An Active Noise Control Algorithm Without Secondary Path Identification Based On Kalman Filter

With the development of advancing technology, noise pollution has been beingexacerbating, especially serious pollution is low-frequency noise generated byrotating equipments and those with reciprocating motion devices, reciprocatingmotion device. Over the past decade, active noise control (Active Noise Control,ANC) technology, aiming at low frequency noise cancellation, is becoming a hotspot of research in the field of noise control. However, the vast majority of ANCalgorithms are Filtering X algorithms, the need for the secondary path between theANC controller output and eliminate noise online or offline recognition. Secondarypath identification will not only introduce the auxiliary noise to reduce the amountof noise reduction, but the identification error also led to deterioration of theperformance of the control system.In this paper, we propose a novel ANC algorithm without secondary pathidentification based on Kalman filter, referred to as Model Error CompensatoryKalman Filter (MECKF). The ANC problem is described in discrete-timestate-space form first, then the dynamics of the primary path can be attributed to thestate variables. Kalman filter is applied to estimate the weights using residual noisesequence. Furthermore, based on acoustics properties and stochastic theory, weintroduce a model error compensating mechanism by shifting the influence of theunknown secondary path into variance of measurement matrix. In addition, a newmethod of estimating the statistical properties of the noise in dynamic model isgiven in the context of ANC system, with merits of reduced computationalcomplexity, increased convergence rate, and ensured real-time. Simulation resultsdemonstrate the effectiveness of the proposed MECKF algorithm in both stable andunstable environment.