Name
Abstract | ||
|---|---|---|
A prototype compressed sensing radar processor boosts the accuracy of target range and velocity estimations by over 6× compared with conventional processing techniques. The prototype numerically solves basis pursuit denoising with a biologically plausible spiking neural network. A unique form of weight compression allows on-chip storage of all weights for the large fully connected network. Capable of producing over 200000 range-velocity scene reconstructions per second, the prototype improves throughput by 8× and efficiency by 18× over the state of the art. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/JSSC.2020.3025864 | IEEE Journal of Solid-State Circuits |
Keywords | DocType | Volume |
Compressed sensing (CS),digital integrated circuits,Doppler radar,radar detection,radar signal processing | Journal | 56 |
Issue | ISSN | Citations |
3 | 0018-9200 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Lawrence Brown | 1 | 0 | 0.34 |
| Matthew R. O'Shaughnessy | 2 | 5 | 2.48 |
| Christopher Rozell | 3 | 472 | 45.93 |
| Justin K. Romberg | 4 | 5856 | 514.08 |
| Michael P. Flynn | 5 | 74 | 8.98 |