Multistory parking can accommodate a maximum number of vehicles in a limited space. However, in multistory and outdoor busy parking, it becomes challenging for drivers to find free parking slots, and they have to search in different parking lanes and floors. This results in the wastage of fuel and time and contaminates the atmosphere. To address this issue, the state-of-the-art solution exploits an optical sensor to detect if a car is present in the parking slot or not. The solution requires an optical sensor for each parking slot, which makes the optical sensor solution expensive and complex. Moreover, such a solution fails in harsh weather conditions in outdoor parking. A low-cost mm-wave radar-based solution is proposed to detect multiple cars using only one radar and pass the corresponding information to the developed computer/mobile app. Using the app, users can view the free parking slots in advance. Our proposed solution also provides free parking slot information at the parking entrance. A driver can select one from the available ones and park his car there. In the next version, people will be able to book the parking slots from the available ones. To detect the presence of vehicle in multiple parking slots, our proposed system uses Infineon’s Postion2Go module, which is one transmit and two receive antenna frequency-modulated continuous-wave (FMCW) radar. We develop a parking model using stationary objects, clutter, and vehicles in the parking. The vehicle detection algorithm is based on background subtraction and updation. First, the background is subtracted from each received snapshot to prominent the parking slot where the latest activity has been done. Then, once the activity is stable (the vehicle is fully parked or left), the background is updated. The algorithm also uses constant-false-alarm-rate (CFAR) for adaptive detection of vehicles and thresholds to detect different activities. The method of monitoring outdoor parking is simple, while the indoor parking is more challenging. Demonstrated results show the effectiveness of the proposed system.

Most of the operating vessel traffic management systems experience problems, such as track loss and track swap, which may cause confusion to the traffic regulators and lead to potential hazards in the harbour operation. The reason is mainly due to the limited adaptive capabilities of the algorithms used in the detection process. The decision on whether a target is present is usually based on the magnitude of the returning echoes. Such a method has a low efficiency in discriminating between the target and clutter, especially when the signal to noise ratio is low. The performance of radar target detection depends on the features, which can be used to discriminate between clutter and targets. To have a significant improvement in the detection of weak targets, more obvious discriminating features must be identified and extracted. This research investigates conventional Constant False Alarm Rate (CFAR) algorithms and introduces the approach of applying ar1ificial intelligence methods to the target detection problems. Previous research has been unde11aken to improve the detection capability of the radar system in the heavy clutter environment and many new CFAR algorithms, which are based on amplitude information only, have been developed. This research studies these algorithms and proposes that it is feasible to design and develop an advanced target detection system that is capable of discriminating targets from clutters by learning the .different features extracted from radar returns. The approach adopted for this further work into target detection was the use of neural networks. Results presented show that such a network is able to learn particular features of specific radar return signals, e.g. rain clutter, sea clutter, target, and to decide if a target is present in a finite window of data. The work includes a study of the characteristics of radar signals and identification of the features that can be used in the process of effective detection. The use of a general purpose marine radar has allowed the collection of live signals from the Plymouth harbour for analysis, training and validation. The approach of using data from the real environment has enabled the developed detection system to be exposed to real clutter conditions that cannot be obtained when using simulated data. The performance of the neural network detection system is evaluated with further recorded data and the results obtained are compared with the conventional CFAR algorithms. It is shown that the neural system can learn the features of specific radar signals and provide a superior performance in detecting targets from clutters. Areas for further research and development arc presented; these include the use of a sophisticated recording system, high speed processors and the potential for target classification.

Submitted by Israel Vieira Neto (israel.vieiraneto@ufpe.br) on 2015-03-04T14:38:14Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação_Wlademir.pdf: 1036658 bytes, checksum: 04fe836142917907ff72a77d87b726a1 (MD5) / Made available in DSpace on 2015-03-04T14:38:14Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação_Wlademir.pdf: 1036658 bytes, checksum: 04fe836142917907ff72a77d87b726a1 (MD5) Previous issue date: 2012-04-27 / O setor de energia elétrica, sobretudo o voltado à distribuição, nos últimos anos vem sofrendo influencia de diversos fatores, que vão desde o econômico ao climático. Altamente regulamentadas, as concessionárias de energia elétrica possuem regras que eventualmente podem mudar ao longo dos ciclos tarifários, que ocorrem a cada quatro anos e de certa forma contribuir diretamente para o resultado financeiro das companhias. Outro aspecto de considerável relevância diz respeito ao social, que pode está diretamente correlacionado com o fator adimplência. O presente trabalho visa destacar as variáveis que de alguma forma contribuem direta ou indiretamente para com o desempenho do Fluxo de Caixa de Serviço da Companhia Energética de Pernambuco – CELPE, montando uma equação, através da metodologia de Regressão Dinâmica, que possa explicar seu desempenho e posteriormente calcular através de simulação Monte Carlo o seu Valor em Risco (CFaR) e poder dimensionar com esse resultado o impacto no Valor da empresa. Destarte, após realizadas todas as simulações observa-se que de fato, o fluxo de caixa da empresa é consideravelmente impactado pelas variáveis embutidas no modelo. Ao final, observas-se que a simulação com um CFaR de 5% proporcionou um valor da empresa 10,2% menor do que o contabilizado no cenário utilizando o Fluxo de Caixa de Serviço da CELPE calculado através do modelo de Regressão Dinâmica. Em termo absoluto essa redução representou R$ 303,94 milhões.

abstract: Constant false alarm rate is one of the essential algorithms in a RADAR detection system. It allows the RADAR system to dynamically set thresholds based on the data power level to distinguish targets with interfering noise and clutters. To have a better acknowledgment of constant false alarm rate approaches performance, three clutter models, Gamma, Weibull, and Log-normal, have been introduced to evaluate the detection's capability of each constant false alarm rate algorithm. The order statistical constant false alarm rate approach outperforms other conventional constant false alarm rate methods, especially in clutter evolved environments. However, this method requires high power consumption due to repeat sorting. In the automotive RADAR system, the computational complexity of algorithms is essential because this system is in real-time. Therefore, the algorithms must be fast and efficient to ensure low power consumption and processing time. The reduced computational complexity implementations of cell-averaging and order statistic constant false alarm rate were explored. Their big O and processing time has been reduced. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2020

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Conventional fixed threshold detectors set a fixed threshold based on the overall statistical characteristics of the spatially uniform clutter over all ranges to give a specific probability of false alarm and detection. However, in radar applications clutter statistics are not known a priori. Constant False Alarm Rate (CFAR) techniques provide an adaptive threshold to estimate the clutter statistics and to distinguish targets from clutter. In Cell Averaging CFAR (CA-CFAR) the threshold is controlled by averaging the fixed size CFAR cells surrounding the cell under test. In this thesis, radar detection of targets in sea clutter modelled by compound Kdistribution is examined from a statistical detection viewpoint by Monte Carlo simulations. The performance of CA-CFAR processors is analysed under varying conditions of sea clutter spatial correlation and spikiness for several cases of false alarm probability, the length of cell size used in the CFAR processor and the number of pulses integrated prior to CA-CFAR processor. v The detection performance of CA-CFAR is compared with the performance of fixed threshold detection. The performance evaluations are quantified by CFAR loss. CFAR loss is defined as the increase in average signal to clutter ratio compared to that of fixed threshold, required to achieve a given probability of detection and probability of false alarm. Curves for CFAR loss to the spikiness and spatial correlation of clutter, number of pulses integrated and the length of cell size are presented.

Det här examensarbetet utvärderar om det är möjligt att använda en eller flera GPUs för att under realtidsförhållanden utföra radarsignalbehandling i ett pulsdopplerradarsystem. En kedja med radarsignalbehandlingsalgoritmer som används för att utföra detektion har implementerats med CUDA och sedan prestandaanalyserats med fokus på låg exekveringstid. Två CFAR-detektionsalgoritmer, CA- och OS-CFAR, har inkluderats i analysen. För CFAR-algoritmerna har flera alternativ formulerats och implementerats för att utvärdera hur de bäst kan anpassas för att exekvera på en GPU. Prestandaanalysen av de implementerade algoritmerna visar att det är möjligt för det tänkta systemet att använda grafikkort för att utföra radarsignalbehandlingen i realtid. Implementationslösningar har presenterats både för CA- och OS-CFAR som uppfyller tidskraven för systemet, i vissa fall med god marginal. Lägst exekveringstider erhölls när vissa kompromisser gjordes med algoritmernas flexibilitet. För CA-CFAR erhölls lägst exekveringstider när ett Summed Area Table användes för tröskelvärdesberäkningen. För OS-CFAR uppmättes de lägsta exekveringstiderna när en rankjämförelse gjordes istället för en full sortering. Prestandaanalysen visar även att det på ett effektivt sätt går att skala upp implementationen för att utnyttja fler än en GPU.

This thesis presents a simulation of an LFM pulse-Doppler radar for surface-to-air applications and compares the performance of multiple CFAR detectors in processing the resulting range-Doppler maps. Each CFAR detector is reviewed and simulated. Their effectiveness in reducing target masking is analyzed. In addition, a new CFAR detector, the RDWT-CA-CFAR detector, is developed that uses the CDF 5/3 wavelet to decompose the range-data of the range-Doppler map along the range dimension and filter the target data from the reference cells, as a means to reduce or eliminate target masking. The QccPack library is used to perform RDWT functions. It is shown that the novel RDWT-CA-CFAR detector performs better in processing range-Doppler maps when compared to the other robust CFAR detectors covered in this project.

Lightning strokes create powerful electromagnetic pulses that routinely cause very low frequency (VLF) waves to propagate across hemispheres along geomagnetic field lines. VLF antenna receivers can be used to detect these whistler waves generated by these lightning strokes. The particular time/frequency dependence of the received whistler wave enables the estimation of electron density in the plasmasphere region of the magnetosphere. Therefore the identification and characterisation of whistlers are important tasks to monitor the plasmasphere in real time and to build large databases of events to be used for statistical studies. The current state of the art in detecting whistler is the Automatic Whistler Detection (AWD) method developed by Lichtenberger (2009) [1]. This method is based on image correlation in 2 dimensions and requires significant computing hardware situated at the VLF receiver antennas (e.g. in Antarctica). The aim of this work is to develop a machine learning based model capable of automatically detecting whistlers in the data provided by the VLF receivers. The approach is to use a combination of image classification and localisation on the spectrogram data generated by the VLF receivers to identify and localise each whistler. The data at hand has around 2300 events identified by AWD at SANAE and Marion and will be used as training, validation, and testing data. Three detector designs have been proposed. The first one using a similar method to AWD, the second using image classification on regions of interest extracted from a spectrogram, and the last one using YOLO, the current state of the art in object detection. It has been shown that these detectors can achieve a misdetection and false alarm rate, respectively, of less than 15% on Marion's dataset. It is important to note that the ground truth (initial whistler label) for data used in this study was generated using AWD. Moreover, SANAE IV data was small and did not provide much content in the study.

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