Abstract
The Mills Cross sonar sensor array, achieved by the virtual element technology, is one way to build a low-complexity and low-cost imaging system while not decreasing the imaging quality. This type of sensor array is widely investigated and applied in sensor imaging. However, the Mills Cross array still holds some redundancy in sensor spatial sampling, and it means that this sensor array may be further thinned. For this reason, the Almost Different Sets (ADS) method is proposed to further thin the Mills Cross array. First, the original Mills Cross array is divided into several transversal linear arrays and one longitudinal linear array. Secondly, the Peak Side Lobe Level (PSLL) of each virtual linear array is estimated in advance. After the ADS parameters are matched according to the thinned ratio of the expectant array, all linear arrays are thinned in order. In the end, the element locations in the thinned linear array are used to determine which elements are kept or discarded from the original Mills array. Simulations demonstrate that the ADS method can be used to thin the Mills array and to further decrease the complexity of the imaging system while retaining beam performance.
Highlights
Sonar imaging is widely used for underwater detection applications, such as military defense, engineering maintenance, accident rescue, topographic survey, and so on [1]
One can observe that the azimuth resolution of the thinned array using Almost Different Sets (ADS) is superior to that using Cyclic Difference Set (CDS), the CDS method can produce a better Peak Side Lobe Level (PSLL) of the thinned array
Main max min range f frequency) kHz, frequency step f 0 = 300 kHz, signal frequency f m in m ax lobe width (MLW) and PSLL are adopted as performance indices
Summary
Sonar imaging is widely used for underwater detection applications, such as military defense, engineering maintenance, accident rescue, topographic survey, and so on [1]. Yan Tao employed an improved genetic algorithm (GA) to obtain 2D sparse planar arrays, in which he established an optimization model with the goal of minimizing the peak side lobe level (PSLL) This method provides a specific sparse ratio of the array elements, and maintains the array performance [5]. The obtained sparse array can greatly bring down the hardware cost and complexity of the imaging system [9] This scenario uses the Cyclic Difference Set (CDS) to perform sparse optimization on Multiple Inputs and Multiple Outputs (MIMO) arrays. The ADS method possesses many advantages, such as obtaining a prior estimation on the side-lobe level of the array beam power pattern, enhancing the using efficiency of array element, fitting the large size array, and avoiding the binary property of the autocorrelation function. The purpose is to further reduce the number of array elements, the manufacturing cost, and the system complexity when using the cross array sensor while maintaining beam pattern and imaging quality
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