Multistatic Radar Data Research Articles

Evaluation of Materials and Structures with a Multistatic Ultra-Wideband Impulse Radar: A Concept Validation

This document describes the results of the study contributing to the methods and tools applicable in plastic waste sorting systems that exploit the multistatic ultra-wideband impulse radar enforced with a deep learning signal processing back-end. The novelty of the research is the use of synthetic data for the development of a trained neural network before real data are available, and the use of a multistatic radar for the improvement of the training data set. The study results are described in multiple publications; the current paper shows the applicability of the described approach. The main results are as follows: a monostatic impulse radar can be used for the determination of material properties, such as thickness, dielectric permittivity, and losses, with limited accuracy; multistatic radar configuration increases the accuracy of the material property estimation; an open source finite difference time domain simulator can be used to simulate electromagnetic wave propagation in dielectric structures in order to generate synthetic data for development of optimized artificial neuron network structures used for the estimation of dielectric material properties, and the developed network can successfully be used for multistatic radar data processing.

PointNet+LSTM for Target List-Based Gesture Recognition With Incoherent Radar Networks

Radar-based gesture recognition can provide autonomous electronic systems with a reliable way to infer a human's intention, e.g., in traffic environments involving vulnerable road users. Particularly in complex scenarios, algorithms operating on radar target lists derived from constant false-alarm rate outputs present an attractive solution, as they not only enable the filtering of relevant targets, but can also make full use of the diverse, high-resolution target parameters provided by modern radar sensors. Therefore, this article proposes PointNet+long short-term memory (LSTM) for the enhanced target list-based recognition of challenging traffic gestures, combining per-frame feature extraction with PointNet and learning from sequences with a LSTM. The approach is generalized to facilitate the use of multistatic radar data from sensor networks to exploit slightly different viewing angles, which is particularly helpful for motions with low radial velocity. The proposed method is validated on a comprehensive dataset comprising eight traffic gestures and data recorded from 35 participants. Measurements are conducted both indoors and outdoors with an incoherent radar sensor network comprising three chirp sequence–multiple-input multiple-output sensors. On this challenging dataset, our approach clearly outperforms a reference convolutional neural network, reaching up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$92.2 \%$</tex-math></inline-formula> cross-validation accuracy.

DopNet: A Deep Convolutional Neural Network to Recognize Armed and Unarmed Human Targets

The work presented in this paper aims to distinguish between armed or unarmed personnel using multi-static radar data and advanced Doppler processing. We propose two modified deep convolutional neural networks (DCNNs) termed single channel DopNet (SC-DopNet) and multiple channel DopNet (MC-DopNet) for mono-static and multi-static micro-Doppler signature ( $\mu $ -DS) classification. Differentiating armed and unarmed walking personnel is challenging due to the effect of the aspect angle and the channel diversity in real-world scenarios. In addition, the DCNN easily overfits the relatively small-scale $\mu $ -DS dataset. To address these problems, the work carried out in this paper makes three key contributions. First, two effective schemes including a data augmentation operation and a regularization term are proposed to train the SC-DopNet from scratch. Next, a factor analysis of the SC-DopNet is conducted based on various operating parameters in both the processing and radar operations. Third, to solve the problem of aspect angle diversity for the $\mu $ -DS classification, we design the MC-DopNet for multi-static $\mu $ -DS which is embedded with two new fusion schemes termed as greedy importance reweighting (GIR) and $\ell _{21}$ -Norm. These two schemes are based on two different strategies and have been evaluated experimentally. The GIR uses a win by sacrificing worst case approach, whereas $\ell _{21}$ -Norm adopts a win by sacrificing best case approach. The SC-DopNet outperforms the non-deep methods by 12.5% in average, and the proposed MC-DopNet with two fusion methods outperforms the conventional binary voting by 1.2% in average. Note that we also argue and discuss how to utilize the statistics of SC-DopNet results to infer the selection of fusion strategies for the MC-DopNet under different experimental scenarios.

Experimental analysis of multistatic wind turbine radar clutter statistics

This Letter presents preliminary results of the analysis of amplitude statistics of wind turbine clutter as extracted from multistatic radar data. It is shown that the T-location-scale distribution provides good fitting of the experimental data, and that there are combinations of bistatic angle and polarisations where the bistatic clutter has more favourable statistics for target detection than the simultaneous monostatic clutter.

Passive multistatic radar experiment using WiMAX signals of opportunity. Part 2: Multistatic velocity backprojection

Passive radar can reduce radio frequency (RF) spectrum crowding and facilitate RF system interoperability by eliminating the necessity to transmit. Part 1 of this study discussed a passive multistatic radar experiment using Worldwide Interoperability for Microwave Access (WiMAX) signals of opportunity and the accompanying signal processing techniques that were employed. Part 2 discusses the multistatic velocity backprojection technique for visualising multistatic radar data and applies this technique to simulated data and passive multistatic data from the experiment discussed in Part 1.

Multistatic polarimetric radar data modeling and imaging of moving targets

This paper advances the theory for multistatic imaging of non-interacting moving targets through the development of a vector radar data model for multiple transmitters and receivers and accompanying imaging operations for visualizing multistatic radar data in a common reference frame. The electromagnetic data model incorporates polarization and antenna effects, and the transmit waveform and scattering behavior of the target are left arbitrary for greater flexibility to model multiple scenarios. Two imaging operations are derived that combine the data collected at each receiver, first assuming that the contributions from all transmitters in the scene are separable and then assuming that the contributions from all transmitting antennas cannot be separated. The data model and imaging operations are used to simulate multiple antenna geometries and transmission schemes. Images produced from simulated data exhibit angle- and polarization-dependent scattering behavior consistent with the antenna geometry, transmit waveforms, and target specific to each scenario.

A Compressive Sensing Approach to Multistatic Radar Change Imaging

This paper describes a new approach for forming change images from multistatic radar data based on compressive sensing (CS). Broadly speaking, change images are naturally sparse in the raw image domain, which suggests a CS reconstruction method. Recent results show that the sparsity of the estimand dictates the number of samples required for faithful reconstruction, meaning a change image can be formed with far fewer measurements than used for conventional radar imaging. Our application has a small number of antennas arranged around the perimeter of a surveillance region, which provide large angular diversity but very poor angular sampling. Furthermore, due to application constraints, the scene is interrogated with limited frequency diversity. We aim to construct a high-resolution change image from the measurements, which are sub-Nyquist both spatially and in frequency. This paper first develops a radar imaging model in the context of CS, and then shows with collected data that a sparseness model improves image utility over conventional methods in our setting.

Numerical and analytical studies of the linear sampling method in electromagnetic inversescattering problems

We present in this study some three-dimensional numerical results that validate the use ofthe linear sampling method as an inverse solver in electromagnetic scattering problems.We recall that this method allows the reconstruction of the shape of an obstaclefrom the knowledge of multi-static radar data at a fixed frequency. It does notrequire any a priori knowledge of the physical properties of the scatterer nor anynonlinear optimization scheme. This study also contains some analytical results in thesimplified case of a spherical scatterer that somehow make the link between knownabstract theoretical results and the numerical scheme. Special attention has beengiven to pointing out the influence of the frequency on the inversion accuracy.