Novel Techniques for Processing Data with an FMCW radar

This dissertation examines and analyzes novel techniques that are useful in the collection and processing of data from a Frequency Modulated Continuous Wave Radar. The major topics discussed in this work are: reduction of amplitude modulation, signature collection without an anechoic chamber, transforming a signature into a matched filter, accounting for electromagnetic interference, accounting for digital noise, and the application of a Support Vector Machine to achieve classification. In addition, this work also provides a broad overview of a framework specifically developed to improve detection and classification without requiring expensive hardware modification. The four main categories analyzed in this work are distortion, spectral signature, optimal detection, and classification. Some notable contributions in this work include the assessment of a novel technique’s effectiveness to improve model accuracy by accounting for amplitude modulation in an FMCW radar, as well as discussion of improved techniques to perform signature collection with an FMCW radar in the absence of an anechoic chamber. The signature collection technique is a novel approach that utilizes physics and wavelets in an effort to improve Signal to Noise Ratio (SNR). This work also considers a novel technique to convert an FMCW target signature into coefficients for a matched filter, thus allowing for the full mathematical application of the optimal matched filter. In addition, this work provides an analysis of the improved performance of an FMCW radar through the development and use of a novel technique to account for both electromagnetic interference and digital noise. Finally the initial discovery, development, and refinement of an innovative application using SVM to classify the matched filter results of FMCW radar targets is given, thus resulting in previously uncollected and undocumented viable baseline data

Novel Techniques for Processing Data with an FMCW radar

This dissertation examines and analyzes novel techniques that are useful in the collection and processing of data from a Frequency Modulated Continuous Wave Radar. The major topics discussed in this work are: reduction of amplitude modulation, signature collection without an anechoic chamber, transforming a signature into a matched filter, accounting for electromagnetic interference, accounting for digital noise, and the application of a Support Vector Machine to achieve classification. In addition, this work also provides a broad overview of a framework specifically developed to improve detection and classification without requiring expensive hardware modification. The four main categories analyzed in this work are distortion, spectral signature, optimal detection, and classification. Some notable contributions in this work include the assessment of a novel technique’s effectiveness to improve model accuracy by accounting for amplitude modulation in an FMCW radar, as well as discussion of improved techniques to perform signature collection with an FMCW radar in the absence of an anechoic chamber. The signature collection technique is a novel approach that utilizes physics and wavelets in an effort to improve Signal to Noise Ratio (SNR). This work also considers a novel technique to convert an FMCW target signature into coefficients for a matched filter, thus allowing for the full mathematical application of the optimal matched filter. In addition, this work provides an analysis of the improved performance of an FMCW radar through the development and use of a novel technique to account for both electromagnetic interference and digital noise. Finally the initial discovery, development, and refinement of an innovative application using SVM to classify the matched filter results of FMCW radar targets is given, thus resulting in previously uncollected and undocumented viable baseline data

Fourier independent component analysis of radar micro-Doppler features

The capability of discriminating radar targets exhibiting multiple moving parts has become of great interest for both aerospace and ground-based target recognition and analysis. In particular, helicopters and other targets with rotors, as for instance miniature Unmanned Aerial Vehicles, exhibit peculiar characteristics in the radar return that can be used for their recognition. In this paper a novel algorithm to address the problem of micro-Doppler signature unmixing is proposed, exploiting the signal separation capabilities of the Independent Component Analysis (ICA). The core of the algorithm is represented precisely by the use of the ICA procedure, that has been already proved to be a very effective technique for separating hidden information in mixtures of observations. ICA has been successfully employed in several applications such as wireless communications, radar beamforming, trace-gases unmixing and medical imaging processing. The helicopter's rotor blade signature unmixing from a multi-static radar system is considered as case study and results obtained through the application of ICA to simulated multi-component micro-Doppler signatures show the capability of the proposed approach to successfully accomplish the unmixing operation

Speed recognition based on ground vehicle in passive forward scattering radar

The merging of noise reduction and reshaped of the signal in time domain is headed to newfangled clustering methods. After a deep investigation on pre-processing the detection of ground vehicle using passive forward scattering radar (PFSR), principal component analysis (PCA) could be used as spectral signature for target’s speed recognition. The clustering-based PCA able to distinguish the target’s rapidity from the passive forward scattering radar receiver. A small five door hatchback vehicle is used for detection as ground vehicle with several speed and various distance from the passive forward scattering radar receiver. The distance give impact to the clustering-based PCA which is closer vehicle to the passive forward scattering radar offers finer variance of training data in speed recognition

An Adversarial Super-Resolution Remedy for Radar Design Trade-offs

Radar is of vital importance in many fields, such as autonomous driving, safety and surveillance applications. However, it suffers from stringent constraints on its design parametrization leading to multiple trade-offs. For example, the bandwidth in FMCW radars is inversely proportional with both the maximum unambiguous range and range resolution. In this work, we introduce a new method for circumventing radar design trade-offs. We propose the use of recent advances in computer vision, more specifically generative adversarial networks (GANs), to enhance low-resolution radar acquisitions into higher resolution counterparts while maintaining the advantages of the low-resolution parametrization. The capability of the proposed method was evaluated on the velocity resolution and range-azimuth trade-offs in micro-Doppler signatures and FMCW uniform linear array (ULA) radars, respectively.Comment: Accepted in EUSIPCO 2019, 5 page

GigaRad – a multi-purpose high-resolution ground-based radar system

Recently DLR has developed and constructed a new experimental radar instrument for various applications like radar signature collection, SAR/ISAR imaging, motion detection, tracking, etc., where high performance and high flexibility have been the key drivers for system design. Consequently a multi-purpose and multi-channel radar called GigaRad is operated in X band and allows an overall bandwidth of up to 6 GHz, resulting in a theoretical range resolution of up to 2.5 cm. Hence, primary obligation is a detailed analysis of various possible error sources, being of no or less relevance for low-resolution systems. A high degree of digital technology enables advanced signal processing and error correction to be applied. The paper outlines technical main features of the radar, the basic error correction strategy and illustrates some first imaging results

Automatic refocus and feature extraction of single-look complex SAR signatures of vessels

In recent years, spaceborne synthetic aperture radar ( SAR) technology has been considered as a complement to cooperative vessel surveillance systems thanks to its imaging capabilities. In this paper, a processing chain is presented to explore the potential of using basic stripmap single-look complex ( SLC) SAR images of vessels for the automatic extraction of their dimensions and heading. Local autofocus is applied to the vessels' SAR signatures to compensate blurring artefacts in the azimuth direction, improving both their image quality and their estimated dimensions. For the heading, the orientation ambiguities of the vessels' SAR signatures are solved using the direction of their ground-range velocity from the analysis of their Doppler spectra. Preliminary results are provided using five images of vessels from SLC RADARSAT-2 stripmap images. These results have shown good agreement with their respective ground-truth data from Automatic Identification System ( AIS) records at the time of the acquisitions.Postprint (published version

Experimental analysis of multistatic multiband radar signatures of wind turbines

This study presents the analysis of recent experimental data acquired using two radar systems at S-band and X-band to measure simultaneous monostatic and bistatic signatures of operational wind turbines near Shrivenham, UK. Bistatic and multistatic radars are a potential approach to mitigate the adverse effects of wind farm clutter on the performance of radar systems, which is a well-known problem for air traffic control and air defence radar. This analysis compares the simultaneous monostatic and bistatic micro-Doppler signatures of two operational turbines and investigates the key differences at bistatic angles up to 23°. The variations of the signature with different polarisations, namely vertical transmitted and vertical received and horizontal transmitted and horizontal received, are also discussed

Micro-doppler-based in-home aided and unaided walking recognition with multiple radar and sonar systems

Published in IET Radar, Sonar and Navigation. Online first 21/06/2016.The potential for using micro-Doppler signatures as a basis for distinguishing between aided and unaided gaits is considered in this study for the purpose of characterising normal elderly gait and assessment of patient recovery. In particular, five different classes of mobility are considered: normal unaided walking, walking with a limp, walking using a cane or tripod, walking with a walker, and using a wheelchair. This presents a challenging classification problem as the differences in micro-Doppler for these activities can be quite slight. Within this context, the performance of four different radar and sonar systems – a 40 kHz sonar, a 5.8 GHz wireless pulsed Doppler radar mote, a 10 GHz X-band continuous wave (CW) radar, and a 24 GHz CW radar – is evaluated using a broad range of features. Performance improvements using feature selection is addressed as well as the impact on performance of sensor placement and potential occlusion due to household objects. Results show that nearly 80% correct classification can be achieved with 10 s observations from the 24 GHz CW radar, whereas 86% performance can be achieved with 5 s observations of sonar