Range-Doppler domain spatial alignment for networked radars

Abstract

An important prerequisite for the radar network detection is that the measurements from local radars are transformed to a common reference frame without systematic or registration errors. For the signal level alignment, only partial signals are available for global decision-making due to power and bandwidth limitations. In this paper, a low-communication-rate spatial alignment in range-Doppler domain is proposed for networked radars without the prior spatial information (positions and attitudes) of radars, which is different from the existing methods in the trajectory domain or echo domain for alignment. To reduce the radar-to-fusion-center communication-rate, the method of initial constant false alarm rate detection is used to censor the signals in range-Doppler domain from local radars. Based on the spatial alignment model for the networked radars in geometry, a maximization problem is formulated. The objective function is the cross-correlation between the range-Doppler domain signals from different local radars. The optimization problem is solved by a genetic algorithm. Simulation results show that the rotation matrix and translation vector are estimated, and the detection probability of the proposed algorithm is improved after alignment and fusion compared with state-of-art methods.

Performance Evaluation of Simultaneous Sensor Registration and Object Tracking Algorithm

“…They provide results from a simulated test environment of true sensor bias versus estimated sensor bias their approach. Cong et al [11] model the alignment of reference frames in networked radars as a maximisation problem, solved by a genetic algorithm. They provide a comprehensive performance evaluation and comparison with five other methods.…”

“…n and p are determined empirically and set to values of N and 0.5, respectively. The weights are updated with a likelihood function, ℓ k (ψ i k,s |Z k ), derived from the evaluation of the integral form of the Kalman likelihood conditioned on ψ i k,s -see Equation (11) in [16]. Note that Z k is the set of all sensor measurements up to time k. In the case of the EKF:…”

“…, the second term on the right hand side of Equation (11), can be immediately evaluated using Equation (9) using standard vector calculus results. In short:…”