211 research outputs found
Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar
Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity.
In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system
Sparse Representation Denoising for Radar High Resolution Range Profiling
Radar high resolution range profile has attracted considerable attention in radar automatic target recognition. In practice, radar return is usually contaminated by noise, which results in profile distortion and recognition performance degradation. To deal with this problem, in this paper, a novel denoising method based on sparse representation is proposed to remove the Gaussian white additive noise. The return is sparsely described in the Fourier redundant dictionary and the denoising problem is described as a sparse representation model. Noise level of the return, which is crucial to the denoising performance but often unknown, is estimated by performing subspace method on the sliding subsequence correlation matrix. Sliding window process enables noise level estimation using only one observation sequence, not only guaranteeing estimation efficiency but also avoiding the influence of profile time-shift sensitivity. Experimental results show that the proposed method can effectively improve the signal-to-noise ratio of the return, leading to a high-quality profile
Through-the-Wall Imaging and Multipath Exploitation
We consider the problem of using electromagnetic sensing to estimate targets in complex environments, such as when they are hidden behind walls and other opaque objects. The often unknown electromagnetic interactions between the target and the surrounding area, make the problem challenging. To improve our results, we exploit information in the multipath of the objects surrounding both the target and the sensors. First, we estimate building layouts by using the jump-diffusion algorithm and employing prior knowledge about typical building layouts. We also take advantage of a detailed physical model that captures the scattering by the inner walls and efficiently utilizes the frequency bandwidth. We then localize targets hidden behind reinforced concrete walls. The sensing signals reflected from the targets are significantly distorted and attenuated by the embedded metal bars. Using the surface formulation of the method of moments, we model the response of the reinforced walls, and incorporate their transmission coefficients into the beamforming method to achieve better estimation accuracy. In a related effort, we utilize the sparsity constraint to improve electromagnetic imaging of hidden conducting targets, assuming that a set of equivalent sources can be substituted for the targets. We derive a linear measurement model and employ l1 regularization to identify the equivalent sources in the vicinity of the target surfaces. The proposed inverse method reconstructs the target shape in one or two steps, using single-frequency data. Our results are experimentally verified. Finally, we exploit the multipath from sensor-array platforms to facilitate direction finding. This in contrast to the usual approach, which utilizes the scattering close to the targets. We analyze the effect of the multipath in a statistical signal processing framework, and compute the Cramer-Rao bound to obtain the system resolution. We conduct experiments on a simple array platform to support our theoretical approach
Millimetre-Resolution Photonics-Assisted Radar
Radar is essential in applications such as anti-collision systems for driving, airport security screening,
and contactless vital sign detection. The demand for high-resolution and real-time recognition in
radar applications is growing, driving the development of electronic radars with increased bandwidth,
higher frequency, and improved reconfigurability. However, conventional electronic approaches are
challenging due to limitations in synthesising radar signals, limiting performance.
In contrast, microwave photonics-enabled radars have gained interest because they offer numerous
benefits compared to traditional electronic methods. Photonics-assisted techniques provide a broad
fractional bandwidth at the optical carrier frequency and enable spectrum manipulation, producing
wideband and high-resolution radar signals in various formats. However, photonic-based methods
face limitations like low time-frequency linearity due to the inherent nonlinearity of lasers, restricted RF bandwidth, limited stability of the photonic frequency multipliers, and difficulties in achieving
extended sensing with dispersion-based techniques.
In response to these challenges, this thesis presents approaches for generating broadband radar
signals with high time-frequency linearity using recirculated unidirectional optical frequency-shifted
modulation. The photonics-assisted system allows flexible bandwidth tuning from sub-GHz to over 30
GHz and requires only MHz-level electronics. Such a system offers millimetre-level range resolution
and a high imaging refresh rate, detecting fast-moving objects using the ISAR technique. With
millimetre-level resolution and micrometre accuracy, this system supports contactless vital sign
detection, capturing precise respiratory patterns from simulators and a living body using a cane toad.
In the end, we highlight the promise of merging radar and LiDAR, foreshadowing future
advancements in sensor fusion for enhanced sensing performance and resilience
Advanced photon counting techniques for long-range depth imaging
The Time-Correlated Single-Photon Counting (TCSPC) technique has emerged as a
candidate approach for Light Detection and Ranging (LiDAR) and active depth imaging
applications. The work of this Thesis concentrates on the development and
investigation of functional TCSPC-based long-range scanning time-of-flight (TOF)
depth imaging systems. Although these systems have several different configurations
and functions, all can facilitate depth profiling of remote targets at low light levels and
with good surface-to-surface depth resolution. Firstly, a Superconducting Nanowire
Single-Photon Detector (SNSPD) and an InGaAs/InP Single-Photon Avalanche Diode
(SPAD) module were employed for developing kilometre-range TOF depth imaging
systems at wavelengths of ~1550 nm. Secondly, a TOF depth imaging system at a
wavelength of 817 nm that incorporated a Complementary Metal-Oxide-Semiconductor
(CMOS) 32×32 Si-SPAD detector array was developed. This system was used with
structured illumination to examine the potential for covert, eye-safe and high-speed
depth imaging. In order to improve the light coupling efficiency onto the detectors, the
arrayed CMOS Si-SPAD detector chips were integrated with microlens arrays using
flip-chip bonding technology. This approach led to the improvement in the fill factor by
up to a factor of 15. Thirdly, a multispectral TCSPC-based full-waveform LiDAR
system was developed using a tunable broadband pulsed supercontinuum laser source
which can provide simultaneous multispectral illumination, at wavelengths of 531, 570,
670 and ~780 nm. The investigated multispectral reflectance data on a tree was used to
provide the determination of physiological parameters as a function of the tree depth
profile relating to biomass and foliage photosynthetic efficiency. Fourthly, depth
images were estimated using spatial correlation techniques in order to reduce the
aggregate number of photon required for depth reconstruction with low error. A depth
imaging system was characterised and re-configured to reduce the effects of scintillation
due to atmospheric turbulence. In addition, depth images were analysed in terms of
spatial and depth resolution
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