CERES - User contributions [en]

Angle invariant radar reflectors for autmotive safety

2020-10-13T13:53:43Z

Pererik:

{{StudentProjectTemplate
|Summary=Angle invariant radar reflectors for autmotive safety
|Programme=Flexible
|Keywords=Radars, image analysis, automotive safety
|TimeFrame=Asap + 6 months
|Supervisor=Pererik Andreasson, Josef Bigun, Emil Nilsson, Ross Friel
|Examiner=Depends on course
|Author=Pererik Andreasson
|Level=Flexible
|Status=Finished
}}
The purpose is to design low cost radar reflectors to increase automotive safety. WIth the same purpose as optical reflectors (Swedish: ”reflexer”), sensitive traffic (pedestirams, prams, bicycles) could be equipped with a radar reflector which informs the car about the presence of such traffic. The main purpose would be to investigate how an angle (and distance) independent radar reflector could be designed and verify this with signal analysis, image analysis both numerically and possibly experimentally.

Angle invariant radar reflectors for autmotive safety

2019-11-11T09:37:17Z

Pererik:

{{StudentProjectTemplate
|Summary=Angle invariant radar reflectors for autmotive safety
|Programme=Flexible
|Keywords=Radars, image analysis, automotive safety
|TimeFrame=Asap + 6 months
|Supervisor=Pererik Andreasson, Josef Bigun, Emil Nilsson, Ross Friel
|Examiner=Depends on course
|Author=Pererik Andreasson
|Level=Flexible
|Status=Ongoing
}}
The purpose is to design low cost radar reflectors to increase automotive safety. WIth the same purpose as optical reflectors (Swedish: ”reflexer”), sensitive traffic (pedestirams, prams, bicycles) could be equipped with a radar reflector which informs the car about the presence of such traffic. The main purpose would be to investigate how an angle (and distance) independent radar reflector could be designed and verify this with signal analysis, image analysis both numerically and possibly experimentally.

Stefan Axelsson

2019-10-10T07:35:01Z

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Mark Dougherty

2019-10-10T07:34:14Z

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Angle invariant radar reflectors for autmotive safety

2019-10-10T07:33:18Z

Pererik: low cost radar reflectors to increase automotive safety

{{StudentProjectTemplate
|Summary=Angle invariant radar reflectors for autmotive safety
|Programme=Flexible
|Keywords=Radars, image analysis, automotive safety
|TimeFrame=Asap + 6 months
|Supervisor=Pererik Andreasson, Josef Bigun, Emil Nilsson, Ross Friel
|Examiner=Depends on course
|Author=Pererik Andreasson
|Level=Flexible
|Status=Open
}}
The purpose is to design low cost radar reflectors to increase automotive safety. WIth the same purpose as optical reflectors (Swedish: ”reflexer”), sensitive traffic (pedestirams, prams, bicycles) could be equipped with a radar reflector which informs the car about the presence of such traffic. The main purpose would be to investigate how an angle (and distance) independent radar reflector could be designed and verify this with signal analysis, image analysis both numerically and possibly experimentally.

Björn Nilsson

2019-10-10T07:26:24Z

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Physically small antennas

2019-10-10T07:24:38Z

Pererik: Designing meta-materials (computationally and experimentally) for electrically small antennas

{{StudentProjectTemplate
|Summary=Designing meta-materials (computationally and experimentally) for electrically small antennas
|Programme=Flexible
|Keywords=Antenna, computational electromagnetics, hearing aids
|TimeFrame=Asap + 6 months
|Supervisor=Björn Nilsson, Pererik Andreasson
|Examiner=Depends on course
|Author=Pererik Andreasson
|Level=Flexible
|Status=Open
}}
This project is aiming on paving the way and giving suggestion for new physical small antennas in hearing instruments. The antennas could be made using 3D technology, exploring volume, maximizing the usage of free physical space. Painted or screen-printed antennas is also of great interest. Exploring different methods to increase the antenna efficiency, mainly for the 2.4 GHz ISM band, by investigating the building methods and exploring different materials. The project partners have a long experience in antenna and RF building practice, covering both commercial and research aspects. The combined competence arching over antennas, 3D printed quasi-optics [1], low power IoT [2], RF-ASIC design [3], and nano-structure fabrication.. Hearing instruments need small antennas for wireless communication. The carrier frequency is in the 2.4GHz ISM band. Antennas are placed close to the head, often in the ear or behind the ear. Losses due to the close vicinity of the body need to be addressed. One of the antenna types used today is the magnetic loop antenna with a dimension much smaller than the wavelength, giving the antenna low efficiency. Investigation if it is possible to create new materials that enables miniaturization, good radiation properties, low loss, and low cost. Today the conducting antenna, made of copper, uses FR4 as carrier substrate.

Computationally efficient radar estimation from multiple radar sensors

2019-10-03T13:42:26Z

Pererik:

{{StudentProjectTemplate
|Summary=Computationally efficient radar estimation from multiple radar sensors.
|Programme=Computer science
|Keywords=Radar, point clouds, sensor fusion
|TimeFrame=asap, 6 months
|References=Test
|Prerequisites=Test
|Supervisor=Johan Thunberg, Emil Nilsson, Pererik Andreasson
|Examiner=Depending on which course
|Author=Johan Thunberg, Emil Nilsson
|Level=Flexible
|Status=Open
}}
Radar is emerging as a non-invasive alternative in applications such as healthcare monitoring. Key features such as pulse, respiration and movement patterns are possible to detect using spectral methods. A natural question in this context is how the estimation of such features can be improved by utilizing multiple radar sensors. In the context of computer vision, the benefits of using multiple cameras over one are well known. The multiple data obtained from different cameras can be used together to improve the accuracy of the feature estimations. However, the extension to multiple sensors comes at a price. The multiple data obtained from the different sensors need to be synchronized, or equivalently be associated or put into correspondence, which comprises a new problem not present for one sensor.

In computer vision such synchronization algorithms have been well studied and can be applied at different steps in the feature detection process. Radar data however, differs from the image data obtained in computer vision. There is a need to understand if algorithms from computer vision can be efficiently applied to such data on the one hand, and what new methods need to be developed on the other. To answer these questions we propose the following project:

A multi-radar test bed shall be developed. The radar sensors are placed at different positions and with different orientations. They observe a common scene. The scene can be either static (nothing is moving) or dynamic (a person or on object moves). The data from the multiple sensors will be used for evaluation of the algorithms. In order to benchmark the algorithms, additional information will be used to obtain the ground truth of the scenes in question. Such information is obtained by using regular cameras on the one hand, and knowledge about the objects’ geometry on the other. This means that, all-in-all, the testbed contains multiple radars, multiple cameras and objects with (at least partly known) geometry.
The suggested development environment is Matlab. However, the data format and storage is yet to be determined.
The main output of the project is a data set with radar data and camera data that can be used for algorithm evaluation. A secondary goal, which is not a requirement for completion of the project, is that a basic evaluation of some algorithm for feature estimation is implemented.

Computationally efficient radar estimation from multiple radar sensors

2019-10-03T13:37:46Z

Pererik: Created page with "{{StudentProjectTemplate |Summary=Computationally efficient radar estimation from multiple radar sensors. |Programme=Computer science |Keywords=Radar, point clouds, sensor fus..."

{{StudentProjectTemplate
|Summary=Computationally efficient radar estimation from multiple radar sensors.
|Programme=Computer science
|Keywords=Radar, point clouds, sensor fusion
|TimeFrame=asap -> 6 months
|Supervisor=Johan Thunberg, Emil Nilsson, Pererik Andreasson
|Examiner=Depending on which course
|Level=Flexible
|Status=Open
}}
Radar is emerging as a non-invasive alternative in applications such as healthcare monitoring. Key features such as pulse, respiration and movement patterns are possible to detect using spectral methods. A natural question in this context is how the estimation of such features can be improved by utilizing multiple radar sensors. In the context of computer vision, the benefits of using multiple cameras over one are well known. The multiple data obtained from different cameras can be used together to improve the accuracy of the feature estimations. However, the extension to multiple sensors comes at a price. The multiple data obtained from the different sensors need to be synchronized, or equivalently be associated or put into correspondence, which comprises a new problem not present for one sensor.

In computer vision such synchronization algorithms have been well studied and can be applied at different steps in the feature detection process. Radar data however, differs from the image data obtained in computer vision. There is a need to understand if algorithms from computer vision can be efficiently applied to such data on the one hand, and what new methods need to be developed on the other. To answer these questions we propose the following project:

A multi-radar test bed shall be developed. The radar sensors are placed at different positions and with different orientations. They observe a common scene. The scene can be either static (nothing is moving) or dynamic (a person or on object moves). The data from the multiple sensors will be used for evaluation of the algorithms. In order to benchmark the algorithms, additional information will be used to obtain the ground truth of the scenes in question. Such information is obtained by using regular cameras on the one hand, and knowledge about the objects’ geometry on the other. This means that, all-in-all, the testbed contains multiple radars, multiple cameras and objects with (at least partly known) geometry.
The suggested development environment is Matlab. However, the data format and storage is yet to be determined.
The main output of the project is a data set with radar data and camera data that can be used for algorithm evaluation. A secondary goal, which is not a requirement for completion of the project, is that a basic evaluation of some algorithm for feature estimation is implemented.

Computational algorithms for radar point target clouds

2019-10-01T15:37:14Z

Pererik: Created page with "{{StudentProjectTemplate |Summary=Computational efficient association of objects/targets/features in scene from multiple radar data. |Programme=Any |Keywords=image analysis, r..."

{{StudentProjectTemplate
|Summary=Computational efficient association of objects/targets/features in scene from multiple radar data.
|Programme=Any
|Keywords=image analysis, radar
|TimeFrame=6 months
|Supervisor=Emil Nilsson, Johan Thunberg
|Examiner=Depends on programme
}}

Emil Nilsson

2019-09-20T15:37:17Z

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Pererik Andreasson

2019-09-20T15:31:46Z

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Control system for automotive radar

2019-09-20T15:28:32Z

Pererik: Created page with "{{StudentProjectTemplate |Summary=Build an interfaced software to control an automotive radar (provided) |Programme=Computer science, embedded (suitable for both 15 and 30 cre..."

{{StudentProjectTemplate
|Summary=Build an interfaced software to control an automotive radar (provided)
|Programme=Computer science, embedded (suitable for both 15 and 30 credit theses)
|Keywords=automotive radar, embedded systems
|TimeFrame=6 mongths
|References=http://www.ti.com/tool/AWR1642BOOST#
|Prerequisites=programming skills either MatLab, Python
|Supervisor=Pererik Andreasson, Emil Nilsson
|Examiner=Depends on the course
|Author=Pererik Andreasson
|Level=Flexible
|Status=Open
}}