Abstract
Distributed array radar can improve radar detection capability and measurement accuracy. However, it will suffer cyclic ambiguity in its angle estimates according to the spatial Nyquist sampling theorem since the large sparse array is undersampling. Consequently, the state estimation accuracy and track validity probability degrades when the ambiguous angles are directly used for target tracking. This paper proposes a second probability data association filter (SePDAF)-based tracking method for distributed array radar. Firstly, the target motion model and radar measurement model is built. Secondly, the fusion result of each radar’s estimation is employed to the extended Kalman filter (EKF) to finish the first filtering. Thirdly, taking this result as prior knowledge, and associating with the array-processed ambiguous angles, the SePDAF is applied to accomplish the second filtering, and then achieving a high accuracy and stable trajectory with relatively low computational complexity. Moreover, the azimuth filtering accuracy will be promoted dramatically and the position filtering accuracy will also improve. Finally, simulations illustrate the effectiveness of the proposed method.
Highlights
Distributed array radar has been widely concerned with its superiority in many aspects since it has been put forward [1]
The distributed coherent aperture radar has been proposed in order to obtain the N3 times signal-to-noise ratio (SNR)
Assume the distributed array radar consists of N sub-radars which are arranged in a uniform linear array (ULA), the sub-radar spacing is d, the baseline of the distributed array is (N −1)d
Summary
Distributed array radar has been widely concerned with its superiority in many aspects since it has been put forward [1]. Inspired by the DOA tracking method [24] and the multiple sensors tracking method [25,26,27,28,29,30], this paper proposes a second probability data association filter (SePDAF)-based tracking method It uses the unambiguous angle and ambiguous angles as measurements, twice applying filtering, i.e., EKF and SePDAF, to achieve the high accuracy unambiguous filtering estimate and stable trajectory simultaneously. This method produces a novel tracking mode with relatively low computational complexity for distributed array radar in order to replace the traditional one. Measurement model; Section 3 firstly analyzes the probability model, proposes the SePDAF method to achieve the high accuracy trajectory, after that investigates the computational complexity; Section 4 carries out the simulations to validate the effectiveness of the proposed method; and, Section 5 draws the conclusion
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