Parametric Modeling for Damped Sinusoids From Multiple Channels

Abstract

The problem of parametric modeling for noisy damped sinusoidal signals from multiple channels is addressed. Utilizing the shift invariance property of the signal subspace, the number of distinct sinusoidal poles in the multiple channels is first determined. With the estimated number, the distinct frequencies and damping factors are then computed with the multi-channel weighted linear prediction method. The estimated sinusoidal poles are then matched to each channel according to the extreme value theory of distribution of random fields. Simulations are performed to show the performance advantages of the proposed multi-channel sinusoidal modeling methodology compared with existing methods.

Fundamental frequency and model order estimation using spatial filtering

“…In the multi-channel parameter estimation methods in [10] and [8], it has been considered that a desired signal is contaminated only by Gaussian noise, although in situations with spatially separated interference signals, which are likely in real scenarios, the joint fundamental frequency and constrained model order estimates [14] can be facilitated using spatial filters [20]. Simulations show beamforming will yield better results than the corresponding single-channel estimates, and the optimal MVDR beamformer outperforms the DS, as an example, for closely spaced signal sources.…”

“…In most of the state-of-the-art methods for fundamental frequency and number of harmonics estimations, the desired signal is assumed to be degraded by additive white Gaussian noise [7][8][9][10]. For example, the Markov-like weighted leastsquares (WLS) [11] (see also [1,12]) and the maximum a posteriori (MAP) [6,13] methods are fundamental frequency and number of harmonics estimators for only one signal source.…”

Simultaneous Model and Parameter Estimation for Joint Communication and Positioning

“…These methods have the advantage of performing robustly. Recent approaches [40], [41] to simultaneously estimate the model order and the parameters rely on a signal model described in the frequency domain. These models come with a systematic error.…”

“…In [17], the authors propose a promising shift-invariance based order selection technique for exponential data modeling. This technique was then further employed in a proposal for joint parameter estimation and model order selection in [40]. The authors could numerically show the impact of model order selection on the parameter estimation.…”

“…The authors could numerically show the impact of model order selection on the parameter estimation. As we will clarify when introducing the system framework used here, formulating the signal model in the frequency domain like in [17], [21], [40]- [44] is impossible without some undesired signal design impairments like oversampling for the here investigated joint communication and positioning framework. Secondly, it is not possible without accepting a limited estimation accuracy.…”