In this paper, we derive a filtering method that is based on wavelet packets theory. With the theory we can decompose a length of measurement data into two parts in different resolution levels. One is the low frequency component, the other is the high frequency component. We can detect the target maneuvering respectively in the two components according to different criteria because characteristics of the maneuver information in the two components are not the same. The temporal and spatial location of the target maneuvering can be derived by an 'AND' operation between those of the components. On the other hand, we can also reconstruct the signal with the decomposed components. When in signal reconstruction, we can deliberately filter or even discard some subspaces of the wavelet packets. The reconstruction can be done in certain resolution levels. Thus, by means of subspace selection and reconstruction, we can design a wavelet packets filter with some characteristics, such as the characteristics of lowpass filtering or highpass filtering. With the wavelet packets decomposition and reconstruction, we can not only detect the target maneuver in different resolution level, but we can also attenuate the noise component with little or without attenuation of the target's location and tracking information and maneuver information, providing a 'better' data for the later Kalman filter bank.