Computationally efficient 2 D adaptive filters based on essentially separable FIR structures
Adaptive filters are used successfully in sonar, radar, communications, seismology and biomedical engineering applications [1] This paper presents a theory of how computational complexity of 2 D adaptive FIR filters can be reduced by using essentially separable 2 D FIR filter structures. Results illustrate that essentially separable filters are more computationally efficient than conventional 2 D adaptive filters. The trade off is the error introduced by approximating the 2 D filter as a parallel bank of separable sub filters. Experimental results show that the coefficients of a 2 D non separable filter are more accurately identified as the number of separable sections increases in the essentially separable structure.
2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS)