In this paper, an estimation algorithm for the position and velocity of a moving target in a multistatic radar system is investigated. Estimation accuracy is improved by using bistatic range (BR), time-difference-of-arrival (TDOA), and Doppler shifts. Multistatic radar system includes several independent receivers and transmitters of time synchronization. Different transmitters radiate signals of different frequencies, and receivers detect the Doppler shifts of the received signals. These estimation parameters, BR, TDOA, and Doppler shifts, are readily available. The proposed algorithm combines different estimated parameters and optimizes estimation accuracy by two-step weighted least squares minimisations (WLS). This estimation algorithm is analysed and verified by simulations, which can reach the Cramer–Rao lower bound (CRLB) performance under mild Gaussian noise when the measurement error is small. Numerical simulations also demonstrate the superior performance of this method.
With the rapid development of the Internet of Things (IoT), autonomous vehicles have been receiving more and more attention because they own many advantages compared with traditional vehicles. A robust and accurate vehicle localization system is critical to the safety and the efficiency of autonomous vehicles. The global positioning system (GPS) has been widely applied to the vehicle localization systems. However, the accuracy and the reliability of GPS have suffered in some scenarios. In this paper, we present a robust and accurate vehicle localization system consisting of a bistatic passive radar, in which the performance of localization is solely dependent on the accuracy of the proposed off-grid direction of arrival (DOA) estimation algorithm. Under the framework of sparse Bayesian learning (SBL), the source powers and the noise variance are estimated by a fast evidence maximization method, and the off-grid gap is effectively handled by an advanced grid refining strategy. Simulation results show that the proposed method exhibits better performance than the existing sparse signal representation-based algorithms, and performs well in the vehicle localization system.
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