To use monostatic based imaging algorithms for multireceiver synthetic aperture sonar, the monostatic conversion is often carried out based on phase centre approximation, which is widely exploited by multireceiver SAS systems. This paper presents a novel aspect for dealing with the multireceiver SAS imagery, which still depends on the idea of monostatic conversion. The approach in this paper is based on Loffeld's bistatic formula that consists of two important terms, i.e., quasi monostatic and bistatic deformation terms. Our basic idea is to preprocess the bistatic deformation term and then incorporate the quasi monostatic term into an analogous monostatic spectrum. With this new spectrum, traditional imaging algorithms designed for monostatic synthetic aperture sonar can be easily exploited. In this paper, we show that Loffeld's bistatic formula can be reduced to the same formula as spectrum based on phase centre approximation when certain conditions are met. Based on our error analysis, the maximum error magnitude of PCA method is about 1 rad, which would noticeably affect the SAS imagery. Fortunately, the error magnitude of presented method can be always kept within π 4 . It means that Loffeld's bistatic formula provides a more accurate approximation of the spectrum compared to that based on phase centre approximation. After that, this paper develops a new imaging scheme and presents imaging results. Based on quantitative comparisons, the presented method well focuses multireceiver SAS data, and it provides better image compared to phase centre approximation method.

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A Gaussian distribution is used by all traditional underwater acoustic signal processors, thus neglecting the impulsive property of ocean ambient noise in shallow waters. Undoubtedly, signal processors designed with a Gaussian model are sub-optimal in the presence of non-Gaussian noise. To solve this problem, firstly a quantile-quantile (Q-Q) plot of real data was analyzed, which further showed the necessity of investigating a non-Gaussian noise model. A Middleton Class A noise model considering impulsive noise was used to model non-Gaussian noise in shallow waters. After that, parameter estimation for the Class A model was carried out with the characteristic function. Lastly, the effectiveness of the method proposed in this paper was verified by using simulated data and real data.Modeling non-Gaussian noise in water is much slower than in an electromagnetic environment. Traditional noise analysis methods mainly include signal self-correlation, power spectrum estimation, short-time Fourier transform, Wigner-Ville analysis, wavelet analysis, and so on. Second-order statistical characteristics are used by these methods, indirectly adopting a Gaussian model based on Liu's work in [6]. In [7], high-order statistical characteristics including high-order moment and accumulation spectra are employed by Li. Unfortunately, high-order moment and accumulation are usually very complicated, requiring large calculation. Fourth-order moment and accumulation are exploited in practice. Obviously, non-Gaussian noise cannot be completely described. There are many other distributions to model non-Gaussian noise, such as Stein's Gaussian mixture in [8], the Laplacian model developed by Miller and Thomas in [9], and symmetric alpha-stable distribution (SαS) developed by Nikias and Shao in [10]. A Gaussian-Laplacian mixture model cannot truly depict the heavy-tailed characteristic of impulsive noise. The SαS model does not possess finite second-order moment or the closed form of a probability density function. Besides, none of these noise models possess a strong physical or theoretical justification.In this research, firstly, a quantile-quantile (Q-Q) plot was used to analyze real data. It showed the necessity of investigating a non-Gaussian noise model. Then, ocean ambient noise based on a Class A model was studied. With the characteristic function, the parameters of the Class A model were estimated. Lastly, the processed results of a simulation and real data were used to verify the proposed method.

Traditional chirp scaling algorithm is often developed based on the quadratic approximation of point target reference spectrum, which neglects the higher-order coupling between range and azimuth dimensions. Besides, the scaling operation is conducted based on quadratic term of reference range, and the residual quadratic error increases with target range far away from reference range. The analysis of phase error shows that both errors are noticeable under the case of wide-bandwidth signal, and the imaging performance would be seriously distorted. To solve this problem, the higher-order coupling and quadratic error are compensated by exploiting sub-block processing method in the range dimension when the chirp scaling algorithm is exploited to reconstruct targets. Compared to traditional methods, the presented method significantly improves the focussing performance based on the simulation results. The processing of real data further shows that the performance of presented method is superior to that of traditional method.