A novel algorithm is proposed for estimating the direction of arrival (DOA) of linear frequency modulated (LFM) signals for the uniform circular array (UCA). Firstly, the UCA is transformed into an equivalent virtual uniform linear array (ULA) using the mode-space algorithm. Then, the short time Fourier transform (STFT) of each element's output is worked out. We can obtain the spatial time-frequency distribution matrix of the virtual ULA by selecting the single-source time-frequency (t-f) points in the t-f plane and then get the signal subspace of the array. The characteristics nature of the Bessel function allow us to obtain the multiple invariance (MI) of the virtual ULA. So the multiple rotational invariant equation of the array can be obtained and its closed-form solution can be worked out using the multi-least-squares (MLS) criterion. Finally, the two dimensional (2-D) DOA estimation of LFM signals for UCA can be obtained. Numerical simulation results illustrate that the UCA-STFT-MI-ESPRIT algorithm proposed in this paper can improve the estimation precision greatly compared with the traditional ESPRIT-like algorithms and has much lower computational complexity than the MUSIC-like algorithms.

Conventional radar imaging systems use Fourier transform for image formation, but due to the target's complicated motion the Doppler spectrum is time-varying and thus the reconstructed image becomes blurred even after applying standard motion compensation algorithms. Therefore, sophisticated algorithms such as polar reformatting are usually employed to produce clear images. Alternatively, Joint Time-Frequency (JTF) analysis can be used for image formation which produces clear image without using polar reformatting algorithm. In this paper, a new JTF-based method is proposed for image formation in inverse synthetic aperture radars (ISAR). This method uses minimum entropy criterion for optimum parameter adjustment of JTF algorithms. Short Time Fourier Transform (STFT) and Fractional Fourier Transform (FrFT) are applied as JTF for time-varying Doppler spectrum analysis. Both the width of Gaussian window of STFT and the order of FrFT, α, are adjusted using minimum entropy as local and total measures. Furthermore, a new statistical parameter, called normalized correlation, is defined for comparison of images reconstructed by different methods. Simulation results show that α-order FrFT with local adjustment has much better performance than the other methods in this category even in low SNR.

A blind symbol synchronization scheme for MIMO and Multi-User OFDM systems is proposed, which utilizes short-time Fourier Transformation (STFT) to obtain 2D (time and frequency) timing information from the received signals. By analyzing the obtained 2D time-frequency amplitude spectrum, intervals where no inter-symbol interference (ISI) exists are checked out for symbol synchronization, and samples during these intervals are used to carry out carrier frequency offset estimation. Theoretical analysis and simulation results show that the proposed method is more robust and provides more accurate carrier frequency offset estimation than traditional schemes.

This paper demonstrates the slope isn't an appropriate parameter to characterize a signal regarding conducted electromagnetic disturbances. On the other hand, a relevant criterion is made conspicuous: it defines the maximum slope deviation between two segments forming a signal. This criterion is validated by a signal with a maximum slope of 400 mA/µs.

A speech analysis/synthesis system that relies on a time-varying Auto Regressive Moving Average (ARMA) process and the Short-Time Fourier Transform (STFT) is proposed. The narrowband components in speech are represented in the frequency domain by a set of harmonic components, while the broadband random components are represented by a time-varying ARMA process. The time-varying ARMA model has a dual function, namely, it creates a spectral envelope that fits accurately the harmonic STFT components, and provides for the spectral representation of the broadband components of speech. The proposed model essentially combines the features of waveform coders by employing the STFT and the features of traditional vocoders by incorporating an appropriately shaped noise sequence.