Shape self-calibration of a highly deformed sonar array by adding heading sensors

Flexible arrays exhibit excellent detection performance due to their large aperture. However, flexible arrays often undergo significant shape deformation, which will negatively affect the results of array processing. To address this issue, an array shape self-calibration method utilizing both acoustic and non-acoustic data for a highly deformed array is proposed in this paper. Based on the positions of heading sensors uniformly distributed on the sensor array, the whole array is partitioned into several subarrays. The array steering matrix for each subarray is derived using the constant modulus algorithm, and phase differences between adjacent sensors are computed from the columns of the estimated steering matrix. In a manner similar to the partitioned eigenvector method (PEM), sensor positions can be estimated using the heading angles measured by the heading sensors, without requiring the signal directions. Moreover, the heading sensor data can also be employed to resolve the phase ambiguity caused by significant shape deformations and multiple possible values of sensor positions caused by the port or starboard uncertainty. The statistical property of the proposed method is analyzed in simulations. Additionally, the shallow water evaluation cell experiment 1996 data set is applied to confirm the effectiveness of the proposed approach.