A bistatic chirp scaling algorithm (CSA) is presented to process the forward-looking bistatic synthetic aperture radar (FL-BiSAR) data. First, the double-square-root equation in the general bistatic range model can be simplified to a square-root equation, when the size of the scene is small enough. Based on the model, the range Doppler spectrum is derived. Then, a CSA is obtained. The range model is proved to be an effective proxy by a simulation.
This paper deals with the imaging problem of one stationary bistatic Synthetic aperture radar (BiSAR) with motion error .Since a motion error is the main phase error source in the aircraft SAR, the motion errors have to properly be compensated during the SAR image reconstruction. The nonlinear chirp scaling (NLCS) algorithm is one of the typical algorithms in the practical radar system. An improved NLCS algorithm which can compensate motion error for One-Stationary BiSAR is proposed. Specially, the proposed method is based on the constant acceleration movement and the sub-aperture technique.
In distributed passive localization and tracking system, the track
observed by the subsystem seems like Brownian motion track, because the
tracked target is non-cooperative target and its maneuver is often
complex, and the localization accuracy is poor. These track
characteristics will seriously disturb track association between
different subsystems. In order to solve this problem, the track to track
association algorithm based on empirical mode decomposition (EMD) is
proposed in this letter. Each dimension data of the track from each
subsystem is processed by EMD and the high frequency components are
eliminated. A track motion trend (MT) vector is created by collecting
the rest components of the processed data. For these vectors, the
corresponding rule is constructed, in which the association threshold is
self-adaptive, and the assumption of the target motion model is not
required. The simulation results show that the proposed algorithm can
accomplish track association effectively in passive localization
systems.
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