Abstract
In the radar array signal processing direction of arrival (DOA), the estimation of weak non-stationary signal is an important and difficult problem when both strong and weak signals are coexisting particularly because the weak non-stationary signals are often submerged in noise. In this paper, we proposed a novelty method to estimate the direction of arrival (DOA) of weak non-stationary signal in scenario for strong non-stationary interference signals and Gaussian white noise. The method utilizes spatial time-frequency distribution (STFD) of cross terms rather than suppressing cross terms in time-frequency analysis. The STFD of cross terms are introduced as an alternative matrix, which is similar to data covariance matrix in multiple signal classification (MUSIC), for the DOA estimation of a weak non-stationary signal. The cross-term amplitude of the strong and weak signals is usually above the noise and is easier to use than the auto-term of the weak signal. In the cross term, the information of the weak signal is included, and the auto-term of these weak signals is difficult to extract directly. The ability to incorporate the STFD of cross terms empowers information about a weak non-stationary signal for DOA estimation, leading to improved signal estimates for direction finding. The method based on the STFD of cross terms for DOA estimation of the weak non-stationary signal is revealed to outperform the time-frequency MUSIC and traditional MUSIC algorithm by simulation, respectively. This method has the advantages of the time-frequency direction finding method and also deals with the situation of weak signals. When the strong and weak signals exist at the same time and the two angles are similar, the cross-terms can be used to perform DOA estimation on the weak signal.
Highlights
1 Introduction Among numerous non-stationary signals that arise in many radars [1] and communication [2, 3], instantaneous frequency (IF) signals, for instance, linear frequency modulated (LFM) signals, have obvious time-frequency characteristics which are continuous and decided the location
We straightforwardly examine a kind of Cohen’s class, specially, the WignerVille distribution (WVD) as well as its characteristic
5 Conclusions When the desired weak non-stationary signal may be buried in noise, especially in the condition of low signalto-noise ratio, it is difficult or almost impossible to extract the auto-term of the weak non-stationary signal
Summary
Among numerous non-stationary signals that arise in many radars [1] and communication [2, 3], instantaneous frequency (IF) signals, for instance, linear frequency modulated (LFM) signals, have obvious time-frequency characteristics which are continuous and decided the location. WignerVille distribution (WVD) is considered as the timefrequency distribution representation since it provides the most energy concentration in time-frequency domains, displays the non-stationary properties of the signal, and satisfies the marginal conditions [11]. Combining the time-frequency and spatial characteristic is accomplished into a framework named STFDs in time-frequency MUSIC (TF-MUSIC) [12]. This structure applies the signal location characteristic and energy concentration for increasing signal-to-noise ratio (SNR) and source signal identification before achieving the highresolution direction-of-arrival estimation [13]. The framework desires the calculations of the WVD from the data obtained at each sensor, for instance, auto-terms of WVD and the cross terms of WVD between sensor antennas
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