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Leak detection and location play an important role in the management of a pipeline system. Some model-based methods,such as those based on the extended Kalman filter (EKF) or based on the strong tracking filter (STF), have been presentedto solve this problem. But these methods need the nonlinear pipeline model to be linearized. Unfortunately, linearizedtransformations are only reliable if error propagation can be well approximated by a linear function, and this condition doesnot hold for a gas pipeline model. ; This will deteriorate the speed and accuracy of the detection and location. Particle filtersare sequential Monte Carlo methods based on point mass (or ?particle?) representations of probability densities, which canbe applied to estimate states in nonlinear and non-Gaussian systems without linearization. Parameter estimation methods arewidely used in fault detection and diagnosis (FDD), and have been applied to pipeline leak detection and location. However,the standard particle filter algorithm is not applicable to time-varying parameter estimation. ; To solve this problem, artificialnoise has to be added to the parameters, but its variance is difficult to determine. In this paper, we propose an adaptiveparticle filter algorithm, in which the variance of the artificial noise can be adjusted adaptively. This method is applied toleak detection and location of gas pipelines. Simulation results show that fast and accurate leak detection and location canbe achieved using this improved particle filter.