Energy Efficient Compression of Shock Data Using Compressed Sensing

This work analyses the potential of compressed sensing (CS) for compressing shock data signals of space launch vehicles. Multiple shock data signals were compressed using compressed sensing by exploiting the sparsity of the shock data signals in the time domain. Since shock data signals are sparse in wavelet domain also, thresholding based DWT compression was performed to compare the performance of compressed sensing. Three performance metrics, viz. Peak Root mean-square Difference (PRD), Compression Ratio (CR) and execution time were used. It is also evaluated how compression of the shock data reflects in the Shock Response Spectrum (SRS). The results clearly show that CS surpasses the DWT based compression in terms of execution time for a given CR but has slightly inferior results in terms of PRD for higher values of CR. With lower computation power requirements and dimensionality reduction, CS becomes an ideal choice for compressing shock data signals in a mobile signal processing system with constraints on processing power and for transmission over a power-hungry wireless network.