# An Improved Kernelized Correlation Filter Algorithm for Underwater Target Tracking
## 1 Introduction  
With the rapid development of the world economy, the strategic position of the ocean has become more and more important [1]. Forward-looking sonar is a device that detects oceans by sound waves. It is mainly used for underwater target location, underwater target tracking, and obstacle avoidance [2]. In particular, underwater target tracking in forward-looking sonar image sequences has gradually attracted more global attention. Therefore, numerous approaches have recently been proposed for providing exceptional underwater target tracking performance. At present, the underwater target tracking methods are mainly the extension of optical image target tracking methods. Among them, mean-shift [3], Kalman filter (KF) [4], and particle filter (PF) [5] are the most commonly used. [PERSON]. [PERSON] et al. [6] introduced the optical flow motion estimation to track the target in sonar image sequences. The method is shown to work well, with a good tracking performance when objects merge, split, and change shape. In order to solve the problem of large computations of the optical flow motion estimation tracking method, [PERSON] et al. [7] improved the target's alternating problem and occlusion problem using the combination of mean-shift and improved KF, through which the accuracy of target tracking is guaranteed. For underwater target tracking, [PERSON] et al. [8] introduced the KF for multitarget tracking in forward-looking sonar image sequences; this algorithm was designed for still target lying on a flat seafloor. On this basis, a framework using navigation data for performing robust multitarget tracking based on the KF is proposed to track an obstacle in a series of sonar images [9], but this work cannot be used for the tracking of moving obstacles. In addition, [PERSON] et al. [10] introduced the PF to track the target in forward-looking sonar image sequences. The experimental results show that the PF has robustness in target tracking. Later, [PERSON] et al. [11] adopted an improved Otsu method to detect the underwater target, and then carried outunderwater target tracking through the PF algorithm combined with a multifeature adaptive fusion strategy. However, the PF suffers from a high computation cost, because it requires a larger number of particles to represent the posterior density of the object state. Although there are many improved algorithms, most of them are still based on the above algorithms. There are some limitations in the improvement of these algorithms. Correlation filter tracking as a discriminative algorithm [12] is beginning to be noticed, due to its robustness and flexibility. Correlation filter tracking as a discriminative algorithm was first used for target tracking by [PERSON] in 2010 [13]. The proposed minimum output sum of squared error (MOSSE) filter first uses the target region as a positive sample in the current frame and the background region as a negative sample. The classifier is trained by a machine learning method, then the next frame with a trained classifier to find the best region as the target area. Among the discriminative tracking algorithms, the correlation filter tracking algorithm is one of the outstanding representatives. Based on the correlation filter tracking algorithm, a series of improved algorithms have been proposed. [PERSON] et al. [14] used a circulant structure of tracking-by-detection with kernels (CSK) to detect the correlation of adjacent frames and transform to the frequency domain to accelerate the operation, which can solve the problem of increasing the calculational amount caused by too many training samples of the filter. In addition, [PERSON] [15] introduced a correlation filter based on a color feature template by studying the contribution of color in the detection and tracking framework. The results showed that the color features provide excellent performance in tracking. In 2015, [PERSON] et al.