Masao Masugi

Automatic Parameter Setting Method for an Accurate Kalman Filter Tracker Using an Analytical Steady-State Performance Index

We present an automatic parameter setting method to achieve an accurate second-order Kalman filter tracker based on a steady-state performance index. First, we propose an efficient steady-state performance index that corresponds to the root-mean-square (rms) prediction error in tracking. We then derive an analytical relationship between the proposed performance index and the generalized error covariance matrix of the process noise, for which the automatic determination using the derived relationship is presented. The model calculated by the proposed method achieves better accuracy than the conventional empirical model of process noise. Numerical analysis and simulations demonstrate the effectiveness of the proposed method for targets with accelerating motion. The rms prediction error of the tracker designed by the proposed method is 63.8% of that with the conventional empirically selected model for a target accelerating at 10 m/s2.

Proposal of A Novel EMI Diagnostic System using Independent Component Analysis

This paper presents a noise diagnosing system that can separate multiple interfering signals due to crosstalk or common-mode noise problems. In estimating original time-series signals from measured data sets, our system uses the independent component analysis (ICA) scheme, which uses a statistical technique to separate blind source signals from measured data sets. In addition, mutual-correlation coefficients between separated and measured signals are used to obtain a set of measured and estimated signals. Evaluations of transmission lines confirmed that our system is effective for diagnosing multiple electromagnetic interference (EMI) problems, resulting in the mutual-correlation coefficients between the original and estimated signals being more than 0.9 for 1-20 MHz sinusoidal signals.

Measurement And Analysis Of Electromagnetic Pulses Caused By Electrostatic Dischrage

This paper describes experimental and analytical results of electromagnetic pulses caused by electrostatic discharge (ESD). Energy spectra of ESD pulses measured by plural antennas are analyzed for three frequency domains ( Analytical results show that ESD energy does not always increase in proportion to discharge voltages and that ESD pulses produced by low voltage ESD may cause strong electromagnetic interference. 1-30MHzI 30-200MH2, 200-750MHz ) .

Performance analysis of α - β - γ tracking filters using position and velocity measurements

This paper examines the performance of two position-velocity-measured (PVM) α- β- γ tracking filters. The first estimates the target acceleration using the measured velocity, and the second, which is proposed for the first time in this paper, estimates acceleration using the measured position. To quantify the performance of these PVM α- β- γ filters, we analytically derive steady-state errors that assume that the target is moving with constant acceleration or jerk. With these performance indices, the optimal gains of the PVM α- β- γ filters are determined using a minimum-variance filter criterion. The performance of each filter under these optimal gains is then analyzed and compared. Numerical analyses clarify the performance of the PVM α- β- γ filters and verify that their accuracy is better than that of the general position-only-measured α- β- γ filter, even when the variance in velocity measurement noise is comparatively large. We identify the conditions under which the proposed PVM α- β- γ filter outperforms the general α- β- γ filter for different ratios of noise variance in the velocity and position measurements. Finally, numerical simulations verify the effectiveness of the PVM α- β- γ filters for a realistic maneuvering target.

α–β–γ tracking filters using acceleration measurements

BackgroundAlthough real-time tracking of moving objects using a variety of sensor parameters is in great demand in monitoring systems, no studies have reported α–

Gait Classification of Young Adults, Elderly Non-Fallers, and Elderly Fallers Using Micro-Doppler Radar Signals: Simulation Study

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Velocity/Shape Estimation Algorithm Using Tracking Filter and Data Fusion of Dual Doppler Radar Interferometers

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