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In the Internet, network congestion is becoming an intractable problem. Congestion results in longer delay, drastic jitter and excessive packet losses. As a result, quality of service (QoS) of networks deteriorates, and then the quality of experience (QoE) perceived by end users will not be satisfied. As a powerful supplement of transport layer (i.e. TCP) congestion control, active queue management (AQM) compensates the deficiency of TCP in congestion control. In this paper, a novel adaptive traffic prediction AQM (AT-PAQM) algorithm is proposed. ATPAQM operates in two granularities. In coarse granularity, on one hand, it adopts an improved Kalman filtering model to predict traffic; on the other hand, it calculates average packet loss ratio (PLR) every prediction interval. In fine granularity, upon receiving a packet, it regulates packet dropping probability according Z. Na ( ) School to the calculated average PLR. Simulation results show that ATPAQM algorithm outperforms other algorithms in queue stability, packet loss ratio and link utilization.

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An algorithm based on back propagation neural network and particle swarm optimization is proposed to solve the direction of arrival (DOA) estimation of coherent signals received by the sensor array in colored noise environment. First, a spatial differential smoothing algorithm is adopted to eliminate colored noise and the independent signals to obtain a covariance matrix only containing the coherent sources. Then, the first line of the covariance matrix is extracted as an input characteristic parameter vector, meanwhile, the DOA of the coherent signals are taken as output. Finally, the trained back propagation neural network optimized by particle swarm algorithm is exploited to reckon the directions of coherent signals. The algorithm put forward in this paper does not require eigen-decomposition and spectral peak searching, so the computational burden is low. Theoretical analysis and simulations demonstrate that the proposed algorithm has high angular resolution and direction finding accuracy in colored noise environment.INDEX TERMS Coherent signals, back propagation neural network, colored noise, particle swarm optimization.

Knowing number of nodes is the precondition of their locating and estimation of other parameters in wireless sensor network, actually it can be evaluated according to the signal received by the sensor array. The popular algorithms are only appropriate for the uncorrelated signals and circumstance of Gaussian white noise, the estimation precision will deteriorate at some harsh environments. Hence a new algorithm for determining coherent node number based on multiple feature extraction in wireless sensor network is presented in this paper. First, perform spatial difference smoothing to the array data. Then the eigenvalues and eigenvectors can be acquired by eigen-decomposition, consequently multiple features which are used for training are constructed. Finally, back propagation neural network and particle swarm optimization are exploited for calculating node number. Simulation results demonstrate that the algorithms has good performances under the background of colored noise and small samples.

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For the sake of positioning the illegal unmanned aerial vehicle operators, the paper proposes a direction of arrival (DOA) estimation algorithm based on the reconnaissance plane with multiple array sensors. First, the number of unmanned aerial vehicle signals is determined by information theory criteria. Then combined support vector regression, the direction of the operator is calculated according to some approximating function through training. Finally, the location can be estimated by integrating the DOAs acquired with the array sensors on the reconnaissance aircraft. This algorithm is convenient and fast to be realized, moreover, as a result of adopting super resolution and multiple kernel learning, it can locate numerous radio signals simultaneously and performs well in the circumstance that signals impinge on the sensor array with small-angle interval, as well as the conditions of small samples and low signal to noise ratio, besides, the algorithm also applies to the array which gain-phase inconsistency exists among the sensors. INDEX TERMS Sensor array, radio positioning, direction of arrival, support vector regression, gain-phase inconsistency.