• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.11
• /
• pp.1551-1558
• /
• 2014

We propose a multi-rate sensor fusion of vision and radar using Kalman filter to solve problems of asynchronized and multi-rate sampling periods in object vehicle tracking. A model based prediction of object vehicles is performed with a decentralized multi-rate Kalman filter for each sensor (vision and radar sensors.) To obtain the improvement in the performance of position prediction, different weighting is applied to each sensor's predicted object position from the multi-rate Kalman filter. The proposed method can provide estimated position of the object vehicles at every sampling time of ECU. The Mahalanobis distance is used to make correspondence among the measured and predicted objects. Through the experimental results, we validate that the post-processed fusion data give us improved tracking performance. The proposed method obtained two times improvement in the object tracking performance compared to single sensor method (camera or radar sensor) in the view point of roots mean square error.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.45 no.12
• /
• pp.1076-1083
• /
• 2017

This paper discusses the error estimation in radar measurement data obtained while tracking a launch vehicle. It is known that typical radar measurement data consist of the true positional or orientation information on the vehicle being tracked, random noise and a deterministic bias due to radio refraction. Unlike previous research works, this paper proposes a tracking-error (mainly bias) estimation method solely based on the single radar measurement with no aid of other measurement such as GPS. The proposed method has been verified with real measurement data obtained while tracking the KSLV-I launch vehicle.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.8
• /
• pp.633-642
• /
• 2016

It is important for advanced active safety systems and autonomous driving cars to get the accurate estimates of the nearby vehicles in order to increase their safety and performance. This paper proposes a sensor fusion method for radar and vision sensors to accurately estimate the state of the preceding vehicles. In particular, we performed a study on compensating for the lateral state error on automotive radar sensors by using a vision sensor. The proposed method is based on the Interactive Multiple Model(IMM) algorithm, which stochastically integrates the multiple Kalman Filters with the multiple models depending on lateral-compensation mode and radar-single sensor mode. In addition, a Probabilistic Data Association Filter(PDAF) is utilized as a data association method to improve the reliability of the estimates under a cluttered radar environment. A two-step correction method is used in the Kalman filter, which efficiently associates both the radar and vision measurements into single state estimates. Finally, the proposed method is validated through off-line simulations using measurements obtained from a field test in an actual road environment.

• Journal of the Society of Naval Architects of Korea
• /
• v.60 no.4
• /
• pp.248-255
• /
• 2023

In order to implement the autonomous navigation function, it is essential to track an object within a certain radius of the ship's route. This paper proposes the Multiple Probabilistic Data Association Filter (MPDAF), which can track multiple ships by extending Probabilistic Data Association Filter (PDAF), an existing single object tracking algorithm, using radar data obtained from real marine environments. The proposed MPDAF algorithm was developed to address the problem of tracking multiple objects in a complex environment where there can be significant uncertainty in the number and identification of objects to be tracked. Using real-world radar data provided by the German aerospace center (DLR), it has been verified that the proposed algorithm can track a large number of objects with a small position error.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.1
• /
• pp.151-156
• /
• 2006

Accurate vessel traffic observation is indispensable to carry out vessel traffic management, design of vessel traffic route, planning of port construction, etc. In order to observe the vessel traffic accurately without many efforts such as the use of a ship or car equipped with special radar observation system and the preparation of observation staff, the authors have been developing completely automated remote radar/AIS network system covering the main traffic area in Tokyo Bay. The composite radar image observed at Yokosuka and Kawasaki radar stations with AIS information can be seen on web site of Internet. In addition to the development of radar/AIS observation system, the software to analyze observed vessel traffic flow has been developed. This software has various functions such as automatic tracking of ship's positions, automatic estimation of ship's size, automatic integration of radar image and AIS data, animation of ships' movements, extraction of dangerous ship encounters, etc. The configuration and functions of the developed remote radar/AIS network system are shown first in this paper. Then various functions of the software to analyze vessel traffic are introduced, and some analyzed results on the vessel traffic in Tokyo Bay are described demonstrating the effectiveness of the developed system.

• Atmosphere
• /
• v.28 no.4
• /
• pp.443-455
• /
• 2018

Nowcasting algorithms using weather radar data are mostly based on extrapolating the radar echoes. We estimate the echo motion vectors that are used to extrapolate the echo properly. Therefore, understanding the general characteristics of these motion vectors is important to improve the performance of nowcasting. General characteristics of radar-based motions are analyzed for warm season precipitation over Jeju region. Three-year summer season data (June~August, 2011~2013) from two radars (GSN, SSP) in Jeju are used to obtain echo motion vectors that are retrieved by Variational Echo Tracking (VET) method which is widely used in nowcasting. The highest frequency occurs in precipitation motion toward east-northeast with the speed of $15{\sim}16m\;s^{-1}$ during the warm season. Precipitation system moves faster and eastward in June-July while it moves slower and northeastward in August. The maximum frequency of speed appears in $10{\sim}20m\;s^{-1}$ and $5{\sim}10m\;s^{-1}$ in June~July and August respectively while average speed is about $14{\sim}15m\;s^{-1}$ in June~July and $8m\;s^{-1}$ in August. In addition, the direction of precipitation motion is highly variable in time in August. The speed of motion in Lee side of the island is smaller than that of the windward side.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.20 no.4
• /
• pp.482-490
• /
• 2017

The radar signal processing procedure is divided into the pre-processing such as frequency down converting, down sampling, pulse compression, and etc, and the post-processing such as doppler filtering, extracting target information, detecting, tracking, and etc. The former is generally designed using FPGA because the procedure is relatively simple even though there are large amounts of ADC data to organize very quickly. On the other hand, in general, the latter is parallel processed by multiple DSPs because of complexity, flexibility and real-time processing. This paper presents the radar signal processor design using FPGA which includes not only the pre-processing but also the post-processing such as doppler filtering, bore-sight error, NCI(Non-Coherent Integration), CFAR(Constant False Alarm Rate) and etc.

• Journal of Advanced Navigation Technology
• /
• v.18 no.1
• /
• pp.29-34
• /
• 2014

Data fusion techniques combine data from multiple radars and related information to achieve more accurate estimations than could be achieved by a single, independent radar. In this paper, we analyze delay and loss of packets to be processed by multiple radar and minimize data processing interval from centralized data processing operation as fusing multiple radar data. Therefore, we model radar network about central data fusion, and analyze delay and loss of packets inside queues on assuming queues respectively as the M/M/1/K using NS-2. We confirmed average delay time, processing fused multiple radar data, through the analysis data. And then, this delay time can be used as a reference time for radar data latency in fusion center.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.29 no.1 s.232
• /
• pp.139-144
• /
• 2005

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion. have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• Aerospace Engineering and Technology
• /
• v.11 no.2
• /
• pp.171-181
• /
• 2012

As a sub-system of an air traffic control system, SDP(Surveillance Data Processor) provides with the system tracks of aircraft using the surveillance sensor data from various air traffic surveillance sensors, such as radars. Therefore, the high accuracy of tracking results is a crucial requirement for safe flights, and verification of the required system performance of SDP is an essential step in development. Moreover, the quantitative evaluation of target tracking accuracy is important for newly developed SDP, since there are several tracking methods for Multi-Sensor Multi-Target Tracking, such as MRT(Multi Radar Tracking), inevitably required as the main function of SDP. In this study, definition of required system performances, establishment of test environment, and test results for MRT performance evaluation of SDP, which is being developed in KARI(Korea Airspace Research Institute) are presented.