An Improved Imaging Algorithm for Multi-Receiver SAS System with Wide-Bandwidth Signal

Abstract

When the multi-receiver synthetic aperture sonar (SAS) works with a wide-bandwidth signal, the performance of the range-Doppler (R-D) algorithm is seriously affected by two approximation errors, i.e., point target reference spectrum (PTRS) error and residual quadratic coupling error. The former is generated by approximating the PTRS with the second-order term in terms of the instantaneous frequency. The latter is caused by neglecting the cross-track variance of secondary range compression (SRC). In order to improve the imaging performance in the case of wide-bandwidth signals, an improved R-D algorithm is proposed in this paper. With our method, the multi-receiver SAS data is first preprocessed based on the phase center approximation (PCA) method, and the monostatic equivalent data are obtained. Then several sub-blocks are generated in the cross-track dimension. Within each sub-block, the PTRS error and residual quadratic coupling error based on the center range of each sub-block are compensated. After this operation, all sub-blocks are coerced into a new signal, which is free of both approximation errors. Consequently, this new data is used as the input of the traditional R-D algorithm. The processing results of simulated data and real data show that the traditional R-D algorithm is just suitable for an SAS system with a narrow-bandwidth signal. The imaging performance would be seriously distorted when it is applied to an SAS system with a wide-bandwidth signal. Based on the presented method, the SAS data in both cases can be well processed. The imaging performance of the presented method is nearly identical to that of the back-projection (BP) algorithm.

Azimuth-Invariant Motion Compensation and Imaging Chirp Scaling Algorithm for Multiple-Receiver Synthetic Aperture Sonar

“…Point P , i.e., (0, , ) nn yz , is an arbitrary point target in the imaging scene. In the ideal situation, the coordinates [14][15][16][17][18] of the transmitter can be represented by ( ,0,0) vt In practice, owing to the influence of the six-DOF motion errors, the positions of the transmitter and receivers during signal transmission and reception change. It is difficult to obtain the analytical expressions of the six-DOF motion errors with respect to the azimuth time t because of their random variation laws.…”

“…These errors are referred to as the surge, sway, heave, roll, pitch, and yaw, which are collectively called the six-degree of freedom (DOF) motion errors [11][12][13]. Conventional SAS imaging algorithms [14][15][16][17][18] are mostly based on an ideal motion model and do not consider motion errors and MOCO operation, which can make obtaining optimal two-dimensional (2D) SAS images difficult.…”

Effects and Prospects of the Vibration Isolation Methods for an Atomic Interference Gravimeter

“…[1][2][3]. Today, high-precision gravity field maps, like remote sensing satellite images, SAR images and other satellite images, play an important role in the field of national economy and people's livelihood [4][5][6][7]. In the underwater navigation of submarines, the traditional sonar technology is unable to meet the requirements for high precision navigation at the seabed since it cannot receive any signal in deep waters, but a seabed high precision gravity map can be used to assist submarines in rapidly locating and avoiding the obstacles at seabed [1,8,9].…”

A Viterbi Decoder under Class A Modeled Noise in Shallow Water

“…The noise distribution [1,2] plays an important role in developing underwater signal processors. Traditional signal processors such as underwater localization [3][4][5][6][7][8][9][10][11][12][13], underwater tracking [9,14,15], sonar imaging [16][17][18][19][20][21][22][23][24][25][26][27], direction of arrival (DOA) estimation [28][29][30][31][32], and underwater acoustic communication (UAC) are mostly based on Gaussian noise, which can be supported by a central limit theorem. Besides, the Gaussian model is just determined by the first-order and second-order statistics [33].…”