Abstract
The big brown bat (Eptesicus fuscus) uses FM echolocation calls to accurately estimate range and resolve closely spaced objects in clutter and noise. They resolve glints spaced down to 2 μs in time delay which surpasses traditional signal processing techniques. The matched filter for these calls maintains 10 μs resolution while the inverse filter (IF) achieves higher resolution at the cost of significantly degraded detection performance. Recent work by Fontaine and Peremans [J. Acoust. Soc. Am.(2009)] demonstrated that a sparse representation of bat echolocation calls coupled with a random FIR filter sensing method facilitates distinguishing closely spaced objects over realistic SNRs. Their work raises the intriguing question of whether sensing approaches structured more like the bat’s auditory system contain the necessary information for the hyper-resolution observed in behavioral tests. This research estimates sparse echo signatures using a gammatone filterbank closer to the bat auditory system. The filterbank outputs are decimated then processed with ℓ1 minimization. Simulations demonstrate that this model maintains higher resolution than the MF and significantly better detection performance than the IF for SNRs of 5–45 dB while downsampling the return signal by a factor of 6. [Work supported by ONR and the SMART Program.]