne polarimetric SAR systems

The paper debates an external calibration approach based on the stable natural targets, namely Permanent Scatterers (PS), that can be spotted in the illuminated frame. The method, hereby called PolPSCal, allows for relative calibration of the full 4 by 3 polarimetric distortion matrices (PDMs) affecting the stack images. The algorithm is neither constrained to a particular PDM model (thus its implementation is practically feasible for any SAR sensor) nor to any external information. These latter are eventually demanded afterwards in order to normalize the returned PDM stack to an absolute reference. The PolPSCal mathematical framework is reported, and a performance analysis with concern to the PS detection and the PDM estimates accuracy is carried out on both synthetic data and a 29 images RADARSAT-2 dataset