Investigation of Sensor Bias and Signal Quality on Target Tracking with Multiple Radars

Carniglia, Peter; Balaji, Bhashyam; Damini, Anthony

doi:10.1109/i2mtc48687.2022.9806656

Cited by 3 publications

(2 citation statements)

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“…� Given the assumption that a sufficient statistic is available, the association probabilities are computed only for the latest measurements. � The state estimation is done separately for each target [28].…”

Section: Reid Networkmentioning

confidence: 99%

“…In typical tracking algorithms, determining whether two position points belong to the same target is based solely on position information, which can result in low accuracy and reliance on filter performance. The JPDA algorithm is a classical method that addresses this issue and involves the following phases: The measurement‐to‐target association probabilities are computed jointly across the targets. Given the assumption that a sufficient statistic is available, the association probabilities are computed only for the latest measurements. The state estimation is done separately for each target [28]. …”

Section: Proposed Hdt Networkmentioning

confidence: 99%

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HDT network: A high‐resolution range profile multi‐target detection and tracking method based on neural network

Sun

Liu

Wang

et al. 2023

IET Radar Sonar & Navi

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In radar target detection and tracking tasks, the detection algorithm and data association algorithm are the primary technologies. The accuracy of detection, stability of tracking and processing speed are the key points to accomplishing effective high‐resolution range profile (HRRP) multi‐target tracking (MTT). Classic HRRP target tracking algorithms conduct the detection by extracting handcraft features and conduct tracking based on the data association algorithm. With the development of neural network methods, deep learning methods have been widely applied in target detection and tracking. An HRRP detection and tracking (HDT) network based on neural networks is proposed, which includes three steps: Firstly, the HRRP signal is processed by a convolutional neural network detector to extract the feature of the origin signal and generate the detection measurement. Secondly, a predicted measurement is generated by a Kalman filter, which estimates the current state of the target based on its previous states and the detection measurement. Finally, the ReID network calculates the cosine similarity between the tracks and measurements to associate them and a linear sum assignment operation is used to match the tracks and measurements. By using the proposed algorithm, the HDT network is capable of detecting and tracking multiple targets in complex cluttered environments with high accuracy. Experimental results on an HRRP dataset collected by a moving radar platform show the outstanding performance of the HDT network, including detection accuracy, track integrity and real‐time MTT.

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Section: Reid Networkmentioning

confidence: 99%