Space-borne bistatic radar has stronger viability than monostatic radar. And larger radar cross section (RCS) makes anti-stealth capability of space-borne bistatic radar stronger. However, space-borne bistatic radar is also in down-looking working state, which will suffer serious clutter interference. Since the transmitter and receiver are not placed on the same platforms, bistatic configurations cause clutter characteristics more complicated. It is mainly manifested in resolution spatial variation and tanglesome space-time distribution. In this paper, we first establish the signal model of spaceborne bistatic radar based on satellite-earth relation. Then, we focus on arbitrary orbital plane configuration to study resolution spatial variation and space-time distribution characteristics against bistatic geometric relationships. Last, we adopt the full link evaluation model to evaluate the performance of space-time adaptive processing (STAP) under corresponding bistatic geometric relationships. This paper can provide significant references for system design of space-borne bistatic radar.
Space-time adaptive processing (STAP) can effectively suppress the clutter, which plays an important role in ground moving target indication (GMTI). However, it is difficult to obtain sufficient training samples with an increase in the number of spatial channels and adaptive processor dimensions in large arrays, especially in a complex geomagnetic detection environment. Traditional reduced-dimension STAP methods cannot offer significant benefits in real data processing in this issue. Thus, in this paper, a reduced-dimension post-Doppler STAP method based on tensor Tucker decomposition is proposed. Firstly, the distribution characteristics of the clutter spectrum in the post-Doppler domain are analyzed. Then, the feature spaces of beam and Doppler are extracted by tensor Tucker decomposition. Finally, the data dimension is reduced by the feature spaces, and clutter suppression is carried out. The results of the experiments based on real measured data demonstrate that the proposed method can achieve good performance with fewer samples than traditional methods.
KEYWORDS: Signal processing, Signal to noise ratio, Feature extraction, Radar, Synthetic aperture radar, Signal detection, Radar signal processing, Filtering (signal processing), Time-frequency analysis
The fluent ship targets with micro-motion which is caused by oceanic waves leading to defocused images. Due to the large size ship, there is a multi-component echo signal in one range bin, thus it is crucial to extract the micro-Doppler (m-D) features quickly and precisely to refocus the images. This paper puts forward a novel micro-motion feature extraction and estimation method. The method is composed of two steps, and the first step is preprocessing to do the Short-Time Fourier Transform (STFT). After that, we propose a new form of synchrosqueezing transform to concentrate the energy spread curves which can be established as a state translation model. Then in the second step, we use the RFS-based Bernoulli filter to estimate the parameters of the multi-component signal. In this step, the method avoids the disturbance of stray points and empty areas so that the m-D parameters can be estimated accurately. The experimental results prove the availability of the proposed method and the accuracy of the estimation of m-D parameters.
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