Performance Analysis of Stochastic Signal Detection with Compressive Measurements
Compressed sensing (CS) enables the recovery of sparse or compressible signals from relatively a small number of randomized measurements compared to Nyquist-rate samples. Although most of the CS literature has focused on sparse signal recovery, exact recovery is not actually necessary in many signal processing applications. Solving inference problems with compressive measurements has been addressed by recent CS literature. This paper takes some further steps to investigate the potential of CS in signal detection problems. We provide theoretical performance limits verified by simulations for detection performance in arbitrary random signal detection with compressive measurements.
Wimalajeewa, Thakshila; Chen, Hao; and Varshney, Pramod K.. (2010). "Performance Analysis of Stochastic Signal Detection with Compressive Measurements". 2010 44th Asilomar Conference on Signals, Systems and Computers, November 7-10 2010, Piscataway, NJ, 813-817.