# 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  
The filter algorithm plays an important role in the tracking process for reducing the noise interference. In most tracking problems, the target is usually described by the state space model which includes the state and observation equation. Assuming \(Z^{k}=\{z_{1},z_{2},\ldots,z_{k}\}\) is all measurements up to time \(k\). The estimation of \(x_{k}\) is called state filter, and the estimation of \(x_{k+l},l>0\) is named as state prediction. In this part, we classify the algorithms based on using different filters. In the existing literature, underwater target tracking algorithms are usually based on KF, EKF, UKF, and PF. Because EKF and UKF are variations of the KF for nonlinear problems, we classify the tracking algorithms into that based on KF and PF. Compared to terrestrial tracking, underwater target tracking is in face of more serious noise interference. Therefore, some data pre-processing techniques such as arithmetic average and Sage-Husa model is required. Filtering Techniques  
_KF_: It is a recursive estimation method based on the state space model, which is aimed at producing estimates with high accuracy by a set of low-level and redundant measurements. However, KF is only suitable for tracking the target with linear motion and moving in the environment white Gaussian noise. Therefore, EKF and UKF, variations of KF, are developed for solving the nonlinear problem. The algorithm named 3 DUTT is employed to determine the location of the target with the trilateration in [82]. However, due to the lack of a prediction step, the tracking accuracy is not satisfactory. Inspired by this, the authors in [84] propose an adaptive underwater target tracking algorithm based on KF for UWSNs, where the output of trilateration is inserted into KF as the measuring model to produce final results with high accuracy. Since bearing measurements have a nonlinear relationship with the state of the target, vast literature considers adopting EKF and PF in some bearing-only tracking cases [84; 85; 86]. However, in practice, underwater object maneuvering can be described as a simple linear model because the angle changes little during the sampling interval for the limited speed of the target compared with the long distance between objects and the receivers. Based on this concept, the authors in [77] use KF, which is an optimal estimation algorithm with lower computation complexity compared with EKF, UKF, and PF, to track the underwater target. The existing range-based tracking algorithms usually locate the target by the distance to some sensing nodes, and the distance is obtained by multiplying the speed of the acoustic wave \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **Reference** & **Precision** & **Tracking Technology** & \
\begin{tabular}{c} **Measurements** \\ **Received** \\ \end{tabular} \\ \hline \multirow{6}{*}{Passive} & [37] & High & ADCSP, data fusion, PF & TDOA \\  & [56] & Medium & MEGEKF/UKF, data fusion & DOA \\  & [73] & Medium & IUKF, consensus estimation & DOA \\  & [74] & High & Pre-processing of noise, UKF, IUKF & DOA \\  & [75] & High & EKF & DOA, TDOA \\  & [77] & High & KF, AR, SD-CFAR & DOA \\  & [78] & High & EM, EKF, UKF & DOA \\  & [80] & Medium & EKF & TDOA \\ \hline \multirow{6}{*}{Active} & [38] & Medium &
\begin{tabular}{c} Control strategy for AUV \\ A branch and bound technique \\ \end{tabular} & DOA, TOA \\  & [81] & High & FCM, KF & RSSI \\  & [82] & Medium & 3 DUT, BND & TDOA \\  & [83] & High & CEUTT & TDOA \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of tracking mode-based methods. by the traveling time. However, the sound speed is not constant in underwater environments, which results in the range-based tracking algorithms having poor tracking performance. To address the aforementioned problem, an isogradient sound speed profile (SSP) is adopted in [87] to formulate the range-based localization problem with the time-based problem.