This invention relates to a method of adjusting the weighting coefficients corresponding to N successive stages of a transversal filter according to the least means squares algorithm, in which method signal samples are supplied to the filter in succession, differences between output signals of the filter and a reference are determined, which output signals each result from a respective group of N of said samples being weighted by said weighting coefficients, and a respective correction derived from the product of a said difference and the current content of a stage of the filter is applied to each said weighting coefficient. The invention also relates to a modification of such a method in which inter alia the transversal filter is replaced by a decision feedback filter. Moreover the invention relates to arrangements for implementing such methods.
It is often required that the weighting coefficients of a transversal filter be adjusted in order to render the filter characteristic closer to that currently required. For example, such a filter may be employed as a so-called "equaliser" for compensating for distortion to a transmitted signal caused by the imperfect nature of a transmission channel. If the channel characteristics vary with time then, in order that satisfactory equalisation continues to be achieved, it is necessary that the filter characteristic be updated either effectively continuously or periodically to take into account the changed channel characteristics. To this end methods of the general kind defined in the first paragraph are well-known, the "least mean squares algorithm" being one of several algorithms which could be employed for the adjustment process. In the known methods each coefficient is adjusted once for every sample applied to the filter according to the formulae: ##EQU1## where C.sub.1 (k), C.sub.2 (k) . . . C.sub.N (k) are the weighting coefficients corresponding to the N successive stages of the filter for a given sample period k of the input signal, .mu. is a constant which is usually less than 0.1, e(k) is the difference between the output of the filter and the reference for the given sample period k, and V.sub.1 (k), V.sub.2 (k), . . . V.sub.N (k) are the respective contents of the N successive stages of the filter for the given sample period k. This adjustment of every coefficient once for every sample period gives theoretically optimum results in respect of speed of acquisition of the required characteristic. However, the large amount of computation required in order to achieve this implies a relatively large power consumption, which can be a disadvantage in many applications.