Deep-Learning-Based Multiple Model Tracking Method for Targets with Complex Maneuvering Motion

Abstract

The effective detection of unmanned aerial vehicle (UAV) targets is of great significance to guarantee national military security and social stability. In recent years, with the development of communication and control technology, the movement of UAVs has become increasingly flexible and complex, presenting diverse trajectory forms and different motion models in different phases. The Gaussian mixture probability hypothesis density filter incorporating the linear Gaussian jump Markov system approach (LGJMS-GMPHD) provides an efficient method for tracking multiple maneuvering targets, as applied to the switching of motions between a set of models in a Markovian chain. However, in practice, the motion model parameters of targets are generally unknown and the model switching is uncertain. When the preset filtering model parameters are mismatched, the tracking performance is dramatically degraded. In this paper, within the framework of the LGJMS-GMPHD filter, a deep-learning-based multiple model tracking method is proposed. First, an adaptive turn rate estimation network is designed to solve the filtering model mismatch caused by unknown turn rate parameters in coordinate turn models. Second, a filter state modification network is designed to solve the large tracking errors in the maneuvering phase caused by uncertain motion model switching. Finally, based on simulations of multiple maneuvering targets in cluttered environments and experimental field data verification, it can be concluded that the proposed method has strong adaptability to multiple maneuvering forms and can effectively improve the tracking performance of targets with complex maneuvering motion.

Hybrid Dual-Scale Neural Network Model for Tracking Complex Maneuvering UAVs

“…, m p , and k ≤ min(n p, m p ); (u 1,2 k , v k ) indicates that two tracks with different prediction scales match the first and middle points in the groundtruth track; and n p and m p , respectively, represent the number of data points in the two tracks. Set δ i,j to the dual-scale prediction of the distance between the first point u 1,2 k in the track and the point v k in the ground-truth track, that is, δ 1,2 i,j = d( p1,2 i , p j ). OSPA distance can be used to determine whether the middle segment of two tracks predicted by a dual-scale neural network deviates.…”

“…Unmanned aerial vehicles (UAVs) are integral in various fields, including aerial photography, logistics, search, and rescue missions [1]. Tracking moving targets is essential to ensure effective UAV mission execution, maintain steady tracking of the target, and provide the necessary data and information for decision-making [2]. However, the maneuvering performance and behavior of UAVs may be affected by a variety of factors, such as wind, manipulator's intent, and environmental influences, resulting in varied and unpredictable motion patterns.…”

An Interactive Transient Model Correction Active Sonar Target Tracking Method

“…Their research suggests that under high measurement error conditions, the IMM-UKF algorithm has higher tracking accuracy than the IMM-EKF algorithm. In recent years, the algorithm based on depth learning and neural networks has been applied to underwater target tracking to solve model mismatches and other problems and improve tracking stability [33][34][35].…”

A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification

“…Moreover, SAR data have been used in diverse applications, including fish species recognition [8], urban land classification [9], marine spatial planning [10], and earthquake disaster prediction [11]. To further broaden the versatility of SAR technology and highlight the potential to contribute to an array of scientific and practical domains, geophysical researchers have employed machine learning and deep learning techniques to support fish species recognition, military objective protection, and natural hazard prevention [12][13][14]. However, SAR images inherently exhibit speckle noise due to the attenuation of echo signals, which is characterized by a distribution of granular patterns [15].…”