Multi-carrier waveforms such as orthogonal frequency-division multiplexing (OFDM) found their way into radar applications in the last few years. However, currently, typically only a fraction of the large baseband bandwidth required to obtain high resolution is available in practice due to hardware limitations. In this paper, we propose a frequency agile sparse OFDM radar processing which allows the transmission of consecutive bandwidth-reduced OFDM pulses on different carriers and thereby covering a much larger measurement bandwidth in a measurement frame. Through joint processing of multiple narrowband pulses and compressed sensing methods, high resolution and unambiguity in the joint range-velocity profile is obtained comparable to an equivalent wideband OFDM. It shows that a baseband bandwidth of 20 % of the full channel bandwidth is sufficient to reliably obtain the same result as for an equivalent wideband OFDM signal. The proposed processing scheme is validated using simulations and radar measurements at 77 GHz.
In Compressed Sensing, a real-valued sparse vector has to be reconstructed from an underdetermined system of linear equations. However, in many applications of digital communications the elements of the unknown sparse vector are drawn from a finite set. The standard reconstruction algorithms of Compressed Sensing do not take this knowledge into account, hence, enhanced algorithms are required to achieve optimum performance. In this paper, we propose a new approach for the reconstruction of discrete-valued sparse signals. On the one hand, the algorithm is tailored to the discrete nature of the signal. On the other hand, reliability information is utilized within the successive reconstruction procedure. Via numerical simulations it is shown that the proposed variant of the Orthogonal Matching Pursuit clearly outperforms the wellknown standard versions.
An enhanced algorithm for the reconstruction of discrete-valued sparse vectors from an underdetermined system of linear equations under the presence of noise is presented. Knowledge from the field of compressed sensing and from digital communications is combined in optimising the estimation step in the orthogonal matching pursuit (OMP) algorithm according to the minimum mean-squared error (MMSE) criterion. Thereby, a gain in performance of more than 1 dB is achieved compared with the conventional OMP.
Abstract-In Compressed Sensing, a real-valued sparse vector has to be recovered from an underdetermined system of linear equations. In many applications, however, the elements of the sparse vector are drawn from a finite set. Adapted algorithms incorporating this additional knowledge are required for the discrete-valued setup. In this paper, turbo-based algorithms for both cases are elucidated and analyzed from a communications engineering perspective, leading to a deeper understanding of the algorithm. In particular, we gain the intriguing insight that the calculation of extrinsic values is equal to the unbiasing of a biased estimate and present an improved algorithm.
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