A novel stochastic estimator using pre-processing technique for long range target tracking in heavy noise environment

Kumar, Dowluru Ravi; Rao, S. Koteswara; Raju, K. Padma

doi:10.1016/j.ijleo.2016.01.196

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…Designing a calibration method and choosing proper filter algorithms can reduce the error. The authors in [ 74 ] design a pre-processor to reduce the error of obtained bearing information, which makes a great improvement of the tracking performance. In addition, as Table 6 shows, almost every tracking algorithms use some methods for optimization.…”

Section: Discussion Challenges Countermeasures and Lessons Learmentioning

confidence: 99%

“…This is apparent from Figure 8 . A novel tracking algorithm dealing with the BOT problem is proposed in [ 74 ], which reduce tracking errors by the pre-processing process of the obtained bearings. The preprocessing technique efficiently reduces the variance of the noise present at the bearing measurements by taking an average of the present and projected the previous measurement since the natural noise is generally unbiased.…”

Section: Classification Underwater Acoustic Target Tracking Algorimentioning

confidence: 99%

“…Because of the complexity of the marine environment, the acquired measurements are usually corrupted seriously by noise, and the measuring error may result in large tracking error even employing filtering techniques. In [ 74 ], a novel tracking algorithm is proposed to address the problem of noise interference. The measuring error in bearings is removed by pre-processing progress using arithmetic average.…”

Section: Classification Underwater Acoustic Target Tracking Algorimentioning

confidence: 99%

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Underwater Acoustic Target Tracking: A Review

Luo

Han

Fan

2018

Sensors

114

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Advances in acoustic technology and instrumentation now make it possible to explore marine resources. As a significant component of ocean exploration, underwater acoustic target tracking has aroused wide attention both in military and civil fields. Due to the complexity of the marine environment, numerous techniques have been proposed to obtain better tracking performance. In this paper, we survey over 100 papers ranging from innovative papers to the state-of-the-art in this field to present underwater tracking technologies. Not only the related knowledge of acoustic tracking instrument and tracking progress is clarified in detail, but also a novel taxonomy method is proposed. In this paper, algorithms for underwater acoustic target tracking are classified based on the methods used as: (1) instrument-assisted methods; (2) mode-based methods; (3) tracking optimization methods. These algorithms are compared and analyzed in the aspect of dimensions, numbers, and maneuvering of the tracking target, which is different from other survey papers. Meanwhile, challenges, countermeasures, and lessons learned are illustrated in this paper.

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Section: Discussion Challenges Countermeasures and Lessons Learmentioning

confidence: 99%

Section: Classification Underwater Acoustic Target Tracking Algorimentioning

confidence: 99%

Section: Classification Underwater Acoustic Target Tracking Algorimentioning

confidence: 99%

See 1 more Smart Citation

Underwater Acoustic Target Tracking: A Review

Luo

Han

Fan

2018

Sensors

114

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“…In the existing literature, underwater target tracking algorithms are usually based on a Kalman filter, extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle Filter (PF). As bearing measurements are nonlinear to the target state, EKF [10,11] and PF are adopted in many bearing-only tracking cases [12][13][14][15][16][17]. However, EKF has limitations, such as track divergence and poor estimation accuracy, particularly for high initial errors [18].…”

Section: Introductionmentioning

confidence: 99%

Research on Area of Uncertainty of Underwater Moving Target Based on Stochastic Maneuvering Motion Model

Shasha

Wang

Shen

et al. 2022

Sensors

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Considering the influence of measurement error on target state estimation, there is an uncertain dispersion region for target position estimate, that is, the area of uncertainty (AOU, area of uncertainty). In underwater target tracking, the state estimation is point estimation without AOU estimation and its accuracy is poor in the early stage because of large measurement errors. Fast tracking with higher accuracy and AOU estimation are of great significance to time-sensitive target tracking. To improve the state estimation accuracy in the early stage, and estimate the AOU, a method of AOU estimation of underwater moving target is presented based on a stochastic maneuvering motion (SMM, stochastic maneuvering motion) model. The stochastic maneuvering motion model is established based on the Langevin equation to reflect the movement characteristics of an underwater moving target. Then, the target state is estimated with a noise adaptive Kalman filter by constructing the measurement equation and state equation according to measurement error characteristic and stochastic maneuvering model. Based on the physical significance of the error covariance matrix from the Kalman filter, the parameters of AOU are deduced. Simulation results of underwater target tracking and AOU estimation are presented to demonstrate the relative performance of the proposed algorithm compared with the adaptive Kalman filter. It is clearly shown from the results that SMM tracking algorithm achieves higher accuracy of state estimation in the initial stage of tracking, and the predicted AOU is consistent with the actual distribution of underwater moving targets while yielding more concentrated distribution, which reveals that estimated AOU can be precisely represented by the confidence ellipses. The presented approach and obtained results may be useful in time-sensitive target threat analysis and weapon strike applications.

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