No description available.

Background: Evaluation of Data Visualization methods is a major challenge within the software and other industries. With complex data and requirements, often organizations require effective visualization methods which impact business decisions and convince stakeholders. This is a similar challenge in the development of a software demonstrator for the innovative safety radar system at ABB Jokab Safety whose aim is to improve the detection reliability using multiple radar sensors and requires an effective visualization method which will satisfy all the requirements. Objectives: The main objective of this study is to explore different data visualization methods involved in illustrating the raw data and with the help of developers, and other team members feedback with reference to existing literature and filter them with respect to the system functionalities. Establish evaluation criteria with relevant metrics to perform analytic evaluations on the visualization methods to determine an effective method. Methods: A Case Study which includes a multivocal literature review, is conducted at ABB Jokab Safety. Initially, to gather information on the subject, both formal and grey literature are explored and documented to filter our appropriate data visualization methods for this system. A task-based evaluation using semi-structured interview is conducted on 14 participants to determine an effective visualization method followed by statistical analysis to derive proper validation of the findings. The Response time, Ease of understanding, Confidence and Accuracy of the visualization methods are evaluated with feedback from the participants. Results: The Multivocal literature review filtered 16 primary articles which encouraged the use of 4 data visualization methods used in the safety radar system with distinct functionalities. A coordinate transformation engine to combine the data sets was also developed for the safety radar system contributing to the overall improvement of detection reliability. The evaluation including both quantitative and qualitative results validate each other’s findings through statistical tests like Kruskal-Wallis and Bonferroni post hoc followed by narrative analysis resulting a heat temperature plot to be more effective in the visualization of the radar data from multiple sensors. Conclusions: The results from this research provide insight into how data visualization evaluation can be implemented for real-time industrial problems and furnish validation process to determine an effective data visualization method. This study helps object detection using similar radar technologies visualize their data in an effective way and provides a scientific approach for evaluating similar data visualization problems.

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.

Conventional methods of CFAR detection always use windowing, in the sense that some number of cells are investigated and the target present/absent decision is made according to the composition of the cells in that window. The most commonly used versions of CFAR detection algorithms are cell averaging CFAR, smallest of cell averaging CFAR, greatest of cell averaging CFAR and order-statistics CFAR. These methods all use windowing to set the decision threshold. In this thesis, rather than using windowed CFAR algorithms, a new method of estimating the background threshold is presented, analyzed and simulated. This new method is called the Switching IIR CFAR algorithm and uses two IIR filters to accurately estimate the background threshold. Then, using a comparison procedure, one of the filters is selected as the current threshold estimate and used. The results are seen to be satisfactory and comparable to conventional CFAR methods. The basic advantages of using the SIIR CFAR method are computational simplicity, small memory requirement and acceptable performance under clutter edges and multiple targets.

As part of the processing of tracking targets, surveillance radars detect the presence of targets and estimate their locations. This dissertation re-examines some of the often ignored practical considerations of radar tracking. With the advent of digital computers, modern radars now use sampled versions of received signals for processing. Sampling rates used in practice result in the bin-straddling phenomenon, which is often treated as an undesired loss in signal power. Here, a signal model that explicitly models the sampling process is used in the derivation of the average loglikelihood ratio test (ALLRT), and its detection performance is shown to defeat the bin-straddling losses seen in traditional radar detectors. In monopulse systems, data samples are taken from the sum and difference channels, by which a target direction-of-arrival (DOA) estimate can be formed. Using the same signal model, we derive new estimators for target range, strength, and DOA and show performance benefits over traditional monopulse techniques that are predominant in practice. Since tracking algorithms require an error variance report on target parameter estimates, we propose using the generalized Cramer-Rao lower bound (GCRLB), which is the CRLB evaluated at estimates rather than true values, as an error variance report. We demonstrate the statistical efficiency and variance consistency of the new estimators. With several parameter estimates collected over time, tracking algorithms are used to compute track state estimates and predict future locations. Using agile- beam surveillance radars with programmable energy waveforms, optimal scheduling of radar resources is a topic of interest. In this dissertation, we focus on the energy management considerations of tracking highly maneuverable aircraft. A comparison between two competing interacting multiple model (IMM) filter configurations is made, and a recently proposed unbiased mixing procedure is extended to the case of three modes. Finally, we introduce the radar management operating curve (RMOC), which shows the fundamental tradeoff in radar time and energy, to aid radar designers in the selection of an overall operating signal-to-noise level.

No description available.

Under a number of practical operating scenarios, traditional moving target indicator (MTI) systems inadequately suppress ground clutter in airborne radar systems. Due to the moving platform, the clutter gains a nonzero relative velocity and spreads the power across Doppler frequencies. This obfuscates slow-moving targets of interest near the "direct current" component of the spectrum. In response, space-time adaptive processing (STAP) techniques have been developed that simultaneously operate in the space and time dimensions for effective clutter cancellation. STAP algorithms commonly operate under the assumption of homogeneous clutter, where the returns are described by complex, white Gaussian distributions. Empirical evidence shows that this assumption is invalid for many radar systems of interest, including high-resolution radar and radars operating at low grazing angles. We are interested in these heterogeneous cases, i.e., cases when the Gaussian model no longer suffices. Hence, the development of reliable STAP algorithms for real systems depends on the accuracy of the heterogeneous clutter models. The clutter of interest in this work includes heterogeneous texture clutter and point clutter. We have developed a cell-based clutter model (CCM) that provides simple, yet faithful means to simulate clutter scenarios for algorithm testing. The scene generated by the CMM can be tuned with two parameters, essentially describing the spikiness of the clutter scene. In one extreme, the texture resembles point clutter, generating strong returns from localized range-azimuth bins. On the other hand, our model can also simulate a flat, homogeneous environment. We prove the importance of model-based STAP techniques, namely knowledge-aided parametric covariance estimation (KAPE), in filtering a gamut of heterogeneous texture scenes. We demonstrate that the efficacy of KAPE does not diminish in the presence of typical spiky clutter. Computational complexities and susceptibility to modeling errors prohibit the use of KAPE in real systems. The computational complexity is a major concern, as the standard KAPE algorithm requires the inversion of an MNxMN matrix for each range bin, where M and N are the number of array elements and the number of pulses of the radar system, respectively. We developed a Gram Schmidt (GS) KAPE method that circumvents the need of a direct inversion and reduces the number of required power estimates. Another unavoidable concern is the performance degradations arising from uncalibrated array errors. This problem is exacerbated in KAPE, as it is a model-based technique; mismatched element amplitudes and phase errors amount to a modeling mismatch. We have developed the power-ridge aligning (PRA) calibration technique, a novel iterative gradient descent algorithm that outperforms current methods. We demonstrate the vast improvements attained using a combination of GS KAPE and PRA over the standard KAPE algorithm under various clutter scenarios in the presence of array errors.

The main aim of using radars in industrial safety system is to detect the presence of target accurately. The conventional methods of radar target detection algorithm such as the Cell averaging constant false alarm rate method (CA-CFAR), Greatest of constant false alarm method (GO-CFAR) and the Smallest of constant false alarm rate method (SO-CFAR) has their own disadvantage when it comes to precise target detection which is a key factor for a safety system. This thesis investigates the above mentioned conventional CFAR algorithms for its pros and cons in target detection and proposes a new and improved method called Cell Evaluation target detection method. The proposed method is shown to mitigate the limitations present and the assumptions made in the conventional target detection method. Further more angular estimation is performed to determine the precise location of the target and the artifacts due to the angular estimation is eliminated by aggregating the detected points from multiple radar modules by linear translation. This gives a better visualization of the target. / Radarteknik kan användas inom maskinsäkerhet (MS) för att detektera skyddsvärda objekt, typiskt människor i arbete nära maskiner. Konventionella metoder för detektering med given frekvens falsk alarm (eng. Constant False Alarm Rate (CFAR)) som baseras på medelvärden, har dock betydande brister. Främst beträffande precision och tillförlitlighet, vilket är centralt för MS. Exempel som studerats i detta examensarbete är “Cell-averaging CFAR” (CA-CFAR), “Greatest of CFAR” (GO-CFAR) samt “Smallest of CFAR” (SO-CFAR). Med målet att förbättra detektionen föreslås även en ny CFAR-metod, vilken benämns ”Cell Evaluation target detection”. I detta arbete visas denna metod undertrycka begränsningar med konventionella tekniker. Den undviker även en del antaganden som inte alltid stämmer i praktiken. Studien inkluderar även skattning av riktning. Det visas hur visualisering av skyddsobjekt kan förbättras, genom att felaktigheter elimineras efter sammanläggning av detektioner från flera radarmoduler efter koordinattransformation.

Thesis (Master').

Avec le contexte actuel des défis sécuritaires, la détection de petits objets devient un enjeu majeur pour lutter contre les actes malveillants. Mais les évolutions des technologies en bandes millimétriques et le potentiel de ces bandes, notamment autour de 60 GHz peuvent faciliter la conception de systèmes de plus en plus performants, permettant de répondre à ces enjeux. Cette thèse s’inscrit dans ce contexte pour proposer un système de détection ultra-large bande (ULB) millimétrique pour des applications de courte portée. Après un état de l'art détaillé des fondamentaux de la détection, nous présentons une validation de l'estimation de la surface équivalente radar (SER) par simulation avec le logiciel HFSS et par mesures en chambre anéchoïque. Ces deux études nous ont permis d'identifier les éléments essentiels à l'analyse système et les paramètres critiques de la détection d'objets canoniques cylindres et plaques. Une fois l'analyse système cernée, nous avons proposé une approche de détection multi-bande basée sur le radar monostatique pour améliorer la couverture de détection des systèmes, mais aussi et surtout pour pallier la limitation de la détection des objets en fonction de leur orientation par rapport à l'axe de viser de l'antenne. Nous avons également proposé une architecture (émetteur-récepteur) simple pouvant être associée au principe de détection. Le dimensionnement du système nous a permis d'identifier les amplificateurs faible bruit (LNAs) comme éléments critiques du récepteur et ainsi d'établir leurs spécifications pour atteindre les performances visées. Une comparaison des technologies SG13S de IHP et D007IH de OMMIC est menée dans la dernière partie de ce travail. Le choix de la technologie est justifié et la conception des LNAs sous ADS Keysight est détaillée. Une simulation de l'ensemble du système basée sur les performances des LNAs conçus et sur les simulations de SER est présentée pour illustrer la mise en œuvre de la détection. Enfin, les performances de la détection sont évaluées pour des cibles cylindriques et les apports du système proposé sont illustrés en comparaison avec une détection classique mono-bande / With the current context of security challenges, small objects detection is becoming a major issue in the fight against malicious acts. However, developments in millimeter-band technologies and the potential of these bands, particularly around 60 GHz, can facilitate the design of increasingly efficient systems to meet these challenges. This thesis is part of this context to propose an ultra-wideband (UWB) millimeter-wave detection system for short-range applications.After a detailed state-of-the-art of the fundamentals of detection, we present a validation of the radar cross-section (RCS) estimation by simulations with HFSS software and by measurements in anechoic chamber. These two studies allowed us to identify the essential elements for system analysis and the critical parameters for the detection of canonical objects such as cylinders and plates.Once the system analysis was identified, we proposed a multi-band detection approach based on monostatic radar to improve the detection coverage of the systems, but also and especially to overcome the limitation of objects detection according to their orientation in relation to the antenna boresight. We also proposed a simple architecture (transceiver) that could be associated with the detection principle. The system design allowed us to identify low noise amplifiers (LNAs) as critical elements of the receiver and thus establish their specifications to achieve the targeted performances.A comparison of IHP's SG13S and OMMIC's D007IH technologies is conducted in the last part of this work. The choice of technology is justified and the design of LNAs under ADS Keysight is detailed. A simulation of the entire system based on the performance of the designed LNAs and RCS simulations is presented to illustrate the implementation of detection. Finally, detection performances are evaluated for cylindrical targets and the contributions of the proposed system are illustrated in comparison with conventional single-band detection