Millimeter wave (mmWave) radar systems are a leading technology in autonomous vehicle object sensing. The radar’s ability to detect surrounding objects is critical to its performance. One method of increasing object detection performance is to enhance object visibility. Frequency selective reflectors can increase object visibility. This work examines the performance of a mmWave 77 GHz radar system developed by Texas Instruments in conjunction with frequency selective surfaces. Two bandpass frequency selective surfaces are designed and fabricated using a loaded cross aperture configuration to analyze their application to object detection. The chosen design frequencies are 8 GHz and 79 GHz. The frequency selective surfaces are designed and simulated in 3-D electromagnetic simulation software, High Frequency Structure Simulator (HFSS). The frequency selective surfaces are fabricated on 127μm thick FR4 dielectric. The 8 GHz frequency selective surface demonstrates bandpass center frequency at 8.12 GHz. The 8 GHz and 79 GHz frequency selective surface reflection characteristics are compared to a copper sheets with the same physical cross section as each respective design. Although different testing methodology is used for each design, both frequency selective surfaces demonstrate bandpass characteristics at their respective design frequencies.

Autonomous drone navigation in classical approaches typically involves constructing a map representation and employing path planning and collision checking algorithms within that map. Recently, novel deep learning techniques combined with depth camera observations have emerged as alternative approaches capable of achieving comparable collision-free performance. While these methods have demonstrated effective collision-free performance in dense environments, they rely on low-noise range or visual data, which may not be feasible in extreme degraded environments characterized by factors such as dust, smoke, weak geometries, or low-texture areas. A possible alternative is to leverage recent progress in mmWave radar imaging, which previously has produced data of insufficient resolution for such purposes. Through the use of a Variational Autoencoder and existing collision prediction algorithms, the goal of this study is to prove the use of mmWave radar for navigating difficult environments. The results of the study exhibit successful navigation in simulated scenarios featuring sparse obstacles. Additionally, results of utilizing real-world mmWave radar data in example scenarios is provided to demonstrate the potential for further application of this technology. / Autonom navigation för drönare i klassiska tillvägagångssätt innebär vanligtvis att man konstruerar en kartrepresentation och använder vägplanerings- och kollisionskontrollalgoritmer inom den kartan. Nyligen har nya djupinlärningstekniker kombinerat med djupkameraobservationer framträtt som alternativa tillvägagångssätt som kan uppnå jämförbar prestanda utan kollisioner. Även om dessa metoder har visat effektiv prestanda utan kollisioner i täta miljöer, är de beroende av störningsfria avstånds- eller visuella data, vilket kanske inte är genomförbart i extrema försämrade miljöer som karakteriseras av faktorer som damm, rök, svaga geometrier eller områden med låg textur. Ett möjligt alternativ är att dra nytta av de senaste framstegen inom mmWave-radaravbildning, vilket tidigare har producerat data med otillräcklig upplösning för sådana ändamål. Genom användning av en varieabel autoencoder och befintliga kollisionsprognosalgoritmer syftar denna studie till att bevisa användningen av mmWave-radar för att navigera i svåra miljöer. Resultaten från studien visar framgångsrik navigering i simulerade scenarier med glesa hinder. Dessutom presenteras resultat från användning av verkliga mmWave-radardata i exempelscenarier för att visa potentialen för ytterligare tillämpningar av denna teknik.

This master thesis deals with implementation of the radar sensor for adaptive cruise control system. It discusses used technologies and processes and documents implementation of signal processing serving for the purpose of adaptive cruise control. It also describes the testing on the real data gathered in traffic. Texas Instrument's AWR1843 radar module was used as the sensor. This sensor represents currently very popular milimeter wave technology radars. Result of this master thesis are two implemented systems processing digital signal. One of them is a prototype application of the adaptive cruise control system, which also visualises the data. The other is implemented firmware of radar module doing real-time on-chip signal processing according to adaptive cruise control logic.

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Radars have been under steady development to track, identify, image, and classify targets. Modern radar systems, with the help of embedded systems, have additional comprehensive signal processing capabilities. They can extract useful information from very noisy data, e.g. interference from the environment and unwanted echoes which is collectively known as clutter in radar terms. Concerning the healthcare industry, radar applications for detection of vital signs, i.e. breathing and heart rate, have been extensively developed during the last few decades. Modern radar systems are expected to be a large part of non-intrusive monitoring in the coming smart home industry, where vital signs need to be monitored in the currently aging population. The research presented here is to break new ground in the radar-based healthcare technology, enabling detection of cold-induced shivering to such level that the micro-shivering can be clearly identified. To simplify the radar software optimization, a commercially available radar kit with demo application and a muscle model system using a vibration generator is used. The model is quantified through precise measurements. A simulated human body vital sign plus shivering is applied. By optimizing the radar software, the shivering amplitude and frequency are measured.