Sparse　signal　recovery　becomes　extremely　challenging　for　a　variety　of　real-time　applications.　In　this　paper,　we　improve　the　orthogonal　matching　pursuit　(OMP)　algorithm　based　on　parallel　correlation　indices　selection　mechanism　in　each　iteration　and　Goldschmidt　algorithm.　Simulation　results　show　that　the　improved　OMP　algorithm　with　a　reduced　number　of　iterations　and　low　hardware　complexity　of　matrix　operations　has　higher　success　rate　and　recovery　signal-to-noise-ratio　(RSNR)　for　sparse　signal　recovery.　This　paper　presents　an　efficient　complex-valued　system　hardware　architecture　of　the　recovery　algorithm　for　analog-to-information　structure　based　on　compressive　sensing.　The　proposed　architecture　is　implemented　and　validated　on　the　Xilinx　Virtex6　field-programmable　gate　array　(FPGA)　for　signal　reconstruction　with　N　=　1024,　K　=　36,　and　M　=　256.　The　implementation　results　showed　that　the　improved　OMP　algorithm　achieved　a　higher　RSNR　of　31.04　dB　compared　with　the　original　OMP　algorithm.　This　synthesized　design　consumes　a　few　percentages　of　the　hardware　resources　of　the　FPGA　chip　with　the　clock　frequency　of　135.4　MHZ　and　reconstruction　time　of　170　mu　s,　which　is　faster　than　the　existing　design.