Improved Cubature Kalman Filter for Target Tracking in Underwater Wireless Sensor Networks

EasyChair Preprint 3556

Abstract

The underwater sensor network is currently a hot research field in academia and industry with many underwater applications, such as ocean monitoring, seismic monitoring, environment monitoring, and seabed exploration. Underwater target tracking is a critical component of ocean development. This paper studies the underwater target tracking problem of the wireless sensor network. The core technology of the target tracking algorithm is the filtering algorithm, which identifies the accuracy of the target tracking system. Nonlinear filtering is a hot issue in target tracking because feasible projects are mostly non-linear systems. The linearization method used in traditional Kalman filtering has serious shortcomings. Therefore, this paper presents the improved cubature Kalman filtering (ICKF) algorithm for underwater target tracking. There is uncertainty in the target movement, an adaptive forgetting factor is given into the cubature Kalman filtering algorithm to directly modify the error covariance to reduce the impact of uncertainties. Then, interactive multi-model technology is introduced to establish the IMMICKF algorithm with multiple states. Compared with other filtering algorithms, the new algorithm can effectively deal with non-linear target tracking problems and obtain better estimation accuracy. The numerical simulation is given to demonstrate the effectiveness of the IMMICKF algorithm.

Keyphrases: Adaptive Forgetting Factor, Underwater Sensor Network, cubature Kalman filtering, interacting multiple model, target tracking

@booklet{EasyChair:3556,
  author    = {Junhai Luo and Yanping Chen and Zhiyan Wang and Man Wu and Yang Yang},
  title     = {Improved Cubature Kalman Filter for Target  Tracking in Underwater Wireless Sensor Networks},
  howpublished = {EasyChair Preprint 3556},
  year      = {EasyChair, 2020}}