Closed-form inverses for the mixed pixel/multipath interference problem in AMCW lidar

We present two new closed-form methods for mixed pixel/multipath interference separation in AMCW lidar systems. The mixed pixel/multipath interference problem arises from the violation of a standard range-imaging assumption that each pixel integrates over only a single, discrete backscattering source. While a numerical inversion method has previously been proposed, no close-form inverses have previously been posited. The first new method models reflectivity as a Cauchy distribution over range and uses four measurements at different modulation frequencies to determine the amplitude, phase and reflectivity distribution of up to two component returns within each pixel. The second new method uses attenuation ratios to determine the amplitude and phase of up to two component returns within each pixel. The methods are tested on both simulated and real data and shown to produce a significant improvement in overall error. While this paper focusses on the AMCW mixed pixel/multipath interference problem, the algorithms contained herein have applicability to the reconstruction of a sparse one dimensional signal from an extremely limited number of discrete samples of its Fourier transform

Waveform Diversity and Range-Coupled Adaptive Radar Signal Processing

Waveform diversity may offer several benefits to radar systems though often at the cost of reduced sensitivity. Multi-dimensional processing schemes are known to offer many degrees of freedom, which can be exploited to suppress the ambiguity inherent to pulse compression, array processing, and Doppler frequency estimation. Spatial waveform diversity can be achieved by transmitting different but correlated waveforms from each element of an antenna array. A simple yet effective scheme is employed to transmit different waveforms in different spatial directions. A new reiterative minimum mean squared error approach entitled Space-Range Adaptive Processing, which adapts simultaneously in range and angle, is derived and shown in simulation to offer enhanced performance when spatial waveform diversity is employed relative to both conventional matched filtering and sequentially adapting in angle and then range. The same mathematical framework is utilized to develop Time-Range Adaptive Processing (TRAP) algorithm which is capable of simultaneously adapting in Doppler frequency and range. TRAP is useful when pulse-to-pulse changing of the center frequency or waveform coding is used to achieve enhanced range resolution or unambiguous ranging, respectively. The inherent computational complexity of the new multi-dimensional algorithms is addressed by segmenting the full-dimension cost functions, yielding a reduced-dimensional variants of each. Finally, a non-adaptive approach based on the multi-dimensional TRAP signal model is utilized to develop an efficient clutter cancellation technique capable of suppressing multiple range intervals of clutter when waveform diversity is applied to pulse-Doppler radar

Contribuitions and developments on nonintrusive load monitoring

Energy efficiency is a key subject in our present world agenda, not only because of greenhouse gas emissions, which contribute to global warming, but also because of possible supply interruptions. In Brazil, energy wastage in the residential market is estimated to be around 15%. Previous studies have indicated that the most savings were achieved with specific appliance, electricity consumption feedback, which caused behavioral changes and encouraged consumers to pursue energy conservation. Nonintrusive Load Monitoring (NILM) is a relatively new term. It aims to disaggregate global consumption at an appliance level, using only a single point of measurement. Various methods have been suggested to infer when appliances are turned on and off, using the analysis of current and voltage aggregated waveforms. Within this context, we aim to provide a methodology for NILM to determine which sets of electrical features and feature extraction rates, obtained from aggregated household data, are essential to preserve equivalent levels of accuracy; thus reducing the amount of data that needs to be transferred to, and stored on, cloud servers. As an addendum to this thesis, a Brazilian appliance dataset, sampled from real appliances, was developed for future NILM developments and research. Beyond that, a low-cost NILM smart meter was developed to encourage consumers to change their habits to more sustainable methods.Eficiência energética é um assunto essencial na agenda mundial. No Brasil, o desperdício de energia no setor residencial é estimado em 15%. Estudos indicaram que maiores ganhos em eficiência são conseguidos quando o usuário recebe as informações de consumo detalhadas por cada aparelho, provocando mudanças comportamentais e incentivando os consumidores na conservação de energia. Monitoramento não intrusivo de cargas (NILM da sigla em inglês) é um termo relativamente novo. A sua finalidade é inferir o consumo de um ambiente até observar os consumos individualizados de cada equipamento utilizando-se de apenas um único ponto de medição. Métodos sofisticados têm sido propostos para inferir quando os aparelhos são ligados e desligados em um ambiente. Dentro deste contexto, este trabalho apresenta uma metodologia para a definição de um conjunto mínimo de características elétricas e sua taxa de extração que reduz a quantidade de dados a serem transmitidos e armazenados em servidores de processamento de dados, preservando níveis equivalentes de acurácia. São utilizadas diferentes técnicas de aprendizado de máquina visando à caracterização e solução do problema. Como adendo ao trabalho, apresenta-se um banco de dados de eletrodomésticos brasileiros, com amostras de equipamentos nacionais para desenvolvimentos futuros em NILM, além de um medidor inteligente de baixo custo para desagregação de cargas, visando tornar o consumo de energia mais sustentável

Development of a Compact, Configurable, Real-Time Range Imaging System

This thesis documents the development of a time-of-flight (ToF) camera suitable for autonomous mobile robotics applications. By measuring the round trip time of emitted light to and from objects in the scene, the system is capable of simultaneous full-field range imaging. This is achieved by projecting amplitude modulated continuous wave (AMCW) light onto the scene, and recording the reflection using an image sensor array with a high-speed shutter amplitude modulated at the same frequency (of the order of tens of MHz). The effect is to encode the phase delay of the reflected light as a change in pixel intensity, which is then interpreted as distance. A full field range imaging system has been constructed based on the PMD Technologies PMD19k image sensor, where the high-speed shuttering mechanism is builtin to the integrated circuit. This produces a system that is considerably more compact and power efficient than previous iterations that employed an image intensifier to provide sensor modulation. The new system has comparable performance to commercially available systems in terms of distance measurement precision and accuracy, but is much more flexible with regards to its operating parameters. All of the operating parameters, including the image integration time, sensor modulation phase offset and modulation frequency can be changed in realtime either manually or automatically through software. This highly configurable system serves as an excellent platform for research into novel range imaging techniques. One promising technique is the utilisation of measurements using multiple modulation frequencies in order to maximise precision over an extended operating range. Each measurement gives an independent estimate of the distance with limited range depending on the modulation frequency. These are combined to give a measurement with extended maximum range using a novel algorithm based on the New Chinese Remainder Theorem. A theoretical model for the measurement precision and accuracy of the new algorithm is presented and verified with experimental results. All distance image processing is performed on a per-pixel basis in real-time using a Field Programmable Gate Array (FPGA). An efficient hardware implementation of the phase determination algorithm for calculating distance is investigated. The limiting resource for such an implementation is random access memory (RAM), and a detailed analysis of the trade-off between this resource and measurement precision is also presented

Phase Interferometry Direction Finding

This paper describes the development of a phase interferometry direction finding system for an airborne platform developed for MIT Lincoln Laboratory. A phase interferometer uses the phase difference to determine the Angle of Arrival (AoA) of a received signal. The team utilized three antennas to resolve phase ambiguities and determined the AoA for received electromagnetic signals in the X band to within 0.1 degrees in simulations including realistic noise models for a 170 degree field of view. A prototype was implemented using an FPGA-based board for data acquisition connected via USB to a PC for analysis, which connected to another PC via a TCP connection for display. The team developed a graphical user interface for the system to display results to a system operator

Design of an intrinsically safe series-series compensation WPT system for automotive LiDAR

The earliest and simplest impedance compensation technique used in inductive wireless power transfer (WPT) design is the series-series (SS) compensation circuit, which uses capacitors in series with both primary and secondary coils of an air-gapped transformer. Despite of its simplicity at the resonant condition, this configuration exhibits a major sensitivity to variations of the load attached to the secondary, especially when higher coupling coefficients are used in the design. In the extreme situation that the secondary coil is left at open circuit, the current at the primary coil may increase above the safety limits for either the power converter driving the primary coil or the components in the primary circuit, including the coil itself. An approach often used to minimize this problem is detuning, but this also reduces the electrical efficiency of the power transfer. In low power, fixed-distance stationary WPT, a fair trade-off between efficiency and safety must be verified. This paper aims to consolidate a simple design procedure for such a SS-compensation, exemplifying its use in the prototype of a WPT system for automotive light detection and ranging (LiDAR) equipment. The guidelines herein provided should equally apply to other low power applications.This work has been supported by national funds through FCT—Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019, and also European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 037902; Funding Reference: POCI-01-0247-FEDER-037902]

Spectral LADAR: Active Range-Resolved Imaging Spectroscopy

Imaging spectroscopy using ambient or thermally generated optical sources is a well developed technique for capturing two dimensional images with high per-pixel spectral resolution. The per-pixel spectral data is often a sufficient sampling of a material's backscatter spectrum to infer chemical properties of the constituent material to aid in substance identification. Separately, conventional LADAR sensors use quasi-monochromatic laser radiation to create three dimensional images of objects at high angular resolution, compared to RADAR. Advances in dispersion engineered photonic crystal fibers in recent years have made high spectral radiance optical supercontinuum sources practical, enabling this study of Spectral LADAR, a continuous polychromatic spectrum augmentation of conventional LADAR. This imaging concept, which combines multi-spectral and 3D sensing at a physical level, is demonstrated with 25 independent and parallel LADAR channels and generates point cloud images with three spatial dimensions and one spectral dimension. The independence of spectral bands is a key characteristic of Spectral LADAR. Each spectral band maintains a separate time waveform record, from which target parameters are estimated. Accordingly, the spectrum computed for each backscatter reflection is independently and unambiguously range unmixed from multiple target reflections that may arise from transmission of a single panchromatic pulse. This dissertation presents the theoretical background of Spectral LADAR, a shortwave infrared laboratory demonstrator system constructed as a proof-of-concept prototype, and the experimental results obtained by the prototype when imaging scenes at stand off ranges of 45 meters. The resultant point cloud voxels are spectrally classified into a number of material categories which enhances object and feature recognition. Experimental results demonstrate the physical level combination of active backscatter spectroscopy and range resolved sensing to produce images with a level of complexity, detail, and accuracy that is not obtainable with data-level registration and fusion of conventional imaging spectroscopy and LADAR. The capabilities of Spectral LADAR are expected to be useful in a range of applications, such as biomedical imaging and agriculture, but particularly when applied as a sensor in unmanned ground vehicle navigation. Applications to autonomous mobile robotics are the principal motivators of this study, and are specifically addressed

Development of passive bistatic radars based on orthogonal frequency-division multiplexing modulated signals for short and medium range surveillance

The main activity conducted during the research activity is the development of PBR systems based on OFDM signals of opportunity. In particular, a DAB based PBR for air traffic control (ATC) applications and a DVB-T based PBR for maritime surveillance have been objects of study

Direction of Arrival Estimation and Tracking with Sparse Arrays

Direction of Arrival (DOA) estimation and tracking of a plane wave or multiple plane waves impinging on an array of sensors from noisy data are two of the most important tasks in array signal processing, which have attracted tremendous research interest over the past several decades. It is well-known that the estimation accuracy, angular resolution, tracking capacity, computational complexity, and hardware implementation cost of a DOA estimation and/or tracking technique depend largely on the array geometry. Large arrays with many sensors provide accurate DOA estimation and perfect target tracking, but they usually suffer from a high cost for hardware implementation. Sparse arrays can yield similar DOA estimates and tracking performance with fewer elements for the same-size array aperture as compared to the traditional uniform arrays. In addition, the signals of interest may have rich temporal information that can be exploited to effectively eliminate background noise and significantly improve the performance and capacity of DOA estimation and tracking, and/or even dramatically reduce the computational burden of estimation and tracking algorithms. Therefore, this thesis aims to provide some solutions to improving the DOA estimation and tracking performance by designing sparse arrays and exploiting prior knowledge of the incident signals such as AR modeled sources and known waveforms. First, we design two sparse linear arrays to efficiently extend the array aperture and improve the DOA estimation performance. One scheme is called minimum redundancy sparse subarrays (MRSSA), where the subarrays are used to obtain an extended correlation matrix according to the principle of minimum redundancy linear array (MRLA). The other linear array is constructed using two sparse ULAs, where the inter-sensor spacing within the same ULA is much larger than half wavelength. Moreover, we propose a 2-D DOA estimation method based on sparse L-shaped arrays, where the signal subspace is selected from the noise-free correlation matrix without requiring the eigen-decomposition to estimate the elevation angle, while the azimuth angles are estimated based on the modified total least squares (TLS) technique. Second, we develop two DOA estimation and tracking methods for autoregressive (AR) modeled signal source using sparse linear arrays together with Kalman filter and LS-based techniques. The proposed methods consist of two common stages: in the first stage, the sources modeled by AR processes are estimated by the celebrated Kalman filter and in the second stage, the efficient LS or TLS techniques are employed to estimate the DOAs and AR coefficients simultaneously. The AR-modeled sources can provide useful temporal information to handle cases such as the ones, where the number of sources is larger than the number of antennas. In the first method, we exploit the symmetric array to transfer a complex-valued nonlinear problem to a real-valued linear one, which can reduce the computational complexity, while in the second method, we use the ordinary sparse arrays to provide a more accurate DOA estimation. Finally, we study the problem of estimating and tracking the direction of arrivals (DOAs) of multiple moving targets with known signal source waveforms and unknown gains in the presence of Gaussian noise using a sparse sensor array. The core idea is to consider the output of each sensor as a linear regression model, each of whose coefficients contains a pair of DOAs and gain information corresponding to one target. These coefficients are determined by solving a linear least squares problem and then updating recursively, based on a block QR decomposition recursive least squares (QRD-RLS) technique or a block regularized LS technique. It is shown that the coefficients from different sensors have the same amplitude, but variable phase information for the same signal. Then, simple algebraic manipulations and the well-known generalized least squares (GLS) are used to obtain an asymptotically-optimal DOA estimate without requiring a search over a large region of the parameter space

Multistatic Passive Weather Radar

Practical and accurate estimation of three-dimensional wind fields is an ongoing challenge in radar meteorology. Multistatic (single transmitter / multiple receivers) radar architectures offer a cost effective solution for obtaining the multiple Doppler measurements necessary to achieve such estimates. In this work, the history and fundamental concepts of multistatic weather radar are reviewed. Several developments in multistatic weather radar enabled by recent technological progress, such as the widespread availability of high performance single-chip RF transceivers and the proliferation of phased array weather radars, are then presented. First, a network of compact, low-cost passive receiver prototypes is used to demonstrate a set of signal processing techniques that have been developed to enable transmitter / receiver synchronization through sidelobe radiation. Next, a pattern synthesis technique is developed which allows for the use of sidelobe whitening to mitigate velocity biases in multistatic radar systems. The efficacy of this technique is then demonstrated using a multistatic weather radar system simulator