The fan-chirp transform for non-stationary harmonic signals
Signal Processing
Conditional spectral moments in matching pursuit based on the chirplet elementary function
Digital Signal Processing
Modified adaptive Chirplet decomposition with application in ISAR imaging of maneuvering targets
EURASIP Journal on Advances in Signal Processing
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
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The chirp function is one of the most fundamental functions in nature. Many natural events, for example, most signals encountered in seismology and the signals in radar systems, can be modeled as the superposition of short-lived chirp functions. Hence, the chirp-based signal representation, such as the Gaussian chirplet decomposition, has been an active research area in the field of signal processing. A main challenge of the Gaussian chirplet decomposition is that Gaussian chirplets do not form an orthogonal basis. A promising solution is to employ adaptive type signal decomposition schemes, such as the matching pursuit. The general underlying theory of the matching pursuit method has been well accepted, but the numerical implementation, in terms of computational speed and accuracy, of the adaptive Gaussian chirplet decomposition remains an open research topic. We present a fast refinement algorithm to search for optimal Gaussian chirplets. With a coarse dictionary, the resulting adaptive Gaussian chirplet decomposition is not only fast but is also more accurate than other known adaptive schemes. The effectiveness of the algorithm introduced is demonstrated by numerical simulations