• Journal of Institute of Control, Robotics and Systems
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• v.9 no.10
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• pp.747-754
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• 2003

An enhancement of the probabilistic data association filter is presented for tracking a single maneuvering target in clutter environment. The use of the variable dimensional structure leads the probabilistic data association filter to adjust to real motion of a target. The detection of the maneuver for the model switching is performed by the acceleration estimates taken from a bias estimator of the two stage Kalman filter. The proposed algorithm needs low computational power since it is implemented with a single filtering procedure. A simple Monte Carlo simulation was performed to compare the performance of the proposed algorithm and the IMMPDA filter.

• The Journal of the Acoustical Society of Korea
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• v.38 no.4
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• pp.415-426
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• 2019

In this paper, we discussed automatic target tracking algorithms for diver detection sonar that observes penetration forces of coastal military installations and major infrastructures. First of all, we analyzed sea trial data in diver detection sonar and composed automatic target tracking algorithms based on track existence probability as track quality measure in clutter environment. In particular, these are presented track management algorithms which include track initiation, confirmation, termination, merging and target tracking algorithms which include single target tracking IPDAF (Integrated Probabilistic Data Association Filter) and multitarget tracking LMIPDAF (Linear Multi-target Integrated Probabilistic Data Association Filter). And we analyzed performances of automatic target tracking algorithms using sea trial data and monte carlo simulation data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3145-3162
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• 2023

As an important technology of the internetwork, wireless sensor network technique plays an important role in indoor localization. Non-line-of-sight (NLOS) problem has a large effect on indoor location accuracy. A location algorithm based on improved particle filter and directional probabilistic data association (IPF-DPDA) for WSN is proposed to solve NLOS issue in this paper. Firstly, the improved particle filter is proposed to reduce error of measuring distance. Then the hypothesis test is used to detect whether measurements are in LOS situations or NLOS situations for N different groups. When there are measurements in the validation gate, the corresponding association probabilities are applied to weight retained position estimate to gain final location estimation. We have improved the traditional data association and added directional information on the original basis. If the validation gate has no measured value, we make use of the Kalman prediction value to renew. Finally, simulation and experimental results show that compared with existing methods, the IPF-DPDA performance better.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.4
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• pp.33-42
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• 2013

This paper describes a multiple vehicle tracking algorithm using an integrated probabilistic data association filter (IPDAF) in urban environments. The algorithm consists of two parts; a pre-processing stage and an IPDA tracker. In the pre-processing stage, measurements are generated by a feature extraction method that manipulates raw data into predefined geometric features of vehicles as lines and boxes. After that, the measurements are divided into two different objects, dynamic and static objects, by using information of ego-vehicle motion. The IPDA tracker estimates not only states of tracks but also existence probability recursively. The existence probability greatly assists reliable initiation and termination of track in cluttered environment. The algorithm was validated by using experimental data which is collected in urban environment by using single laser scanner.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.141-146
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• 1992

Joint probabilistic data association filter(JPDAF) for multi-target tracking was developed for real-time implementation, while it abandoned an algorithm for track initiation. In this paper, we propose three features for track initiation that can be adapted to the JPDA filter. In addition, with the proposed approaches, the performance of track maintenance is evaluated in the case of tracks being near. To eliminate the abundant false tracks, we exploit the simple method using the state error covariances. Simulations are performed to demonstrate the efficiency of the proposed approaches.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.2
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• pp.166-175
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• 2002

In this paper. we predict tracking performance of the probabilistic strongest neighbor filter (PSNF). The PSNF is known to be consistent and superior to the probabilistic data association filter (PDAF) in both performance and computation. The PSNF takes into account the probability that the measurement with the strongest intensity in the neighborhood of the predicted target measurement location is not target-originated. The tracking performance of the PSNF is quantified in terms of its estimation error covariance matrix. The estimation error covariance matrix is approximately evaluated by using the hybrid conditional average approach (HYCA). We performed numerical experiments to show the validity of our performance prediction.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.5
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• pp.94-104
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• 2000

The accuracy and reliability of the target tracking is very critical issue in the design of automotive collision warning radar A significant problem in multi-target tracking (MTT) is the target-to-measurement data association If an incorrect measurement is associated with a target, the target could diverge the track and be prematurely terminated or cause other targets to also diverge the track. Most methods for target-to-measurement data association tend to coalesce neighboring targets Therefore, many algorithms have been developed to solve this data association problem. In this paper, a new multi-target data association method based on order statistics is described The new approaches. called the order statistics probabilistic data association (OSPDA) and the order statistics joint probabilistic data association (OSJPDA), are formulated using the association probabilities of the probabilistic data association (PDA) and the joint probabilistic data association (JPDA) filters, respectively Using the decision logic. an optimal or near optimal target-to-measurement data association is made A computer simulation of the proposed method in a heavy cluttered condition is given, including a comparison With the nearest-neighbor CNN). the PDA, and the JPDA filters, Simulation results show that the performances of the OSPDA filter and the OSJPDA filter are superior to those of the PDA filter and the JPDA filter in terms of tracking accuracy about 18% and 19%, respectively In addition, the proposed method is implemented using a developed digital signal processing (DSP) board which can be interfaced with the engine control unit (ECU) of car engine and with the d?xer through the controller area network (CAN)

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.235-243
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• 2000

The probabilistic data association filter(PDAF) is known to provide better tracking performance than the standard Kalman filter(KF) in a cluttered environment. In this paper, the stability of the PDAF of Fortmann et al[7], in the presence of uncertainties with regard to the origin of measurement, is investigated. The modified Riccati equation derived by approximating two random terms with their expectations is used to prove the stability of the PDAF. A new Lyapunov function based approach, which is different from the quantitative evaluation of Li and Bar-Shalom[7], is pursued. With the assumption that the system and observation noises are bounded, specific tracking error bounds are established.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.529-538
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• 2006

Separating a reentry vehicle into warhead and body is a conventional and efficient means of producing a huge decoy and increasing the kinetic energy of the warhead. This procedure causes the radar to track the body, whose radar cross section is larger, and ignore the warhead, which is the most important part of the reentry vehicle. However, the procedure is difficult to perform using standard tracking criteria. This study presents a novel tracking algorithm by integrating input estimation and modified probabilistic data association filter to solve this difficulty in a clear environment. The proposed algorithm with a new defined association probability in this filter provides a good tracking capability for the warhead ignoring the radar cross section. The simulation results indicate that the errors between the estimated and the warhead trajectories are reduced to a small interval in a short time. Therefore, the radar can produce a beam to illuminate to the right area and keep tracking the warhead all the way. In conclusion, this algorithm is worthy of further study and application.

• Journal of Ocean Engineering and Technology
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• v.37 no.1
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• pp.38-48
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• 2023

To maintain the existing systems of ships and introduce autonomous operation technology, it is necessary to improve situational awareness through the sensor fusion of the automatic identification system (AIS) and automatic radar plotting aid (ARPA), which are installed sensors. This study proposes an algorithm for determining whether AIS and ARPA signals are sent to the same ship in real time. To minimize the number of errors caused by the time series and abnormal phenomena of heterogeneous signals, a tracking method based on the combination of the unscented Kalman filter and probabilistic data association filter is performed on ARPA radar signals, and a position prediction method is applied to AIS signals. Especially, the proposed algorithm determines whether the signal is for the same vessel by comparing motion-related components among data of heterogeneous signals to which the corresponding method is applied. Finally, a measurement test is conducted on a training ship. In this process, the proposed algorithm is validated using the AIS and ARPA signal data received by the voyage data recorder for the same ship. In addition, the proposed algorithm is verified by comparing the test results with those obtained from raw data. Therefore, it is recommended to use a sensor fusion algorithm that considers the characteristics of sensors to improve the situational awareness accuracy of existing ship systems.