Shallow Sea Environment Underwater Acoustic Channel Tracking By Multi-bernoulli Filter

Underwater Channel Tracking can be used in channel equalization for underwater acoustic communication and it can also help to explore the underwater channel environment.Traditional methods used in common wireless communication for channel equalization are usually the Least Mean Square(LMS)and the Recursive Least Mean Square(RLS)based methods.These are limited to overcome the time-varying complicated underwater acoustic channel.Hence,Autoregressive(AR)model and Subspace Tracking(ST)methods are introduced and studied in some works to help the channel tracking based on the cross-correlation of underwater acoustic channel.These methods transform the nonlinear channel parameters into some linear ones to simplify the tracking.However,these nonlinear parameters are still important and required to fully understand the underwater acoustic channel.In these work,we provide a channel tracking method to track the channel parameters directly.Underwater acoustic channel is characterized by multipath and time-varying.Hence,we track each physical path of eigen sound rays to obtain the channel parameters and the process of establishment,hiddenness and vanishment for each path based on the framework of Kalman filter.We first establish a slow time-varying multipath model for shallow sea.And fast time-vary path model is also studied including the establishment,hiddenness and vanishment.The delay and the doppler of each physical path of eigen sound rays are transmitted cooperatively during the channel state evolution,so that we can jointly track these two parameters.What's more,parameters such as the depth of the transducer and the distance between the sender and receiver,usually considered in underwater acoustic communication environment with large observation errors,are fitly avoided in this model.Then MB method is used.Even mismatch of the model we can still track each path.We can estimate the number of the paths along with the state of each one at every sampling instant and track each confirmed path from the past to the present.Hence,a visualized evolution of the multipath in the communication environment can be achieved by analyzing all the tracking results related to a success judgement for establishment,hiddenness or vanishment of a path.Other than traditional ones,MB method gets rid of too complicated data association and multihypothesis and it also appears a ”no spooky effect” outbalance a Cardinalized Probability Hypothesis Density(CPHD)filter under the same Random Finite Set(RFS)frame when tracking underwater acoustic channels.Last but not the least,for applicable measurements,we need to extract them from channel estimation.Compressive sensing(CS)providing a better performance than conventional Least Square(LS)methods is considered into this research and it will potentially reduce bit error rates(BER)cooperated with filtering.Two scenes,a simulated Orthogonal Frequency Division Multiplexing(OFDM)system and a part of MACE10 are referred to test the tracking performance.A simple method is put forward to solve the problem of unknown of multipath in real tracking experiment environment about channel estimate parameter adjustment by tracking feedback at last.The results from the simulation and experiment show that: our channel model design can match the channel characteristics in shallow sea environment under certain conditions.Our tracking method can accurately track channel parameters,estimate the number of multipath and analyze the establishment,hiddenness and vanishment of each path.Our measurement method can provide effective measurement both in simulation and experiment.