Search Results for author: Robin Rajamäki

Found 8 papers, 0 papers with code

Sparse Spatial Smoothing: Reduced Complexity and Improved Beamforming Gain via Sparse Sub-Arrays

no code implementations10 Mar 2024 Yinyan Bu, Robin Rajamäki, Anand Dabak, Rajan Narasimha, Anil Mani, Piya Pal

This paper addresses the problem of single snapshot Direction-of-Arrival (DOA) estimation, which is of great importance in a wide-range of applications including automotive radar.

Importance of array redundancy pattern in active sensing

no code implementations13 Jan 2024 Robin Rajamäki, Piya Pal

In this paper, we show that two array geometries with identical sum co-arrays, and the same number of physical and virtual sensors, need not achieve equal identifiability, regardless of the choice of waveform of a fixed reduced rank.

On array geometry and self-interference in full-duplex massive MIMO communications

no code implementations12 Jan 2024 Robin Rajamäki, Risto Wichman

This paper studies the role of the joint transmit-receive antenna array geometry in shaping the self-interference (SI) channel in full-duplex communications.

Harnessing Holes for Spatial Smoothing with Applications in Automotive Radar

no code implementations12 Jan 2024 Yinyan Bu, Robin Rajamäki, Pulak Sarangi, Piya Pal

We explore deliberately introducing holes into this virtual array to leverage resolution gains provided by the increased aperture.

Effect of Beampattern on Matrix Completion with Sparse Arrays

no code implementations12 Jan 2024 Robin Rajamäki, Mehmet Can Hücümenoğlu, Pulak Sarangi, Piya Pal

In this paper, we make advances towards solidifying this understanding by revealing the role of the physical beampattern of the sparse array on the performance of low rank matrix completion techniques.

Low-Rank Matrix Completion

Array-Informed Waveform Design for Active Sensing: Diversity, Redundancy, and Identifiability

no code implementations10 May 2023 Robin Rajamäki, Piya Pal

We derive necessary and sufficient conditions that the array geometry and transmit waveforms need to satisfy for the Kruskal rank -- and hence identifiability -- to be maximized.

Sparse Symmetric Linear Arrays with Low Redundancy and a Contiguous Sum Co-Array

no code implementations18 Oct 2020 Robin Rajamäki, Visa Koivunen

The two array structures also achieve low redundancy, and a contiguous sum and difference co-array, which allows resolving vastly more scatterers or sources than sensors

Hybrid Beamforming for Active Sensing using Sparse Arrays

no code implementations2 Dec 2019 Robin Rajamäki, Sundeep Prabhakar Chepuri, Visa Koivunen

Simulations demonstrate that a hybrid sparse array with very few elements, and even fewer front ends, can achieve the resolution of a fully digital uniform array at the expense of a longer image acquisition time.

Cannot find the paper you are looking for? You can Submit a new open access paper.