主要内容

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自适应滤波器

LMS、RLS、频域滤波器,仿射投影滤波器、自适应格型滤波器

DSP System Toolbox™ 提供了 LMS 和 RLS 自适应有限冲激响应 (FIR) 滤波器算法的多种变体。虽然这些算法在细节上有所不同,但它们采用同一种运算方法,即最小化自适应滤波器输出和所需信号之间的误差。均方误差 (MSE) 是量化这种误差的最常用度量。自适应滤波器的应用范围广泛,包括声学降噪、回声消除、波束成形、系统辨识、生物医学信号增强、通信信道均衡等。有关其中部分应用的示例,请参阅System Identification of FIR Filter Using LMS AlgorithmNoise Cancellation Using Sign-Data LMS AlgorithmInverse System Identification Using RLS Algorithm

当输入着色时,由 dsp.AffineProjectionFilter 对象提供的仿射投影自适应滤波算法相较于 LMS 变体显著提高了收敛速度。尽管计算成本有所增加,但由 dsp.AdaptiveLatticeFilter 对象提供的自适应格型滤波器算法在收敛方面要优于 LMS 和 RLS 变体。您也可以使用 dsp.FrequencyDomainAdaptiveFilter 对象在频域中实现自适应 FIR 滤波器。

收敛性取决于实际的 MSE(由 msesim 确定)的轨迹,以及实际的 MSE 与预测的 MSE(由 msepred 确定)的趋近程度。

对象

dsp.BlockLMSFilterCompute output, error, and weights using block least mean squares (LMS) adaptive algorithm
dsp.LMSFilterCompute output, error, and weights of least mean squares (LMS) adaptive filter
dsp.RLSFilterCompute output, error and coefficients using recursive least squares (RLS) algorithm
dsp.AffineProjectionFilterCompute output, error and coefficients using affine projection (AP) Algorithm
dsp.AdaptiveLatticeFilterAdaptive lattice filter
dsp.FastTransversalFilterFast transversal least-squares FIR adaptive filter
dsp.FilteredXLMSFilterFiltered XLMS filter
dsp.FrequencyDomainAdaptiveFilterCompute output, error, and coefficients using frequency-domain FIR adaptive filter

模块

Block LMS FilterCompute output, error, and weights using LMS adaptive algorithm
Fast Block LMS FilterCompute output, error, and weights using least mean squares (LMS) adaptive algorithm
Frequency-Domain Adaptive FilterCompute output, error, and coefficients using frequency domain FIR adaptive filter
Kalman FilterPredict or estimate states of dynamic systems
LMS FilterCompute output, error, and weights using least mean squares (LMS) adaptive algorithm
LMS UpdateEstimate weights of least mean squares (LMS) adaptive filter
RLS FilterCompute filtered output, filter error, and filter weights for given input and desired signal using RLS adaptive filter algorithm

主题

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