A Log-Normal Complex-Amplitude Likelihood Ratio-Based TBD Method With Soft Orbit-Information Constraints for Tracking Space Targets With Space-Based Radar

Abstract

Space-based radars (SBRs) systems are able to provide an unobstructed field of view for space target detection and tracking. However, the large temperature dynamic range and poor heat dissipation performance of the SBR system cause severe thermal noise, leading to deficiency in distant or dim space target detection tasks. In essence, the challenges above can be categorized as typical low signal-to-noise ratio (SNR) problems, and the track before detect (TBD) processing scheme is applied to solve them in this article. Nevertheless, the typical TBD methods reckon without the following aspects and thus are not well compatible with space target surveillance tasks via the SBR system. First, the typical TBD methods discard the phase information of radar raw data in constructing the likelihood ratio. In addition, most existing work merely considers modeling the amplitude fluctuation as Swerling types, which is not accurate enough for space targets when compared with the log-normal distribution (LND) model. Moreover, orbital space targets follow the orbital dynamic principle while most existing TBD methods neglect this important information, which will cause space targets filtering estimation bias. To address the aforementioned problems, we propose a TBD method based on the complex-amplitude likelihood ratio (CLR) of the LND model and soft orbit-information constraint (OC). In this article, with the aim of acquiring a more accurate likelihood ratio, we first derive the closed mathematical form of the amplitude likelihood ratio (ALR) and the CLR of the LND model. Meanwhile, some approximations are proposed to alleviate the integral computation. Then, the proposed ALR and CLR of the LND model are utilized to be implemented into the TBD scheme. Finally, we design elegant soft OC strategies to modify the associated weights corresponding with birth particles in sequential Monte Carlo (SMC) implementation. Simulation results are provided to validate the effectiveness of the proposed soft OC-CLR-TBD method.