Robust automotive radar interference mitigation using multiplicative-adaptive filtering and Hilbert transform

Radar is one of the sensors that have significant attention to be implemented in an autonomous vehicle since its robustness under many possible environmental conditions such as fog, rain, and poor light. However, the implementation risks interference because of transmitting and/or receiving radar signals from/to other vehicles. This interference will increase the floor noise that can mask the target signal. This paper proposes multiplicative-adaptive filtering and Hilbert transform to mitigate the interference effect and maintain the target signal detectability. The method exploited the trade-off between the step-size and sidelobe effect on the least mean square-based adaptive filtering to improve the target detection accuracy, especially in the long-range case. The numerical analysis on the millimeter-wave frequency modulated continuous wave radar with multiple interferers concluded that the proposed method could maintain and enhance the target signal even if the target range is relatively far from the victim radar

A novel positioning method for magnetic spiral-type capsule endoscope using an adaptive LMS algorithm

In this paper, a novel positioning method for the wireless capsule endoscope (WCE) is proposed. An up-down symmetric array of magnetic sensors is used to detect magnetic signals from an external permanent magnet (EPM) for active control and mixed magnetic signals (the EPM and the WCE), and the adaptive least mean squares (LMS) algorithm is applied. Firstly, the number of iterations is determined by comparing the cancellation effect of input signals of different lengths. Subsequently, to separate the WCE's magnetic signals from the mixed magnetic signals, the data obtained from the magnetic sensor arrays are processed in weighted iterations. The method has been applied to the actual experimental platform. From the experimental results achieved in this work, the average relative errors of the WCE's triaxial signals were found to be 2.04 %, 2.20 %, and 1.47 % respectively. Achieved results demonstrate the feasibility and rationality of the positioning method discussed in this work. Moreover, the method can solve the problem of strong magnetic interference when the EPM provides active control to the WCE. It plays an essential role in driving the realization of closed-loop control of the WCE

Interference Mitigation for FMCW Radar With Sparse and Low-Rank Hankel Matrix Decomposition

In this paper, the interference mitigation for Frequency Modulated Continuous Wave (FMCW) radar system with a dechirping receiver is investigated. After dechirping operation, the scattered signals from targets result in beat signals, i.e., the sum of complex exponentials while the interferences lead to chirp-like short pulses. Taking advantage of these different time and frequency features between the useful signals and the interferences, the interference mitigation is formulated as an optimization problem: a sparse and low-rank decomposition of a Hankel matrix constructed by lifting the measurements. Then, an iterative optimization algorithm is proposed to tackle it by exploiting the Alternating Direction of Multipliers (ADMM) scheme. Compared to the existing methods, the proposed approach does not need to detect the interference and also improves the estimation accuracy of the separated useful signals. Both numerical simulations with point-like targets and experiment results with distributed targets (i.e., raindrops) are presented to demonstrate and verify its performance. The results show that the proposed approach is generally applicable for interference mitigation in both stationary and moving target scenarios.Comment: 12 pages, 8 figure