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A Multiple Input Multiple Output MIMO radar makes use of orthogonal transmit waveforms either in time, in frequency or in code, in order to exploit diversity gain (due to the larger number of degrees of freedom than their phase-array counterparts) and obtain more information from a radar scenario or target. Time Domain Multiplexing at transmit is an easy and cost-effective approach to achieve orthogonality but it implies increasing measurement time with the number of transmitter and a potential ambiguity for Doppler-shift estimation.
An alternative for exploiting MIMO diversity gain in the form of enhanced target detection and localization accuracy is to transmit simultaneously waveforms which are orthogonal in code. These waveforms need to be separated at every receiver in order to create virtual channels for each Tx-Rx module pair. However, such orthogonal signals are separable only when they are perfectly aligned. Therefore, total decoupling is not possible when time delays are considered. To achieve a suboptimal solution signals with good-correlation properties can be investigated, i.e., they should maintain orthogonality over a range of time delays big enough for the particular application, which is in general quite challenging.
The successful candidate will be employed for a maximum period of three years full-time equivalent and receives a generous financial package plus an additional mobility and family allowance according to the rules for Early Stage Researchers (ESRs) in an EU Marie Sklodowska-Curie Actions Innovative Training Networks (ITN). A career development plan will be prepared for each fellow in accordance with his/her supervisor and will include training, planned secondments and outreach activities in partner institutions of the network. The ESR fellows are supposed to complete their PhD thesis by the end of the 3rd year of their employment. For more information please visit the Marie Sklodowska-Curie Actions Innovative Training Networks website.
The main goal of this research project is to find optimum phase-coded waveforms for Compressive Sensing MIMO radar and employ sparse reconstruction to estimate unknown target parameters (delay, Doppler, angle). Additionally, other MIMO waveforms will be considered, time domain multiplexing (TDM), frequency domain multiplexing (FDM) and Doppler domain Multiplexing (DDM) for comparison and validation purposes.
Modern radar systems represent complex sensors with a variety of software-defined degrees of freedom. These systems, however, are not yet capable of self-optimization on the basis of findings derived from measurements.
In the Cognitive Radar department at the Fraunhofer FHR we combine methods of computer science, such as machine learning techniques and artificial intelligence methods, with advanced signal processing and electrical engineering approaches. The aim is to create”smart” sensor systems that are capable of adapting the waveform and the operating parameters to the scene and the mission context in a dynamic manner.
The successful candidate will join a strong and motivated research team with broad experience in MIMO radar, particularly in array design, high-resolution angular estimation, Compressed-Sensing and sparse reconstruction techniques, modulation schemes, Bayesian filtering, and adaptive systems.
More information can be found in the following links:
If you are interested in the MENELAOS project and this position, please apply online (https://www.menelaos-nt.eu/esr9/).