Turbo均衡是基于软信息的迭代处理思想在均衡领域中的成功应用,它将信道均衡和差错控制译码联合起来迭代处理,能在更低的信噪比条件下克服严重信道失真所导致的符号间干扰(ISI)。软输入软输出卷积译码是实现Turbo均衡的一个重要环节。用于Turbo均衡的卷积译码器在输入及输出要求上都与Turbo译码中的卷积译码器有所不同。文中针对这种差异,给出了适于Turbo均衡的MAP译码算法。由于该算法描述并不局限于常见的单输入系统卷积码,因此也可用于多输入、非系统卷积码的译码。 Turbo equalization is a successful application of the idea of ​​iterative processing based on soft information in the field of equalization. It combines channel equalization and error control decoding to iteratively process symbols and overcome the sign of severe channel distortion under lower signal-to-noise ratio Interference (ISI). Soft-input soft-output convolutional decoding is an important part of Turbo equalization. The convolutional decoder used for Turbo equalization differs from the convolutional decoder used in Turbo decoding both in terms of input and output. In view of this difference, this paper presents a MAP decoding algorithm suitable for Turbo equalization. Since the algorithm description is not limited to the common single-input system convolutional code, it can also be used for decoding multiple-input, non-systematic convolutional codes.