Gapped-data Amplitude And Phase Estimation Research Articles

ISAR imaging of manoeuvring target with gapped data using chirp‐GAPES

In inverse synthetic aperture radar (ISAR) imaging, the synthetic aperture is gapped where the echoes during some periods are not valid caused by various reasons such as interference or jamming. Usually, the gapped-data amplitude and phase estimation (GAPES) is implemented to estimate the amplitude and phase of a sinusoidal signal. However, for the reason of the non-cooperative motion of manoeuvring targets, the Doppler frequencies are usually time-varying, and hence the echoes become chirps. As a result, the GAPES technique fails because of the spreading effects due to the time-varying frequency of each scatterer contribution. In this Letter, a novel algorithm which extended the GAPES algorithm is proposed, for the ISAR imaging of manoeuvring targets with gapped data. Finally, the measured data of Yake-42 is utilised to verify the computational effectiveness of the presented method.

Method to suppress transient interference in skywave OTHR

In skywave over-the-horizon radar (OTHR), transient interference is always suppressed with steps of interference localisation, corruption segment blanking and data restoration. The gapped data amplitude and phase estimation (GAPES) method has been proved to achieve good restoration performance. However, the large computational cost limits its application. A computationally efficient method for transient interference suppression is proposed. Quadratic smoothing is used to suppress transient interference instead of blanking the contaminated data. After that, the GAPES method only needs to restore the smoothed target echoes. Since quadratic smoothing can preserve most clutter echoes, the GAPES method will converge much faster. Experimental results demonstrated the effectiveness of the proposed method.

High-Resolution Radar Imaging of Air Targets From Sparse Azimuth Data

This paper derives the signal model for radar imaging of air targets from sparse azimuth data. Then, the sparsity of two data-missing patterns, i.e., the gapped data and random missing data, is studied following the theory of sparse signal representation. The missing samples are grouped together with continuous data segments in the former pattern, while they are placed randomly with a uniform distribution in the latter one. After that, a practical procedure for imaging from sparse azimuth data is proposed. In this procedure, a new method is introduced for gapped-data range alignment. Then, different imaging methods are chosen according to the mutual coherence of the over complete basis. For a small mutual coherence, the imaging method founded on basis pursuit (BP) is proposed. For the gapped data with a large mutual coherence, the gapped-data amplitude and phase estimation (GAPES) is applied to azimuth imaging. Finally, imaging results of measured sparse azimuth data have proved the effectiveness of the proposed method.

Audio Packet Loss Concealment in a Combined MDCT-MDST Domain

Audio streaming applications have become very popular in recent years, owing to their low cost and convenience. However, during network congestions, data packets are often delayed or discarded, creating an annoying gap in the streamed media. This letter presents a new approach to audio packet loss concealment designed for MPEG-audio streaming applications. In a previous work, we introduced a receiver-based concealment algorithm based on applying the gapped-data amplitude and phase estimation (GAPES) interpolation algorithm in the discrete short-time Fourier transform (DSTFT) complex domain and obtained better results compared to past methods. The current approach applies the same algorithm on a different complex domain, formed from combining the modified discrete cosine transform (MDCT) domain as its real part and the modified discrete sine transform (MDST) domain as the imaginary part. The new approach significantly reduces the complexity demands while maintaining similar high-quality results.

Amplitude spectrum estimation for two-dimensional gapped data

The amplitude and phase estimation (APES) approach to nonparametric spectrum estimation of uniformly sampled data has received considerable interest. We consider the extension of APES to gapped data, i.e., uniformly sampled data with missing samples. It has been shown that the APES estimate of the spectrum is the minimizer of a certain least-squares (LS) criterion, and our extension of APES is based on minimizing this LS criterion with respect to the missing data as well. A computationally efficient method for doing this based on cyclic minimization and the conjugate gradient algorithm is proposed. The new algorithm is called gapped-data APES (GAPES) and is developed for the two-dimensional (2-D) case, with the one-dimensional (1-D) case as a special instance. Numerical examples are provided to demonstrate the performance of the algorithm and to show the advantages of 2-D data processing over 1-D (row or column-wise) data processing, as well as to show the applicability of the algorithm to synthetic aperture radar (SAR) imaging.

High-resolution SAR imaging with angular diversity

We propose to use the APES (amplitude and phase estimation) approach for the spectral estimation of gapped data and synthetic aperture radar (SAR) imaging with angular diversity. A relaxation-based algorithm, referred to as GAPES (Gapped-data APES), is proposed, which includes estimating the spectrum via APES and filling in the gaps via a least squares (LS) fitting. For SAR imaging with angular diversity data fusion, we perform one-dimensional (1-D) windowed fast Fourier transforms (FFTs) in range, use the GAPES algorithm to interpolate the gaps in the aperture for each range, apply 1-D inverse FFTs (IFFTs) and dewindow in range, and finally apply the two-dimensional (2-D) APES algorithm to the interpolated matrix to obtain the 2-D SAR image. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

Nonparametric spectral analysis of gapped data via an adaptive filtering approach

We present an algorithm for nonparametric complex spectral analysis of gapped data via an adaptive finite impulse response (FIR) filtering approach, referred to as the gapped-data amplitude and phase estimation (GAPES) algorithm. The incomplete data sequence may contain gaps of various sizes. The GAPES algorithm iterates the following two steps: (1) estimating the adaptive FIR filter and the corresponding complex spectrum via amplitude and phase estimation (APES), a nonparametric adaptive FIR filtering approach, and (2) filling in the gaps via a least-squares APES fitting criterion. The initial condition for the iteration is obtained from the available data segments via APES. Numerical results are presented to demonstrate the effectiveness of the proposed GAPES algorithm.