Abstract
The estimation problem for target velocity is addressed in this in the scenario with a distributed multi-input multi-out (MIMO) radar system. A maximum likelihood (ML)-based estimation method is derived with the knowledge of target position. Then, in the scenario without the knowledge of target position, an iterative method is proposed to estimate the target velocity by updating the position information iteratively. Moreover, the Carmér-Rao Lower Bounds (CRLBs) for both scenarios are derived, and the performance degradation of velocity estimation without the position information is also expressed. Simulation results show that the proposed estimation methods can approach the CRLBs, and the velocity estimation performance can be further improved by increasing either the number of radar antennas or the information accuracy of the target position. Furthermore, compared with the existing methods, a better estimation performance can be achieved.
Highlights
Comparing the Carmér-Rao Lower Bounds (CRLBs) in (29) with the one in (49), we can find that CRLB0 (v) ≥ CRLB(v), so without the position information, the estimation accuracy of target velocity is worse than that with the position information
With the position information, the estimation performance of target velocity is given in maximum likelihood (ML)-based method
In the distributed multi-input multi-output (MIMO) radar system, the estimation problem for target velocity has been considered in this paper
Summary
The multi-input multi-output (MIMO) radar systems can be classified into the following two types: 2. The Carmér-Rao Lower Bound (CRLB) can be used to analyze the target estimation performance in the MIMO radar system. The CRLBs are used to measure the velocity estimation performance in the distributed MIMO radar, and the effect of target position error is considered. The estimation problem for target velocity is addressed in the distributed MIMO radar, and a Maximum Likelihood (ML)-based method is expressed to estimate the target velocity in the scenario with target position information. In the scenario without the position information, an iterative method is proposed to update the target position, and to improve the performance of velocity estimation. The corresponding Carmér-Rao Lower Bounds (CRLBs) for both scenarios are derived and compared with the estimation performance of the proposed methods.
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