# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Classification Based on Tracking Optimization Method
#### 5.3.1 Noise-Driven Optimization Methods
Then the localization is completed by multi-lateration and EKF is utilized to track the moving target in a recursive manner. However, this paper does not take the energy consumption of UWSNs into consideration, which leads to the limitations for its practical applications. An adaptive KF is proposed for tracking underwater maneuvering target in [88]. The algorithm uses another KF to estimate the process noise variance of the main KF, which effectively improves the tracking accuracy and reduces the response time of the filter for maneuvering behaviors of the target. Nevertheless, the algorithm ignores the energy consumption of UWSNs and is only validated in the case of tracking the 2-D target, which is impractical for underwater target tracking. Similarly, in [60], KF is applied in a distributed architecture to estimate the position of a single target moving through UWSNs and improve energy efficiency. However, only the case of tracking 2-D targets is investigated. Based on the concept that the collective estimation is better than the single estimation, a novel tracking approach based on UKF is proposed for BOT problem in [73], which is named as integrated unscented Kalman filter (IUKF). In the tracking process, each sensor pair consisting of two sensors of the towed array provides bearing measurements and the corresponding covariance matrix which are given to the UKF to obtain a rough state estimation. Then these several rough state estimations are fused by using LSE to get the final more accurate state estimation. The simulation result demonstrates that the integration technique has better performance over the traditional one in terms of the estimation error and convergence time. Considering the complexity of PF, the authors in [78] address the BOT problem of tracking multi-target by exploiting EKF and UKF combined with PMHT, which is called PMHTe and PMHTu respectively. Both algorithms show great performance in the environment with low measurement noise while the PMHTu dominates in that with high measurement noise. _PF_: A two-stage PF-based technique is proposed for tracking multi-target based on passive tracking in [89]. Compared with the traditional PF technique sampling from only a single important density, a variation filter named the Mixture PF samples from a mixture of important densities containing the prior and the observation likelihood. To solve the problem of tracking an unknown number of multiple targets, two Mixture PFs are employed. The role of the first filter is target detection. Then the particles corresponding to the detected targets are clustered using one density-based clustering technique. The second filter tracks the detected target by taking these different clusters as the input. For the problem of tracking targets with weak signals, a tracking algorithm is proposed for underwater targets by combining PF with track-before-detect which is named as PF-TBD in [90]. The method is efficient for tracking underwater targets with low signal-to-noise ratio (SNR) by adding an existing variable to the state of particles for existence probability estimation. Different from previous works of the PF-TBD, the algorithm in this paper takes data collected by a vertical line array as the measurements, which can simplify the probability model and have better performance than traditional MFP whatever in cases with low or high SNR. There are some papers proposing PF-based tracking algorithms for WSNs [91; 92]. The two papers have common in adopting distributive PF for the sensing nodes in WSNs to track the target. Similarly, the authors in [93] propose two tracking algorithms based on a distributed PF for cluster-based UWSNs. Both run local PF sequentially at each cluster along the trajectory of the moving target. However, two tracking algorithms are different in the scheme of selecting measurements to keep a balance between the tracking accuracy and the cost including the communication cost and the energy cost. Tracking underwater targets with video sequences is a great challenge due to the complex background.