S. Tsujimichi

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.

Sensor-to-sensor target association in a network of passive sensors

This paper address the problem of sensor-to sensor target association in a distributed passive sensor network. If two or more targets are coplanar with two sensors, ghosts occur among the intersections of line-of-sights. To cancel these ghosts, the authors propose a new association method in which they track angles and angular rates of targets being viewed by each sensor and use not only angle but also angular rate information in the association process. The performance of the proposed method is investigated through Monte Carlo simulation.

Tracking a three-dimensional moving target with two-dimensional angular measurements from multiple passive sensors

This paper addresses the problem of target tracking with angular measurements from distributed passive sensors. The usual triangulation method requires that the sensors observe a moving target synchronously to determine the target range. This paper proposes a new method which is applicable for the situation where the sensors observe the target asynchronously. The method assumes that each sensor observes two-dimensional angle, i.e., azimuth and elevation, and the target position and velocity in a three-dimensional space are to be estimated.

Target tracking with time-delayed data in multiple radar system

The problem of target tracking in a distributed radar system is considered. Measurements about target trajectory obtained by each radar are transmitted to a fusion center. One of the difficult problems encountered when we apply the well-known Kalman filter technique to these systems is a possibility of data out of sequence caused by time delay. We propose a filtering algorithm which can perform a negative-time update to resolve this problem. Our algorithm is an extension of the Kalman filter and is derived using a minimum mean-square error criterion. The performance of the proposed algorithm is investigated through Monte Carlo simulations.

Multisensor vehicle tracking method for intelligent highway system

Presents a vehicle tracking method which fuses data from an image sensor and radar installed on the roadside. The image sensor is widely used for road monitoring systems, but it is difficult to detect a vehicle in poor visibility. On the other hand, millimeter waves are less attenuated by fog, rain and snow. Consequently, it is possible to detect vehicles in all weather by using radar together with the image sensor. As for observation accuracy, the image sensor is superior in the accuracy of angle measuring. On the other hand, radar is superior in accuracy for range measurement. By utilizing these various features, therefore, the tracking performance is improved. This method adopts a correlation technique that uses a likelihood of range rate observed by radar, in addition to a likelihood of position, so that this method is generally able to track the vehicles from observation vectors even if false detection occurs. The performance of this method is evaluated by simulations.

Computation-time reduction of track oriented multiple hypothesis tracking

In recent years MHT (multiple hypothesis tracking) attracts much attention because of its tracking performance. MHT has heavy computation load problem and an approach for computation time reduction by applying N-best algorithm was reported. We have improved the function of MHT and named the improved algorithm as track oriented MHT. It has different data structure from original Reids MHT d(1984), so N-best algorithm can not be applied to it straightforwardly. We show the applying method of N-best algorithm to track oriented MHT. And also we show the efficiency of the method by comparing the computation time in a simulation.

A study of target tracking using track-oriented multiple hypothesis tracking

MHT (multiple hypothesis tracking) is a multiple target tracking methodology in dense environments. While conventional tracking filters have only the function of track maintenance, MHT has the function of track initiation and mis-track removal in addition to the above function. In the conventional MHT, the extraction of target track is very complicated, because the track information is referred to by using combination of hypotheses and observation vectors. However, track-oriented MHT, our proposed methodology, generates hypotheses based on the correlation of tracks and observation vectors and, therefore, extraction of the tracks is facilitated. The paper presents the study of target tracking using track-oriented MHT.

Multiple manoeuvre model track-oriented MHT (multiple hypothesis tracking)

The problem of multiple-target tracking in a dense environment using the MHT (multiple hypothesis tracking) or the JPDA (joint probabilistic data association) has received a lot of attention in recent years. This paper provides a new technique for manoeuvring target tracking in a dense environment, using the track-oriented MHT which is a significant improvement of the original measurement-oriented MHT. This paper has shown the condition that this technique is equivalent to the M/sup 3/JPDA (multiple manoeuvre model JPDA). Here, the M/sup 3/JPDA for manoeuvring multitarget tracking is the extension and improvement of the conventional JPDA.

Track-initiation with the measurements of optical sensors by using MHT techniques

The fundamental problem of target tracking in a dense multiple-target environment with clutter, false alarms, missed detections, and so forth is the uncertain origin of measurements. In such a dense multiple-target environment, there is a need to perform track-initiation procedure by which the track of each target is detected. After we perform the track-initiation procedure, we can perform track-maintenance procedure. In this paper, we propose a new track-initiation method using measurements obtained by the optical sensors in a multiple-target environment. Moreover, we estimate the performance of the proposed method.

Tracking of a highly maneuvering target using a radar multisensor and an angle sensor

Tracking of highly maneuvering targets in a cluttered environment is a difficult problem. It has been previously reported that the target motion model defined as superposition of multiple constant acceleration vectors on a constant velocity straight line motion model using an automatic optimization of the target correlation gate in response to the target maneuvering based on the Multiple Maneuver Model Probabilistic Data Association (M3PDA) proved to be an effective method for severely cluttered conditions. On the other hand, due to the recent developments in the area of sensor technologies it became possible to use highly accurate angle sensors (such as infrared sensors) for target tracking. It is expected that the use of such sensors will greatly improve the tracking performance of radar systems. In this study, we propose an algorithm based on an expanded M3PDA formed by the six-dimensional state vector consisting of the target position and velocity components using three-dimensional observation data from the radar and two-dimensional observation data from angle sensors. We also demonstrate the advantages of the proposed method compared to conventional ones using a simulation of tracking of one maneuvering target.