2-D inverse synthetic aperture Ladar imaging and tracking of maneuvering extended Micro-Doppler UAVs in atmospheric turbulence with skewness

Abstract

The inverse synthetic aperture Ladar (ISAL) is an important system for observation of space objects. Here, a 2-D ISAL imaging and tracking algorithm is proposed for low observable maneuvering extended objects tracking (M−EOT) such as small space UAVs. When imaging maneuvering objects with ISAL, dispersion and Doppler frequency time-variation exist in the range and cross-range echo signal, respectively. Additionally, the ISAL system can achieve high-resolution images for long-range moving objects, while its performance is affected by atmospheric turbulence. Atmospheric turbulence is a very complicated random state formed by the instability of regular laminar flow at a high maneuvering velocity, which is caused by the random motion of the atmosphere and lead to the random fluctuation of the atmospheric refractive index. Therefore, in real-time M−EOT scenario, the measurements distribution over the whole object is not symmetrical but distributed and skewed in some portions while a target maneuvers due to the unbalanced of reflection that caused by the atmospheric turbulence. In practice, the micro Doppler effect caused by micro moving components such as drone rotors will seriously affect the focused ISAL imaging of the M−EOT’s rigid body. To solve these problems in real-time low observable M−EOTs imaging and tracking from 2-D ISAL system, a novel robustness measurement model and recursion multi-Bernoulli track-before-detect filter based on a multiple model skewed non-symmetrically normal distribution is represented with more than one ellipse and lower complexity. In this paper, the Radon-Laplace method is presented to reduce this micro-Doppler effect in ISAL imaging. Additionally, we will eliminate the effect of atmospheric turbulence in ISAL Imaging by using the network-based variance–covariance estimation (NVCE) weighting method. In particular, we describe the M−EOT’s state through the random matrices model (RMM). In RMM, the extent is applied by one ellipse, which often lose information about size, heading, and shape, when the scatter centers are distributed symmetrically around the M−EOT’s centroid. In this paper, a new observation model used to modify a sub-RMM by considering the characteristics of 2-D ISAL data is proposed. Simulation results demonstrates the proposed robustness algorithm with the influence of the atmospheric turbulence and micro-Doppler.