A review of deterministic sensing matrices

Abstract One of the biggest problems in signal acquisition is the large number of samples which results in the need of more sensory devices, bigger memory storage, higher consumption of batteries on sensors, lower transmission rates, etc. Since the signal frequency is often high, using the Nyquist rate becomes impractical. Compressive sensing is a practical solution to this condition. It employs the sparsity of the signal to sample the signal with fewer samples than the conventional method. Infinite possibilities arise with this method, including the possibility of developing cheaper and more efficient hardware. This paper will demonstrate several deterministic matrices used in signal compression to facilitate decoding and use well-known, easy to work with arrays.