一种基于自适应阵列天线的波束赋形算法

自适应阵列天线中的数字波束赋形(DBF)技术是智能天线数字信号处理部分的核心.提出了一种可用于自适应阵列波束赋形的SMI-LMS算法--由SMI(采样协方差矩阵求逆)算法决定LMS(最小均方)算法的初始权向量.该算法充分结合了SMI算法收敛速度快和LMS算法稳态误差小的优点,能在较强干扰环境下,确保权向量的快速收敛和跟踪速度.与传统的LMS算法相比,SMI-LMS算法具有良好的收敛性能、较快的跟踪速度和较小的输出误差,并可以有效改善自适应方向图的副瓣性能.仿真结果验证了该结论.     

Mitigation of Multipath Effects Based on a Robust Fractional Order Bidirectional Least Mean Square (FOBLMS) Beamforming Algorithm for GPS Receivers

Consideration on positioning and location services among the public has been increasing in the recent years with their applications in most of the anticipating milieus such as automobile navigation system etc. This insists for a development of high recitation global navigation satellite system such as global positioning system (GPS). Multipath effects, interference, signal jamming etc. are the major sources of error influencing the performance of the GPS receiver. Literature presents many of the multipath mitigation techniques. Among them, adaptive processing technology based beamforming algorithms appears a viable solution for multipath mitigation. The least mean square (LMS) beamforming algorithms were sensitive to dynamic environments thus affecting the accuracy of GPS. In this paper, an adaptive beamforming algorithm called fractional order bidirectional least mean square (FOBLMS) algorithm is proposed to mitigate the multipath effects and to conceal the jammer signal in a GPS receiver. The FOBLMS is an integration of the fractional calculus and bidirectional least mean square algorithm. The effectiveness of the proposed algorithm is validated using the bit error rate and experimentation gain results over the existing beamforming algorithms. Experimental results demonstrated that the performance of the proposed beamforming algorithm is better than LMS algorithm with maximal relative antenna gain of 28.92 dB, 32.84 dB for two and four element antenna arrays at ??60° and 10°, direction of arrivals respectively. The outcome of this work would be useful for developing a robust technique for multipath mitigation in GPS receivers.

     

Combined RLS-LMS initialization for per tone equalizers in DMT-receivers

In discrete multitone receivers, the classical equalizer structure consists of a (real) time domain equalizer (TEQ) combined with complex one-tap frequency domain equalizers. An alternative receiver is based on a per tone equalization (PTEQ), which optimizes the signal-to-noise ratio (SNR) on each tone separately and, hence, the total bitrate. In this paper, a new initialization scheme for the PTEQ is introduced, based on a combination of least mean squares (LMS) and recursive least squares (RLS) adaptive filtering. It is shown that the proposed method has only slightly slower convergence than full square-root RLS (SR-RLS) while complexity as well as memory cost are reduced considerably. Hence, in terms of complexity and convergence speed, the proposed algorithm is in between LMS and RLS.     

Adaptive joint beamforming and B‐MMSE detection for CDMA signal reception under multipath interference

The combination of antenna array beamforming with multiuser detection can effectively improve the detection efficiency of a wireless system under multipath interference, especially in a fast‐fading channel. This paper studies the performance of an adaptive beamformer incorporated with a block‐wise minimum mean square error(B‐MMSE) detector, which works on a unique signal frame characterized by training sequence preamble and data blocks segmented by zero‐bits. Both beam‐former weights updating and B‐MMSE detection are carried out by either least mean square (LMS) or recursive least square (RLS) algorithm. The comparison of the two adaptive algorithms applied to both beamformer and B‐MMSE detector will be made in terms of convergence behaviour and estimation mean square error. Various multipath patterns are considered to test the receiver's responding rapidity to changing multipath interference. The performance of the adaptive B‐MMSE detector is also compared with that of non‐adaptive version (i.e. through direct matrix inversion). The final performance in error probability simulation reveals that the RLS/B‐MMSE scheme outperforms non‐adaptive B‐MMSE by 1–5 dB, depending on the multipath channel delay profiles of concern. The obtained results also suggest that adaptive beamformer should use RLS algorithm for its fast and robust convergence property; while the B‐MMSE filter can choose either LMS or RLS algorithm depending on antenna array size, multipath severity and implementation complexity. Copyright © 2004 John Wiley & Sons, Ltd.     

Performance of Smart Antenna Under Different Fading Conditions

Optimizing the current distribution of an evenly spaced antenna array has shown to be an efficient approach for reducing side lobe levels. In this article, the Tchebyscheff distribution-based antenna array synthesis approach is combined with an adaptive signal processing algorithm for beamforming and side lobe level reduction in smart antennas in various fading situations. The performance of smart antennas in uniformly spaced linear, planar, circular, and semi-circular arrays are evaluated. The presence of Rayleigh and Rician channels is examined in the network. The least mean square (LMS) and normalised least mean square (NLMS) algorithms are applied as adaptive algorithms. In fading environments, the NLMS algorithm with Tchebyscheff distribution outperforms than the LMS algorithm with Tchebyscheff distribution, with a side lobe level decrease of 11.23 dB. The lowest side lobe achieved with the NLMS algorithm with Tchebyscheff distribution is???45.59 dB for uniform planar array.

     

Adaptive Hybrid Beamformer for Mobile Reception of ATSC DTV

An adaptive hybrid beamformer is proposed to improve the reception performance of the advanced television system committee (ATSC) digital television (DTV) in a mobile environment. Dynamic multipaths and Doppler shifts severely degrade the reception performance of the ATSC DTV receiver. Accordingly, a hybrid beamformer, called a Capon and least mean square (CLMS) beamformer, is presented that uses direction of arrival (DOA) information and the least mean square (LMS) beamforming algorithm. The proposed CLMS algorithm efficiently removes dynamic multipaths and compensates for the phase distortion caused by Doppler shifts in mobile receivers. When the CLMS beamformer has an insufficient degree of freedom (DOF), the subsequent use of an equalizer removes any residual multipath effects, thereby significantly improving the performance of DTV receivers. The performances of the proposed CLMS, Capon, and LMS beamformers are compared based on computer simulations. In addition, the overall performance of the CLMS beamformer followed by an equalizer is also considered.     

机扫雷达旁瓣对消算法研究

自适应旁瓣对消是雷达抑制有源干扰的有效措施,通常采用采样矩阵求逆的方法计算权值,对于方位上机械扫描的雷达,由于对消形成的方向图零点很窄,当天线转动时干扰的方向也会变化,针对这种情况,本文分析了运用采样矩阵求逆算法(SMI)和递归最小二乘算法(RLS)进行旁瓣对消时,干扰对消比的变化情况,并得出了RLS算法具有跟踪角度变...     

A New Adaptive Beamforming of Multiband Fractal Antenna Array in Strong-Jamming Environment

This paper proposes, for the first time, a new radiation pattern synthesis for fractal antenna array that combines the unique multi-band characteristics of fractal arrays with the adaptive beamforming requirements in wireless environment with high-jamming power. In this work, a new adaptive beamforming method based on discrete cbKalman filter is proposed for linear Cantor fractal array with high performance and low computational requirements. The proposed Kalman filter-based beamformer is compared with the Least Mean Squares (LMS) and the Recursive Least Squares (RLS) techniques under various parameter regimes, and the results reveal the superior performance of the proposed approach in terms of beamforming stability, Half-Power Beam Width (HPBW), maximum Side-Lobe Level (SLL), null depth at the direction of interference signals, and convergence rate for different Signal to Interference (SIR) values. Also, the results demonstrate that the suggested approach not only achieves perfect adaptation of the radiation pattern synthesis at high jamming power, but also keep the same SLL at different operating frequencies. This shows the usefulness of the proposed approach in multi-band smart antenna technology for mobile communications and other wireless systems.

     

Improved Soft Iterative Channel Estimation for Turbo Equalization of Time Varying Frequency Selective Channels

In this paper, we present computationally efficient iterative channel estimation algorithms for Turbo equalizer-based communication receiver. Least Mean Square (LMS) and Recursive least Square (RLS) algorithms have been widely used for updating of various filters used in communication systems. However, LMS algorithm, though very simple, suffers from a relatively slow and data dependent convergence behaviour; while RLS algorithm, with its fast convergence rate, finds little application in practical systems due to its computational complexity. Variants of LMS algorithm, Variable Step Size Normalized LMS (VSSNLMS) and Multiple Variable Step Size Normalized LMS algorithms, are employed through simulation for updating of channel estimates for turbo equalization in this paper. Results based on the combination of turbo equalizer with convolutional code as well as with turbo codes alongside with iterative channel estimation algorithms are presented. The simulation results for different normalized fade rates show how the proposed channel estimation based-algorithms outperformed the LMS algorithm and performed closely to the well known Recursive least square (RLS)-based channel estimation algorithm.     

Sparse LMS/F algorithms with application to adaptive system identification

Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advantages of both least mean square (LMS) and least mean fourth (LMF). The advantage of LMS is fast convergence speed while its shortcoming is suboptimal solution in low signal‐to‐noise ratio (SNR) environment. On the contrary, the advantage of LMF algorithm is robust in low SNR while its drawback is slow convergence speed in high SNR case. Many finite impulse response systems are modeled as sparse rather than traditionally dense. To take advantage of system sparsity, different sparse LMS algorithms with l_p‐LMS and l₀‐LMS have been proposed to improve adaptive identification performance. However, sparse LMS algorithms have the same drawback as standard LMS. Different from LMS filter, standard LMS/F filter can achieve better performance. Hence, the aim of this paper is to introduce sparse penalties to the LMS/F algorithm so that it can further improve identification performance. We propose two sparse LMS/F algorithms using two sparse constraints to improve adaptive identification performance. Two experiments are performed to show the effectiveness of the proposed algorithms by computer simulation. In the first experiment, the number of nonzero coefficients is changing, and the proposed algorithms can achieve better mean square deviation performance than sparse LMS algorithms. In the second experiment, the number of nonzero coefficient is fixed, and mean square deviation performance of sparse LMS/F algorithms is still better than that of sparse LMS algorithms. Copyright © 2013 John Wiley & Sons, Ltd.     

Conjugate gradient algorithm for adaptive finite impulse response digital filters

An iterative conjugate gradient algorithm to be used in designing adaptive finite impulse response digital filters is presented in this paper. This algorithm has the advantage of fast convergence compared to the well-known least mean square (LMS) algorithm. This characteristic is demonstrated experimentally through computer simulations. Comparison is made with the LMS algorithm.     

Statistical properties of the LMS fourier analyzer in the presence of frequency mismatch

The statistical performances of the conventional adaptive Fourier analyzers, such as the least mean square (LMS), the recursive least square (RLS) algorithms, and so forth, may degenerate significantly, if the signal frequencies given to the analyzers are different from the true signal frequencies. This difference is referred to as frequency mismatch (FM). We analyze extensively the performance of the conventional LMS Fourier analyzer in the presence of FM. Difference equations governing the dynamics and closed-form steady-state expression for the estimation mean square error (MSE) of the algorithm are derived in detail. It is revealed that the discrete Fourier coefficient (DFC) estimation problem in the LMS eventually reduces to a DFC tracking one due to the FM, and an additional term derived from DFC tracking appears in the closed-form MSE expression, which essentially deteriorates the performance of the algorithm. How to derive the optimum step size parameters that minimize or mitigate the influence of the FM is also presented, which can be used to perform robust design of step size parameters for the LMS algorithm in the presence of FM. Extensive simulations are conducted to reveal the validity of the analytical results.     

Comparative tracking performance of the LMS and RLS algorithms forchirped narrowband signal recovery

This paper studies the comparative tracking performance of the recursive least squares (RLS) and least mean square (LMS) algorithms for time-varying inputs, specifically for linearly chirped narrowband input signals in additive white Gaussian noise. It is shown that the structural differences in the implementation of the LMS and RLS weight updates produce regions where the LMS performance exceeds that of the RLS and other regions where the converse occurs. These regions are shown to be a function of the signal bandwidth and signal-to-noise ratio (SNR). LMS is shown to place a notch in the signal band of the mean lag filter, thus reducing the lag error and improving the tracking performance. For the chirped signal, it is shown that this produces smaller tracking error for small SNR. For high SNR, there is a region of signal bandwidth for which RLS will provide lower error than LMS, but even for these high SNR inputs, LMS always provides superior performance for very narrowband signals     

The multi-user detection under fading channel

This paper proposes a method of blind multi-user detection algorithm based on signal sub-space estimation under the fading channels in the present of impulse noise. This algorithm adapts recursive least square (RLS) filter that can estimate the coefficients using only the signature waveform. In addition, to strengthen the ability of resisting the impulse noise, a new suppressive factor is induced, which can suppress the amplitude of the impulse, and improve the ability of convergence speed. Simulation results show that new RLS algorithm is more robust against consecutive impulse noise and have better convergence ability than conventional RLS. In addition, Compared to the least mean square (LMS) detector, the new robust RLS sub-space based method has better multi-address-inference (MAI) suppressing performance, especially, when channel degrades.     

Transform domain conjugate gradient algorithm for adaptive filtering

This paper proposed a new normalized transform domain conjugate gradient algorithm (NT-CGA), which applies the data independent normalized orthogonal transform technique to approximately whiten the input signal and utilises the modified conjugate gradient method to perform sample-by-sample updating of the filter weights more efficiently. Simulation results illustrated that the proposed algorithm has the ability to provide a fast convergence speed and lower steady-error compared to that of traditional least mean square algorithm (LMSA), normalized transform domain least mean square algorithm (NT- LMSA), Quasi-Newton least mean square algorithm (Q-LMSA) and time domain conjugate gradient algorithm (TD-CGA) when the input signal is heavily coloured.     

基于矩阵广义逆递推的自适应滤波算法

本文把自适应滤波算法的优化准则之一最小二乘准则:J(n)= ∑ n i=1 λ^n-i|e(i)|²写为矩阵形式,利用矩阵广义逆递推公式直接对输入信号矩阵而不是自相关矩阵进行递推更新,得到一种新的自适应滤波算法.和其它算法如LMS算法、NLMS算法、FRLS算法、TDNLMS算法、 APA算法、Leaky-LMS算法和RLS算法进行了计算机模拟仿真比较,仿真结果表明该算法有良好的收敛性能,收敛速度快于LMS算法、NLMS算法、FRLS算法、 APA算法、Leaky-LMS算法和RLS算法.     

OFDM系统自适应数字预失真研究

研究了正交频分复用（OFDM）传输系统中高功率放大器（HPA）的自适应预失真方法。针对OFDM信号的高峰平比特性及HPA带来的非线性失真,提出一种基于训练序列的最小均方误差（LMS）算法和递归最小二乘（RLS）算法的组合算法,将其应用到基于记忆多项式模型的数字预失真系统中。用MATLAB构建一个基于该自适应算法的预失真系统。仿真结果表明：该算法能有效的改善放大器的非线性特性。     

卫星移动通信系统低信噪比抗干扰数字接收机设计

针对阵列天线卫星移动通信抗干扰能力差、传播损耗大等特性,设计了一种数字波束形成技术( DBF)和扩频技术相结合的数字接收机。其中,DBF算法采用基于递归最小均方算法( RLS)的解扩重扩盲自适应波束形成算法,使用VxWorks实现权值计算,FPGA实现波束形成;扩频方式采用直接序列扩频,在FPGA中实现。仿真分析与样机测试显示,通过两项技术联合使用,在信噪比低至-45 dB条件下仍可以实现可靠通信,同时有效加强了系统抗干扰能力。     

The Averaged, Overdetermined, and Generalized LMS Algorithm

This paper provides and exploits one possible formal framework in which to compare and contrast the two most important families of adaptive algorithms: the least-mean square (LMS) family and the recursive least squares (RLS) family. Existing and well-known algorithms, belonging to any of these two families, like the LMS algorithm and the RLS algorithm, have a natural position within the proposed formal framework. The proposed formal framework also includes - among others - the LMS/overdetermined recursive instrumental variable (ORIV) algorithm and the generalized LMS (GLMS) algorithm, which is an instrumental variable (IV) enable LMS algorithm. Furthermore, this formal framework allows a straightforward derivation of new algorithms, with enhanced properties respect to the existing ones: specifically, the ORIV algorithm is exported to the LMS family, resulting in the derivation of the averaged, overdetermined, and generalized LMS (AOGLMS) algorithm, an overdetermined LMS algorithm able to work with an IV. The proposed AOGLMS algorithm overcomes - as we analytically show here - the limitations of GLMS and possesses a much lower computational burden than LMS/ORIV, being in this way a better alternative to both algorithms. Simulations verify the analysis.     

一种新的变步长NLMS盲自适应多用户检测算法

首先推导得到最小输出能力准则的无约束形式,得到易于NLMS算法实现形式;在该NLMS算法实现的基础上,通过重复归一化迭代运算推导,得到重复跌代的等效形式;基于该形式提出一种新的变步长NLMS算法,算法复杂度仍然为O(N),算法具有很好的健壮性,并且具有更好的检测综合性能。仿真表明,与文献［5］所提变参数的变步长NLMS算法相比,本文算法收敛速度更快且输出信干比更好。在同步CDMA系统下,其综合性能达到KALMAN,RLS相当水平。