57 research outputs found
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
Autofocusing SAR Data Using The Blind Deconvolution Approach: Limits And Experimental Results.
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
Optimizing performances of the ENVISAT ASAR ScanSAR modes
This paper focuses on the optimization of the image quality in the two ScanSAR modes of the forecoming ASAR sensor. The best trade-off between Noise Equivalent σ0 (NEσ0) and Equivalent Number of Looks (ENL) is achieved by optimizing a set of system and processing parameters. The optimization exploits a suitable end-to-end system model. This includes a closed form expression to compute ENL in a general case. Recommendations for processing and mosaicking are provide
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