This paper derives recursive linear least-squares fixed-interval smoothing algorithm using covariance information from a Wiener-Hopf integral equation. The algorithm is obtained for the white plus coloured and white Gaussian observation noises. Autocovariance functions of the signal and the coloured noise are expressed using a degenerate kernel. The degenerate kernel can represent general covariance functions of nonstationary or stationary processes by a finite sum of nonrandom functions. The efficiency of the smoother was assured by a numerical example.