Wavelet Packet Domain LMS Based Multi-User Detection

An improved wavelet packet domain least mean square （IWPD-LMS） based adaptive muhiuser detection algorithm is proposed. The algorithm employs the wavelet packet transform to rewhiten the input data, and chooses the best wavelet packet basis according to a novel convergence contribution function rather than the conventional Shannon entropy. The theoretic analyses show that the inadequacy of the eigenvalue spread of the tap-input correlation matrix is ameliorated, thus the convergence performance is improved greatly. The simulation result of convergence performance and bit error rate（BER） performance as a function of the signal power to noise power ratio（SNR） are presented finally to prove the validity of the proposed algorithm.

Extended Kalman filtering-based channel estimation for space-time coded MIMO-OFDM systems

A space-time coded multiple-input multiple-output orthogonal frequency-division multiplexing （MIMO-OFDM） system is considered as a solution to the future wideband wireless communication system. This paper proposes an extended Kalman filtering-based （EKF-based） channel estimation method for space-time coded MIMO-OFDM systems. The proposed method can exploit pilot symbols and an extended Kalman filter to estimate channel without any prior knowledge of channel statistics. In comparison with the least square （LS） and the least mean square （LMS） methods, the EKF-based approach has a better performance in theory. Computer simulations demonstrate the proposed method outperforms the LS and LMS methods. Therefore it can offer draznatic system performance improvement at a modest cost of computational complexity.

Adaptive nonuniformity correction for IRFPA sensors based on neural network framework

For infrared focal plane graded during signal acquisition array sensors, imagery is departicularly nonuniformity. In this paper, an adaptive nonuniformity correction technique is proposed which simultaneously estimates detector-level and readout- channel-level correction parameters using neural network approaches. Firstly, an improved neural network framework is designed to compute the desired output. Secondly, an adaptive learning rate rule is used in the gain and offset parameter estimation process. Experimental results show the proposed algorithm can achieve a faster convergence speed and better stability, remove nonuniformity and track parameters drift effectively, and present a good adaptability to scene changes and nonuniformity conditions.

An Improved Space-Time Joint Anti-jamming Algorithm Based on Variable Step LMS

In wireless communication, the space-time anti-jamming method is widely applied because it shows better performance than the pure airspace and pure temporal anti-jamming methods. However, its application is limited by its computational complexity, and it cannot suppress narrowband interference that is in the same direction as the navigation signal. To solve these problems, we propose improved frequency filter to filter the narrowband interference from the desired signal direction in advance, meanwhile, an improved variable step Least Mean Square （LMS） method is proposed to complete the space-time array weights with fast iteration, thereby reducing computational complexity. The simulation results show that, compared with conventional methods, the anti-jamming capability of the proposed algorithm is significantly enhanced; and its complexity is significantly reduced.

Channel modeling and influenced factor analysis of the broadband dual-orthogonal polarized MIMO land mobile satellite channel

An accurate, complete and realistic channel model is re- quired to accurately analyze the system performance of a multiple input multiple output （MIMO） broadband satellite mobile commu- nication system with dual-orthogonal polarized antennas （DPAs）. In most current studies, the channel characteristic matrix （CCM） is always formed by an independent identical distribution （i.i.d） model of Rayleigh or Rice distribution and nevertheless incomplete and inaccurate to describe a broadband dual-orthogonal polarized MIMO land mobile satellite （BDM-LMS） channel. This paper fo- cuses on establishing the BDM-LMS channel statistical model, which combines the 4-state broadband LMS channel model, the time selective fading features, the channel covariance information （CCI） channel model and polarization correlations between an- tennas. The modeling steps of the channel model are introduced. The main emphasis is placed on the effects of the factors, such as antenna numbers, temporal correlations, terminal environments, elevation angles and polarization correlations between the DPAs, on the channel capacity in the BDM-LMS system. Many simulation results are provided to illustrate the effects of these factors through comparisons of the transmit rate, ergodic capacity and outage capacity with different factor values. Besides, the MIMO outage capacity advantages, which indicate the benefits of MIMO com- pared with a single input single output （SISO） system under the same channel condition, are also studied under i.i.d or BDM-LMS channel.