H. Kameda

Target tracking using TDOA/FDOA measurements in the distributed sensor network

This paper proposes a target positioning algorithm using asynchronous TDOA (Time Difference of Arrival) and FDOA(Frequency Difference of Arrival) measurements for a single target in a distributed sensor network. A conventional algorithm, target localization through TDOA measurements cannot estimate target position when the number of TDOA measurements is not enough for localization at the same time. Our algorithm uses TDOA and FDOA measurements at the different time to compute the position and velocity estimates. Through computer simulation trials, the validity of our algorithm is confirmed.

Target tracking under dense environments using range rate measurements

Summarizes a multiple target tracking algorithm using both position and range rate measurements. This filtering algorithm is discussed in terms of the fundamental issues of tracking performance, tracking success rate and tracking errors as compared with other conventional methodologies. Through several simulations, validity of this algorithm has been confirmed.

Tracking algorithm using N-back scan MHT under dense environments

MHT(multiple hypothesis tracking) is well-known tracking algorithm for its association performance in dense environments. In the MHT, hypotheses are reduced by calculating probability and upper hypotheses are selected in a usual way to reduce computational burden. In the field of visual tracking, the N-back is applied as hypothesis reduction method and its efficiency is verified. The method maintains all hypotheses in recent N scans and selects the best hypothesis in past over N-scan, for hypotheses reduction. We apply the method to radar tracking and evaluate its performance, through Monte Carlo simulation of track maintenance for a low observable target. From the result of simulation, we have confirmed that N-back scan MHT shows better performance than conventional MHT. For example, the former has shown 30 point improvement with respect to tracking success rate for tracking a maneuvering target than latter.

Manoeuvring target tracking using constant velocity and constant angular velocity model

A constant velocity model and a constant acceleration model are often used as a dynamic model of a tracking filter. If a target turns, these filters cannot give full performance due to the disagreement between the real target and these models. To solve the problem, in this paper, we propose the tracking filter using the constant velocity and constant angular velocity model. This model has better agreements with actual dynamics. In this paper, the effectiveness has been confirmed through computer simulations.

Evaluation of tracking filter performance for maneuvering targets in dense environment

A new method for maneuvering target tracking in a dense environment is presented. This is an extension and improvement of the conventional joint probabilistic data association. The maneuver acceleration is assumed to be limited to a time invariant set of discrete values and switched values according to the Markov process. In this method, the maneuver of a target increases the prediction covariance as compared with that obtained by standard Kalman filter equations, and so, the validation gate size varies automatically with the maneuver of the target. The performance of this method is evaluated in terms of tracking success rates by computer simulation.

Target tracking for maneuvering reentry vehicles using multiple maneuvering models

This paper proposes a nonlinear filter design algorithm using multiple maneuvering models for tracking of a maneuvering reentry vehicle (MaRV) from its radar measurements. This filtering algorithm is discussed in terms of the fundamental problems of modeling accuracy, tracking errors with other conventional methodologies. Through several simulations, validity of this algorithm has been confirmed.

Target tracking algorithm in radar reference coordinates using coupled filters

This paper proposes a tracking algorithm in radar reference coordinates (RRC) using coupled filters. Tracking filters are discussed from the viewpoint of the fundamental problems of both modeling accuracy and real-time computational requirements. In most cases, if one becomes better, another gets worse. A trade-off between them always exists. Then our algorithm satisfies these conditions as much as possible by use of coupled filters. Through several simulations, validity of our algorithm has been confirmed in tracking non-maneuvering target.

A sequential detection method for moving targets under low SNR environments

This paper proposes a moving target detection algorithm using the sequential detection approach. Radar target detection under low SNR (signal-to-noise ratio) environments is known as a highly difficult problem. To resolve these subjects, sequential detection algorithm using position and Doppler frequency measurements are proposed. Through several simulations, validity of this algorithm has been confirmed.

A comparison of multiple model filters for maneuvering target tracking

Proposes a maneuvering target tracking algorithm using multiple model filters. This filtering algorithm is discussed in terms of tracking performance, tracking success rate and tracking accuracies for short sampling interval as compared with other conventional methodologies. Through several simulations, validity of this algorithm has been confirmed.

Target tracking using geographically separated radar

This paper proposes a multi-target tracking algorithm using geographically separated radar. The filtering algorithm is discussed in terms of the fundamental issues of tracking performance, tracking success rate and tracking errors as compared with other conventional methodologies. Through several simulations, the validity of this algorithm has been confirmed.