Multi sensor data fusion algorithms for target tracking using multiple measurements

Anitha, Raju; Renuka, S.; Abudhahir, A.

doi:10.1109/iccic.2013.6724283

Cited by 18 publications

(11 citation statements)

References 6 publications

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“…Multi-sensor fusion maneuvering target tracking is one of the subjects that has been investigated during the last decades [1,2]. Multiple sensors provide multi-source information such as distance, angel, audio, and image compared with a single sensor.…”

Section: Introductionmentioning

confidence: 99%

An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking

Luo

Wang

Chen

et al. 2020

Sensors

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In this paper, a new approach of multi-sensor fusion algorithm based on the improved unscented particle filter (IUPF) and a new multi-sensor distributed fusion model are proposed. Additionally, we employ a novel multi-target tracking algorithm that combines the joint probabilistic data association (JPDA) algorithm and the IUPF algorithm. To improve the real-time performance of the UPF algorithm for the maneuvering target, minimum skew simplex unscented transform combined with a scaled unscented transform is utilized, which significantly reduces the calculation of UPF sample selection. Moreover, a self-adaptive gain modification coefficient is defined to solve the low accuracy problem caused by the sigma point reduction, and the problem of particle degradation is solved by modifying the weights calculation method. In addition, a new multi-sensor fusion model is proposed, which better integrates radar and infrared sensors. Simulation results show that IUPF effectively improves real-time performance while ensuring the tracking accuracy compared with other algorithms. Besides, compared with the traditional distributed fusion architecture, the proposed new architecture makes better use of the advantages of radar and an infrared sensor and improves the tracking accuracy.

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Section: Introductionmentioning

confidence: 99%

An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking

Luo

Wang

Chen

et al. 2020

Sensors

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“…Due to the complementary characteristics of RADAR /IR, considerable research is done on their fusion. In References [6,7], RADAR /IR fusion algorithms are discussed, fusion of these two sensors improves the estimation accuracy. In Reference [6], authors compare two measurement fusion techniques (RADAR/IR) for 3D target tracking but this study is limited to a non-cluttered environment.…”

Section: Introductionmentioning

confidence: 99%

“…In Reference [ 6 ], authors compare two measurement fusion techniques (RADAR/IR) for 3D target tracking but this study is limited to a non-cluttered environment. In Reference [ 7 ], several multi sensor data fusion algorithms are discussed which are based on KF based techniques in a non-cluttered environment. Some fusion algorithms with application to the multiple passive sensor scenario with same characteristics are discussed in References [ 8 , 9 , 10 , 11 , 12 ]; whereas complementary characteristics of LADAR/IR fusion is discussed in References [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements

Shi

Qayyum

Memon

et al. 2020

Sensors

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Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same; consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique. The proposed modification includes a new gating strategy to fuse and select measurements from two sensors which originate from the same target. The state estimation performance is evaluated in terms of Root Mean Squared Error (RMSE) for both position and velocity, whereas, track retention statistics are evaluated to gauge the performance of the proposed tracking algorithm. The results clearly show that the proposed technique improves track retention and and false track discrimination (FTD).

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“…State fusion algorithm is similar to hierarchical architecture, while measurement fusion is similar to centralized architecture. They concluded that state fusion algorithm outperforms the other two [4]. In this paper, a hierarchical architecture is used to combine measurements from rotary encoder and ultrasonic sensor.…”

Section: Introductionmentioning

confidence: 99%

Data Fusion Method Based on Adaptive Kalman Filtering

Sirenden¹

2019

MST

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This paper discusses data fusion methods to combine the data from a rotary encoder and ultrasonic sensor. Both sensors are used in a micro-flow calibration system developed by the Research Center of Metrology LIPI. The methods studied are hierarchical data fusion and Kalman filtering. Three types of Kalman filters (KFs) are compared: the conventional Kalman filter and two adaptive Kalman filters. Moreover, a method to combine the uncertainty results from KF in hierarchical data fusion is proposed. The aim of this study is to find appropriate methods of data fusion that can be implemented in microflow calibration systems. Data from two experiment setups are used to compare the methods. The result indicates that one of the methods (with little adjustment) is more appropriate than the other. AbstractMetode Penggabungan Data Berdasarkan Adaptive Kalman Filtering. Makalah ini membahas tentang metode fusi data antara rotary encoder dan sensor ultrasonik. Kedua sensor yang digunakan pada sistem aliran kalibrasi mikro yang dikembangkan oleh Pusat Penelitian Metrologi LIPI (RCM-LIPI). Metode yang dikaji dalam makalah ini adalah fusi data hierarkis dan Kalman Filter. Tiga jenis Kalman Filter dibandingkan dalam makalah ini, konvensional dan dua metode adaptif. Makalah ini juga mengusulkan metode untuk menggabungkan hasil ketidakpastian dari Kalman Filter dalam fusi data yang hiearkis. Tujuannya adalah untuk menemukan metode yang tepat, serta dapat diimplementasikan untuk sistem aliran kalibrasi mikro. Data dari dua konfigurasi percobaan digunakan untuk membandingkan metode-metode tersebut. Hasilnya mengarah ke kesimpulan bahwa salah satu metode (dengan sedikit penyesuaian), lebih tepat daripada lainnya.

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Multi sensor data fusion algorithms for target tracking using multiple measurements

Cited by 18 publications

References 6 publications

An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking

An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking

A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements

Data Fusion Method Based on Adaptive Kalman Filtering

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