Achieving sound communication systems in Under Water Acoustic(UWA)environment remains challenging for researchers.The communication scheme is complex since these acoustic channels exhibit uneven characteristics such a...Achieving sound communication systems in Under Water Acoustic(UWA)environment remains challenging for researchers.The communication scheme is complex since these acoustic channels exhibit uneven characteristics such as long propagation delay and irregular Doppler shifts.The development of machine and deep learning algorithms has reduced the burden of achieving reli-able and good communication schemes in the underwater acoustic environment.This paper proposes a novel intelligent selection method between the different modulation schemes such as Code Division Multiple Access(CDMA),Time Divi-sion Multiple Access(TDMA),and Orthogonal Frequency Division Multiplexing(OFDM)techniques using the hybrid combination of the convolutional neural net-works(CNN)and ensemble single feedforward layers(SFL).The convolutional neural networks are used for channel feature extraction,and boosted ensembled feedforward layers are used for modulation selection based on the CNN outputs.The extensive experimentation is carried out and compared with other hybrid learning models and conventional methods.Simulation results demonstrate that the performance of the proposed hybrid learning model has achieved nearly 98%accuracy and a 30%increase in BER performance which outperformed the other learning models in achieving the communication schemes under dynamic underwater environments.展开更多
In radar systems,target tracking errors are mainly from motion models and nonlinear measurements.When we evaluate a tracking algorithm,its tracking accuracy is the main criterion.To improve the tracking accuracy,in th...In radar systems,target tracking errors are mainly from motion models and nonlinear measurements.When we evaluate a tracking algorithm,its tracking accuracy is the main criterion.To improve the tracking accuracy,in this paper we formulate the tracking problem into a regression model from measurements to target states.A tracking algorithm based on a modified deep feedforward neural network(MDFNN)is then proposed.In MDFNN,a filter layer is introduced to describe the temporal sequence relationship of the input measurement sequence,and the optimal measurement sequence size is analyzed.Simulations and field experimental data of the passive radar show that the accuracy of the proposed algorithm is better than those of extended Kalman filter(EKF),unscented Kalman filter(UKF),and recurrent neural network(RNN)based tracking methods under the considered scenarios.展开更多
Let SFd and ∏φ,n,d ={∑j^n=1bjφ（wj.x＋θj）：bj,θj∈R,wj∈R^d} be the set of periodic and Lebesgue＇s square-integrable functions and the set of feedforward neural network （FNN） functions, respectively. Denote ...Let SFd and ∏φ,n,d ={∑j^n=1bjφ（wj.x＋θj）：bj,θj∈R,wj∈R^d} be the set of periodic and Lebesgue＇s square-integrable functions and the set of feedforward neural network （FNN） functions, respectively. Denote by dist （SFd , ∏φ,n,d） the deviation of the set SFd from the set ∏φ,n,d. A main purpose of this paper is to estimate the deviation. In particular, based on the Fourier transforms and the theory of approximation, a lower estimation for dist （SFd and ∏φ,n,d） is proved. That is, dist（SFd and ∏φ,n,d） ≥C/（nlog2n）1/2. The obtained estimation depends only on the number of neuron in the hidden layer, and is independent of the approximated target functions and dimensional number of input. This estimation also reveals the relationship between the approximation rate of FNNs and the topology structure of hidden layer.展开更多
文摘Achieving sound communication systems in Under Water Acoustic(UWA)environment remains challenging for researchers.The communication scheme is complex since these acoustic channels exhibit uneven characteristics such as long propagation delay and irregular Doppler shifts.The development of machine and deep learning algorithms has reduced the burden of achieving reli-able and good communication schemes in the underwater acoustic environment.This paper proposes a novel intelligent selection method between the different modulation schemes such as Code Division Multiple Access(CDMA),Time Divi-sion Multiple Access(TDMA),and Orthogonal Frequency Division Multiplexing(OFDM)techniques using the hybrid combination of the convolutional neural net-works(CNN)and ensemble single feedforward layers(SFL).The convolutional neural networks are used for channel feature extraction,and boosted ensembled feedforward layers are used for modulation selection based on the CNN outputs.The extensive experimentation is carried out and compared with other hybrid learning models and conventional methods.Simulation results demonstrate that the performance of the proposed hybrid learning model has achieved nearly 98%accuracy and a 30%increase in BER performance which outperformed the other learning models in achieving the communication schemes under dynamic underwater environments.
基金Project supported by the National Natural Science Foundation of China(Nos.61931015,62071335,and 61831009)the Natural Science Foundation of Hubei Province,China(No.2021CFA002)。
文摘In radar systems,target tracking errors are mainly from motion models and nonlinear measurements.When we evaluate a tracking algorithm,its tracking accuracy is the main criterion.To improve the tracking accuracy,in this paper we formulate the tracking problem into a regression model from measurements to target states.A tracking algorithm based on a modified deep feedforward neural network(MDFNN)is then proposed.In MDFNN,a filter layer is introduced to describe the temporal sequence relationship of the input measurement sequence,and the optimal measurement sequence size is analyzed.Simulations and field experimental data of the passive radar show that the accuracy of the proposed algorithm is better than those of extended Kalman filter(EKF),unscented Kalman filter(UKF),and recurrent neural network(RNN)based tracking methods under the considered scenarios.
基金Supported by the National Natural Science Foundation of China (Grant No. 60873206)the National Basic Research Program of China(Grant No. 2007CB311002)
文摘Let SFd and ∏φ,n,d ={∑j^n=1bjφ（wj.x＋θj）：bj,θj∈R,wj∈R^d} be the set of periodic and Lebesgue＇s square-integrable functions and the set of feedforward neural network （FNN） functions, respectively. Denote by dist （SFd , ∏φ,n,d） the deviation of the set SFd from the set ∏φ,n,d. A main purpose of this paper is to estimate the deviation. In particular, based on the Fourier transforms and the theory of approximation, a lower estimation for dist （SFd and ∏φ,n,d） is proved. That is, dist（SFd and ∏φ,n,d） ≥C/（nlog2n）1/2. The obtained estimation depends only on the number of neuron in the hidden layer, and is independent of the approximated target functions and dimensional number of input. This estimation also reveals the relationship between the approximation rate of FNNs and the topology structure of hidden layer.
