In most applications, component extraction is important when components of non-stationary multi-component signal are key features to be monitored and analyzed. Existing methods are either sensitive to noise or forced to select a proper time-frequency representation for the considered signal. In this paper, we present a novel component extraction method for non-stationary multi-component signal. The proposed method combines parameterized de-chirping and band-pass filter to obtain components of multi-component signal, which avoids dealing with time-frequency representation of the signal and works well under heavy noise. In addition, it is able to analyze the multi-component signal whose components have intersected instantaneous frequency trajectories. Simulation results show that the proposed method is promising in analyzing complicated multi-component signals. Moreover, it works effective in a high noise environment in terms of improving the output signal-to-noise rate for the interested component.

Component Extraction for Non-Stationary Multi-Component Signal Using Parameterized De-chirping and Band-Pass Filter

This paper addresses the problem of estimating the instantaneous frequency (IF) of multicomponent radar signals, which are assumed to be frequency-modulated (FM). The difficulties lie in that different FM components which overlap in time-frequency (TF) domain have different time-supports, and their spectral contents may present continuous and stepped IF laws. We propose an IF estimation algorithm by exploiting the combination of TF distributions (TFDs) and image processing techniques. Numerical results are provided to demonstrate desirable capabilities of the proposed algorithm.

IF estimation of FM signals based on time-frequency image

A novel detection approach of linear FM (LFM) signals, with single or multiple components, in the time-frequency plane of Teager-Huang (TH) transform is presented. The detection scheme that combines TH transform and Hough transform is referred to as Teager-Huang-Hough (THH) transform. The input signal is mapped into the time-frequency plane by using TH transform followed by the application of Hough transform to recognize time-frequency components. LFM components are detected and their parameters are estimated from peaks and their locations in the Hough space. Advantages of THH transform over Hough transform of Wigner-Ville distribution (WVD) are: 1) cross-terms free detection and estimation, and 2) good time and frequency resolutions. No assumptions are made about the number of components of the LFM signals and their models. THH transform is illustrated on multicomponent LFM signals in free and noisy environments and the results compared with WVD-Hough and pseudo-WVD-Hough transforms.

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Analysis of multicomponent LFM signals by Teager Huang-Hough transform

Polynomial Fourier domain as a domain of signal sparsity

In this paper, we present a method for estimating the parameters detailing an unknown number of linear, possibly harmonically related, chirp signals, using an iterative sparse reconstruction framework. The proposed method is initiated by a re-weighted group-sparsity approach, followed by an iterative relaxation-based refining step, to allow for high-resolution estimates. Numerical simulations illustrate the achievable performance, offering a notable improvement as compared to other recent approaches. The resulting estimates are found to be statistically efficient, achieving the corresponding Cramer–Rao lower bound.

/pdf/sparse-semi-parametric-estimation-of-harmonic-chirp-signals-4oslndrmx2.pdf

Sparse Semi-Parametric Estimation of Harmonic Chirp Signals

A novel algorithm for parameter estimation of multicomponent chirp signals in complicated noise environment is proposed. By the matching pursuit (MP) algorithm, the signal is decomposed into Gabor atoms which provide sparse information that represents the signal time-frequency signature. The Hough transform (HT) is then directly used to estimate the parameter of chirp components without computing the time-frequency distribution. Simulation results show that this algorithm is capable of estimating parameters of multicomponent chirp signals even in the presence of strong intended interference and colored noise.

Hongxing Zou

Papers

Parameter Estimation of Multicomponent Chirp Signals via Sparse Representation