Underwater Multi-target Tracking Research Articles

Multi-target tracking algorithm in aquaculture monitoring based on deep learning

In order to analyze fish behavior, real-time underwater fish monitoring is essential. This work presents an underwater multi-target tracking algorithm for aquaculture monitoring, utilizing deep learning techniques. Underwater images are acquired using an underwater robot, and the images are defogged using the multi-scale Retinex algorithm based on HSV space. To enhance object detection performance, GhostNetv2 was integrated into the You Only Look Once version 5 (YOLOv5) object detection algorithm, supplemented by the addition of the Coordinate Attention (CA) module, resulting in the development of GN-YOLOv5. For the tracking algorithm, the more accurate Generalized Intersection over Union (GIoU) method was incorporated into the StrongSORT tracking algorithm. Moreover, to achieve more precise target tracking, a fish re-identification model was established. The proposed algorithms were evaluated through experiments conducted on various datasets, including VOC 2012, MOT16, and self-built datasets. The results demonstrate notable improvements: the GN-YOLOv5 model showed a 32.91% reduction in parameters and a 3.22% increase in precision. Furthermore, the enhanced StrongSORT algorithm exhibited a 3.84% increase in MOTA, a 28.00% reduction in IDS, and a speed boost of 7 FPS, leading to stable and accurate multi-target fish tracking.

Multi-target tracking in underwater multistatic AUV networks with a robust Poisson Multi-Bernoulli filter

The study investigates underwater multi-target tracking (MTT) in a multistatic system using autonomous underwater vehicles (AUVs) as receivers separately from transmitters. The most critical aspect of MTT is the accurate extraction of target tracks from the collected measurements, which can be affected by clutter, missed detections, and port-starboard ambiguities. Moreover, the detection parameters—the target-detection probability and clutter rate—used in MTT methods are typically unknown and change with varying environmental conditions and geometric distributions of the AUV, target, and sound transmitter. In view of these challenges, we propose a robust multi-sensor Poisson Multi-Bernoulli Mixture (R-MS-PMBM) filter immune to unknown detection parameters that treats clutter rates and detection probabilities as continuous random variables modeled by beta and gamma distributions, respectively. The R-MS-PMBM filter is a conjugate prior, and its closed form is derived. We introduce a measurement-driven adaptive birth model to initiate target tracks quickly and efficiently. Furthermore, a suboptimal Gibbs sampler is used to reduce the computational complexity of its practical implementation. The simulation results show that the proposed filter is resilient to unknown detection parameters and can effectively perform in challenging underwater scenarios.

Fast Track Smoothing Algorithm for Underwater Target Tracking Based on KD Tree

A fast-track smoothing algorithm based on the KD Tree is proposed to address the problem of state estimation in underwater multi-target tracking. For multi-target tracking, the multi-Bernoulli filter becomes intractable due to the high-dimensional nature of the posterior. This problem can be overcome by the particle filter (PF), which approximates the posterior by a set of weighted samples (“particles”). The probability distribution of the multi-Bernoulli algorithm based on this algorithm contains a series of particles with weights. Therefore, while smoothing it, the state transfer probability of each particle must be computed, and the state transfer function must be calculated, which is related to the difference in states between the k-1 moment and the k moment, we refer to this difference as the state distance. However, the KD Tree can be used to divide the particle set into different state spaces according to the particle states, and thus calculate the state distances more efficiently. The remarkable feature of this algorithm is that the time complexity is greatly reduced from that O(N2) of the traditional algorithm to O(NlogN). This paper implements the proposed method using particles, the improved algorithm based on KD tree effectively mitigates the disadvantages of previous forward-backward smoothing algorithms, and significantly reduces the computation time while improving the system state estimation precision. Experimental results of simulation analysis and the optimal sub-patten assignment distance is employed as an assessment criterion to validate the algorithm’s performance.

A Distributed Underwater Multi-Target Tracking Algorithm Based on Two-Layer Particle Filter

Underwater multi-target tracking is one of the key technologies for military missions, including patrol and combat in the crucial area. Since the underwater environment is complex and targets’ trajectories may intersect when they are in a dense area, it is challenging to guarantee the precision of observed information. In order to provide high-precision underwater localization and tracking services over an underwater monitoring network, a dynamic network resource allocation mechanism and an underwater multi-target tracking algorithm based on a two-layer particle filter with distributed probability fusion (TLPF-DPF) are proposed. The position estimation model based on geometric constraints and the dynamic allocation mechanism of network resources based on prior position estimation are designed. Using the improved filtering algorithm with known initial states, the reliable tracking of multiple targets with trajectory intersection in a small area under complex noises is achieved. In the non-Gaussian environment, the average positioning error of TLPF-DPF is less by nearly 30% than alternative algorithms. When switching from a Gaussian environment to a non-Gaussian environment, the performance degradation of TLPF-DPF is less than 12%, which exhibits stability compared with other algorithms when targets are close to each other with crossing trajectories.

Underwater Multitarget Tracking With Sonar Images Using Thresholded Sequential Monte Carlo Probability Hypothesis Density Algorithm

This letter presents a multi-target tracking algorithm—thresholded Sequential Monte Carlo probability hypothesis density (TH-SMC-PHD) algorithm. The TH-SMC-PHD aims to overcome the problem that underwater multi-target tracking is prone to missing tracking on sonar images, resulting in the breakage of trajectories. First, CA-CFAR and K-means are employed to detect potential underwater targets from sonar images, respectively. Then TH-SMC-PHD is applied to the detection results for tracking, using a continuously lost frame threshold to reduce the missing tracking rate and the Minimum-Sampling-Variance (MSV) resampling to improve tracking accuracy. An actual underwater multi-target tracking experiment using a forward-looking sonar was contracted in a water tank to evaluate the tracking performance. Compared with the other two PHD tracking algorithms, the results demonstrate that the proposed algorithm achieves high-precision, non-fracture, non-missing tracking of three targets. In addition, the TH-SMC-PHD is more stable and less affected by different detection algorithms, which has the potential for practical applications.

Underwater Multi-Target Tracking Algorithm Based on Deep Learning

Underwater Multi-Target Tracking Algorithm Based on Deep Learning

Unconstrained underwater multi-target tracking in passive sonar systems using two-stage PF-based technique

A robust particle filter (PF)-based multi-target tracking solution for passive sonar systems able to track an unknown time-varying number of multiple targets, while keeping continuous tracks of such targets, is presented in this article. PF is a nonlinear filtering technique that can accommodate arbitrary sensor characteristics, motion dynamics and noise distributions. An enhanced version of PF is employed and is called Mixture PF. The commonly used sampling/importance resampling PF samples from the prior importance density, while Mixture PF samples from both the prior and the observation likelihood. In order to be able to track an unknown time-varying number of multiple targets, two Mixture PFs are used, one for target detection and the other for tracking multiple targets, and a density-based clustering technique is used after the first filter. This article demonstrates the applicability of the proposed technique for the passive problem, which suffers from the lack of measurements and the small detection range of the buoys, especially for weak signals. A contact-level simulation was used to generate different scenarios and the performance of the proposed technique called Clustered-Mixture PF was examined with either bearing measurement only or bearing and Doppler measurements, and it demonstrated its high performance.

Clustered Mixture Particle Filter for Underwater Multitarget Tracking in Multistatic Active Sonobuoy Systems

The problem of multitarget tracking in underwater multistatic active sonobuoy systems is challenging because of the large number of false contacts and multiple reflections that reach the receivers. Targeting a robust solution that can track an unknown time-varying number of multiple targets, while keeping continuous tracks even in scenarios with large number of false contacts per ping, a particle filter (PF)-based technique is proposed in this paper. The PF is a nonlinear filtering technique that can accommodate arbitrary sensor characteristics, motion dynamics, and noise distributions. An enhanced version of the PF called the mixture PF is utilized in this paper. While the sampling/importance resampling PF samples from the prior importance density and weights the particles according to the observation likelihood, the mixture PF samples from both importance densities and weights the different groups of particles respectively. The usage of this mixture of importance densities provides better performance and faster convergence to the true targets locations. In order to track an unknown time-varying number of targets, two mixture PFs are used (one for target detection and the other for tracking multiple targets), and a density-based clustering technique. The first filter starts with random uniformly distributed samples over the surveillance area and resets every five pings. Just before the reset, the clustering technique runs to detect the clusters that corresponds to different targets and passes them to the second filter. The performance of the proposed technique is examined and demonstrated by different simulated scenarios and some real datasets from the SEABAR07 trial by the NATO Underwater Research Center.

Underwater multiple target tracking decision making based on an analytic network process

Underwater multi-target tracking logic and decision (UMTLD) has difficulty resolving multi-target tracking problems for underwater vehicles. Present methods assume factors in UMTLD are uncorrelated, when these are actually in a complex, interdependent relationship. To provide this, an index set of multi-target tracking decision characteristics and an analytic network process (ANP) model of the UMTLD method was established. This method brings the index set of multi-target tracking decision into the ANP model, and the optimization multitarket tracking decision is achieved via computation of the resulting supermatrix. The rationality and robustness of decision results increase in simulations by 13% and 47% respectively with analytic hierarchy process (AHP). These results indicate that the ANP method should be the preferred method when UMTLD factors are interdependent.

Efficient and Enhanced Multi-Target Tracking with Doppler Measurements

In many radar and sonar tracking systems, the target state typically includes target position and velocity components that are estimated from a time sequence of target position and Doppler measurements. The use of measured Doppler information directly in the target trajectory estimation leads to a nonlinear filter implementation such as the extended Kalman filter (EKF), particle filter etc. We investigate a method for including Doppler measurements as part of the data association process and then assess the benefits of this approach. It is well understood that data association performance can dominate the total performance of a tracker that is designed to track targets in the presence of clutter. In this case, the Doppler component of the measurements may be used in combination with the target position measurement as an additional discriminant of measurement origin. We have developed a simple but efficient Doppler data association (DDA) method which utilises both position and Doppler measurements for single and multi-target tracking. If the Doppler measurements are not used in trajectory state estimation, then the nonlinear filters for the incorporation of Doppler measurements are not required, however a significant improvement in tracking performance is still observed. The proposed DDA method is demonstrated using both the linear multi-target integrated probabilistic data association algorithm (LMIPDA) and the linear multi-target integrated track splitting algorithm (LMITS) in an active sonar underwater multi-target tracking scenario.