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.

Novel method for identifying the stages of discharge underwater based on impedance change characteristic

It is difficult to determine the discharge stages in a fixed time of repetitive discharge underwater due to the arc formation process being susceptible to external environmental influences. This paper proposes a novel underwater discharge stage identification method based on the Strong Tracking Filter(STF) and impedance change characteristics. The time-varying equivalent circuit model of the discharge underwater is established based on the plasma theory analysis of the impedance change characteristics and mechanism of the discharge process. The STF is used to reduce the randomness of the impedance of repeated discharges underwater, and then the universal identification resistance data is obtained. Based on the resistance variation characteristics of the discriminating resistance of the pre-breakdown, main, and oscillatory discharge stages, the threshold values for determining the discharge stage are obtained. These include the threshold values for the resistance variation rate(K) and the moment(t).Experimental and error analysis results demonstrate the efficacy of this innovative method in discharge stage determination, with a maximum mean square deviation of Scrless than 1.761.

An Underwater Robot Inspection Anomaly Localization Feedback System Based on Sonar Technology

This article introduces an underwater robot inspection anomaly localization feedback system comprising a real-time water surface tracking,detection,and positioning system located on the water surface,while the underwater robot inspection anomaly feedback system is housed within the underwater robot.The system facilitates the issuance of corresponding mechanical responses based on the water surface’s real-time tracking,detection,and positioning,enabling recognition and feedback of anomaly information.Through sonar technology,the underwater robot inspection anomaly feedback system monitors the underwater robot in real-time,triggering responsive actions upon encountering anomalies.The real-time tracking,detection,and positioning system from the water surface identifies abnormal conditions of underwater robots based on changes in sonar images,subsequently notifying personnel for necessary intervention.

Joint target assignment and power allocation in the netted C-MIMO radar when tracking multi-targets in the presence of self-defense blanket jamming

The netted radar system(NRS)has been proved to possess unique advantages in anti-jamming and improving target tracking performance.Effective resource management can greatly ensure the combat capability of the NRS.In this paper,based on the netted collocated multiple input multiple output(CMIMO)radar,an effective joint target assignment and power allocation(JTAPA)strategy for tracking multi-targets under self-defense blanket jamming is proposed.An architecture based on the distributed fusion is used in the radar network to estimate target state parameters.By deriving the predicted conditional Cramer-Rao lower bound(PC-CRLB)based on the obtained state estimation information,the objective function is formulated.To maximize the worst case tracking accuracy,the proposed JTAPA strategy implements an online target assignment and power allocation of all active nodes,subject to some resource constraints.Since the formulated JTAPA is non-convex,we propose an efficient two-step solution strategy.In terms of the simulation results,the proposed algorithm can effectively improve tracking performance in the worst case.

Multi-Target Tracking of Person Based on Deep Learning

To improve the tracking accuracy of persons in the surveillance video,we proposed an algorithm for multi-target tracking persons based on deep learning.In this paper,we used You Only Look Once v5(YOLOv5)to obtain person targets of each frame in the video and used Simple Online and Realtime Tracking with a Deep Association Metric(DeepSORT)to do cascade matching and Intersection Over Union(IOU)matching of person targets between different frames.To solve the IDSwitch problem caused by the low feature extraction ability of the Re-Identification(ReID)network in the process of cascade matching,we introduced Spatial Relation-aware Global Attention(RGA-S)and Channel Relation-aware Global Attention(RGA-C)attention mechanisms into the network structure.The pre-training weights are loaded for Transfer Learning training on the dataset CUHK03.To enhance the discrimination performance of the network,we proposed a new loss function design method,which introduces the Hard-Negative-Mining way into the benchmark triplet loss.To improve the classification accuracy of the network,we introduced a Label-Smoothing regularization method to the cross-entropy loss.To facilitate the model’s convergence stability and convergence speed at the early training stage and to prevent the model from oscillating around the global optimum due to excessive learning rate at the later stage of training,this paper proposed a learning rate regulation method combining Linear-Warmup and exponential decay.The experimental results on CUHK03 show that the mean Average Precision(mAP)of the improved ReID network is 76.5%.The Top 1 is 42.5%,the Top 5 is 65.4%,and the Top 10 is 74.3%in Cumulative Matching Characteristics(CMC);Compared with the original algorithm,the tracking accuracy of the optimized DeepSORT tracking algorithm is improved by 2.5%,the tracking precision is improved by 3.8%.The number of identity switching is reduced by 25%.The algorithm effectively alleviates the IDSwitch problem,improves the tracking accuracy of persons,and has a high practical value.

Neural Network Based Robust Controller for Trajectory Tracking of Underwater Vehicles

A robust neural network controller （NNC） is presented for tracking control of underwater vehicles with uncertainties. The controller is obtained by using backstepping technique and Lyapunov function design in combination with neural network identification. Modeling errors and environmental disturbances are considered in the mathematical model. A twolayer neural network is introduced to compensate the modeling errors, while H∞ control strategy is used to achieve the L2-gain performance. The uniformly ultimately bounded （UUB） stabilities of tracking errors and NN weights are guaran- teed through the proposed controller. An on-line NN weights tuning algorithm is also propesed. Good performances of the tracking control system are illustrated bv the results of numerical simulations.

A Track Initiation Method for the Underwater Target Tracking Environment

A novel efficient track initiation method is proposed for the harsh underwater target tracking environment(heavy clutter and large measurement errors): track splitting, evaluating, pruning and merging method(TSEPM). Track initiation demands that the method should determine the existence and initial state of a target quickly and correctly.Heavy clutter and large measurement errors certainly pose additional difficulties and challenges, which deteriorate and complicate the track initiation in the harsh underwater target tracking environment. There are three primary shortcomings for the current track initiation methods to initialize a target:(a) they cannot eliminate the turbulences of clutter effectively;(b) there may be a high false alarm probability and low detection probability of a track;(c) they cannot estimate the initial state for a new confirmed track correctly. Based on the multiple hypotheses tracking principle and modified logic-based track initiation method, in order to increase the detection probability of a track,track splitting creates a large number of tracks which include the true track originated from the target. And in order to decrease the false alarm probability, based on the evaluation mechanism, track pruning and track merging are proposed to reduce the false tracks. TSEPM method can deal with the track initiation problems derived from heavy clutter and large measurement errors, determine the target’s existence and estimate its initial state with the least squares method. What’s more, our method is fully automatic and does not require any kind manual input for initializing and tuning any parameter. Simulation results indicate that our new method improves significantly the performance of the track initiation in the harsh underwater target tracking environment.

Seam Tracking Technology for Hyperbaric Underwater Welding

Automatic weld seam tracking technology to be used in hyperbaric underwater damaged pipeline repair welding is much more important, because of poor bevel preparation and severe working condition. A weld seam tracking system based on digital signal processing（DSP） passive light weld image processing technology has been established. A convenient charge coupled device（CCD） camera system was used in the high pressure environment with the help of an aperture and focus altering mechanism to guarantee overall image visibility in the scope of pressure below 0.7 MPa. The system can be used in the hyperbaric environment to pick up the real welding image of both the welding arc and the welding pool. The newly developed DSP technology was adopted to achieve the goal of system real time characteristics. An effective weld groove edge recognition technique including narrow interesting window opening, middle value wave filtering, Sobel operator weld edge detecting and edge searching in a defined narrow area was proposed to remove the guide error and system accuracy was ensured. The results of tracking simulation and real tracking application with arc striking have proved the validity and the accuracy of the mentioned system and the image processing method.

Method for Underwater Target Tracking Based on an Interacting Multiple Model

According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm based on fuzzy logic inference （FIMM） is proposed. Maneuvering patterns of the target are represented by model sets, including the constant velocity model （CA）, the Singer mode~, and the nearly constant speed horizontal-turn model （HT） in FIMM technology. The simulation results show that compared to conventional IMM, the reliability and real-time performance of underwater target tracking can be improved by FIMM algorithm.

Nonlinear trajectory tracking control of a new autonomous underwater vehicle in complex sea conditions

Autonomous underwater vehicles (AUVs) navigating in complex sea conditions usually require a strong control system to keep the fastness and stability. The nonlinear trajectory tracking control system of a new AUV in complex sea conditions was presented. According to the theory of submarines,the six-DOF kinematic and dynamic models were decomposed into two mutually non-coupled vertical and horizontal plane subsystems. Then,different sliding mode control algorithms were used to study the trajectory tracking control. Because the yaw angle and yaw angle rate rather than the displacement of the new AUV can be measured directly on the horizontal plane,the sliding mode control algorithm combining cross track error method and line of sight method was used to fulfill its high-precision trajectory tracking control in the complex sea conditions. As the vertical displacement of the new AUV can be measured,in order to achieve the tracking of time-varying depth signal,a stable sliding mode controller was designed based on the single-input multi-state system,which took into account the characteristic of the hydroplane and the amplitude and rate constraints of the hydroplane angle. Moreover,the application of dynamic boundary layer can improve the robustness and control accuracy of the system. The computational results show that the designed sliding mode control systems of the horizontal and vertical planes can ensure the trajectory tracking performance and accuracy of the new AUV in complex sea conditions. The impacts of currents and waves on the sliding mode controller of the new AUV were analyzed qualitatively and quantitatively by comparing the trajectory tracking performance of the new AUV in different sea conditions,which provides an effective theoretical guidance and technical support for the control system design of the new AUV in real complex environment.

Strong tracking adaptive Kalman filters for underwater vehicle dead reckoning

To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.

Three-dimensional neural network tracking control of autonomous underwater vehicles with input saturation

This paper addresses the problem of three-dimensional trajectory tracking control for underactuated autonomous underwater vehicles in the presence of parametric uncertainties,environmental disturbances and input saturation.First,a virtual guidance control strategy is established on the basis of tracking error kinematics,which resolves the overall control system into two cascade subsystems.Then,a first-order sliding mode differentiator is introduced in the derivation to avoid tedious analytic calculation,and a Gaussian error function-based continuous differentiable symmetric saturation model is explored to tackle the issue of input saturation.Combined with backstepping design techniques,the neural network control method and an adaptive control approach are used to estimate composite items of the unknown uncertainty and approximation errors.Meanwhile,Lyapunov-based stability analysis guarantees that control error signals of the closed-loop system are uniformly ultimately bounded.Finally,simulation studies are conducted for the trajectory tracking of a moving target and a spiral line to validate the effectiveness of the proposed controller.

Tracking Control of Autonomous Underwater Vehicles with Internal Moving Mass

追踪轨道的控制问题被调查为一自治在水下作为致动器用一个内部点团和一个后面的推进器搬到垂直飞机的车辆(AUV ) 。与点团的动力学结合了， AUV 作为一个 underactuated 系统被建模。一个基于 Lyapunov 的追踪控制器被使用 backstepping 途径稳定错误动力学并且强迫位置错误到起源的一位小邻居建议。模拟结果验证建议追踪的途径。

Bioinspired Underwater Navigation Using Polarization Patterns Within Snell’s Window

Aiming at the requirement of autonomous navigation capability of the underwater unmanned vehicle(UUV),a novel bionic method for underwater navigation based on polarization pattern within Snell’s window is proposed.Inspired by creatures,polarization navigation is a satellite-free navigation scheme and has great potential to be used in the water.However,because of the complex underwater environment,whether UUV polarization navigation can be realized is doubtful.To illustrate the feasibility of underwater polarization navigation,we firstly establish the model of under-water polarization patterns to prove the stability and predictability of the underwater polarization pattern within Snell’s window.Then,we carry out static and dynamic experiments of underwater heading determination based on developed polarization information detection equipment.Finally,we obtain underwater polarization patterns and conduct the tracking experiment at different water depths.The experimental results of the underwater polarization patterns are consistent with the simulation,which proves the correctness of the proposed model.At the water depth of 5 m,the average angle and position error of the tracking experiment are 14.3508°and 4.0812 m,respectively.It is illustrated that underwater polarization navigation is realizable and the precision can meet the real-time navigation requirements of UUV.This study promotes the improvement of underwater navigation ability and the development of marine equipment.

Real-time localization for underwater equipment using an extremely low frequency electric field

A new real-time underwater equipment location method adopting an electric field induced by a standard current source is proposed.Our goals were real-time tracking and location of stationary or moving underwater equipment both in shallow and deep seas,under noisy conditions.The main features of this method are as follows:(1)a standard current source on the water surface,which can be towed by a vehicle,consisting of two electrodes,a signal generator,and a GPS unit;(2)measurement of the extremely low frequency(ELF)electric field emitted by the current source,made possible by electric field sensors on the underwater equipment;(3)position of the underwater equipment is estimated in real time based on a progressive update extended Kalman filter(PUEKF),which is carried out using the propagation model of an ELF electric field because the electric field at the position of the underwater equipment and the current source position are known.We verified the accuracy of our method and confirmed real-time location feasibility through numerical,physical scale,and real-time sea experiments.Through numerical experiments,we verified that our method works for underwater equipment location in real-world conditions,and the location error can be less than 0.2 m.Next,real-time location experiments for stationary underwater measuring equipment in water tank were conducted.The result shows that the location error can be less than 0.1 m.We also confirmed real-time location feasibility through the use of offshore experiment.We expect that our method will complement conventional underwater acoustic location methods for underwater equipment in acoustically noisy environments.

FGAs-Based Data Association Algorithm for Multi-sensor Multi-target Tracking

A novel data association algorithm is developed based on fuzzy geneticalgorithms (FGAs). The static part of data association uses one FGA to determine both the lists ofcomposite measurements and the solutions of m-best S-D assignment. In the dynamic part of dataassociation, the results of the m-best S-D assignment are then used in turn, with a Kalman filterstate estimator, in a multi-population FGA-based dynamic 2D assignment algorithm to estimate thestates of the moving targets over time. Such an assignment-based data association algorithm isdemonstrated on a simulated passive sensor track formation and maintenance problem. The simulationresults show its feasibility in multi-sensor multi-target tracking. Moreover, algorithm developmentand real-time problems are briefly discussed.

Dynamic cluster member selection method for multi-target tracking in wireless sensor network

Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a special cluster member(CM) node selection method is put forward in the scheme.An energy efficiency model was proposed under consideration of both energy consumption and remaining energy balance in the network.A tracking accuracy model based on area-sum principle was also presented through analyzing the localization accuracy of triangulation.Then,the two models mentioned above were combined to establish dynamic cluster member selection model for MTT where a comprehensive performance index function was designed to guide the CM node selection.This selection was fulfilled using genetic algorithm.Simulation results show that this method keeps both energy efficiency and tracking quality in optimal state,and also indicate the validity of genetic algorithm in implementing CM node selection.

Multi-target tracking algorithm based on PHD filter against multi-range-false-target jamming

Multi-range-false-target(MRFT) jamming is particularly challenging for tracking radar due to the dense clutter and the repeated multiple false targets. The conventional association-based multi-target tracking(MTT) methods suffer from high computational complexity and limited usage in the presence of MRFT jamming.In order to solve the above problems, an efficient and adaptable probability hypothesis density(PHD) filter is proposed. Based on the gating strategy, the obtained measurements are firstly classified into the generalized newborn target and the existing target measurements. The two categories of measurements are independently used in the decomposed form of the PHD filter. Meanwhile,an amplitude feature is used to suppress the dense clutter. In addition, an MRFT jamming suppression algorithm is introduced to the filter. Target amplitude information and phase quantization information are jointly used to deal with MRFT jamming and the clutter by modifying the particle weights of the generalized newborn targets. Simulations demonstrate the proposed algorithm can obtain superior correct discrimination rate of MRFT, and high-accuracy tracking performance with high computational efficiency in the presence of MRFT jamming in the dense clutter.

An Iterative Pose Estimation Algorithm Based on Epipolar Geometry With Application to Multi-Target Tracking

This paper introduces a new algorithm for estimating the relative pose of a moving camera using consecutive frames of a video sequence. State-of-the-art algorithms for calculating the relative pose between two images use matching features to estimate the essential matrix. The essential matrix is then decomposed into the relative rotation and normalized translation between frames. To be robust to noise and feature match outliers, these methods generate a large number of essential matrix hypotheses from randomly selected minimal subsets of feature pairs, and then score these hypotheses on all feature pairs. Alternatively, the algorithm introduced in this paper calculates relative pose hypotheses by directly optimizing the rotation and normalized translation between frames, rather than calculating the essential matrix and then performing the decomposition. The resulting algorithm improves computation time by an order of magnitude. If an inertial measurement unit(IMU) is available, it is used to seed the optimizer, and in addition, we reuse the best hypothesis at each iteration to seed the optimizer thereby reducing the number of relative pose hypotheses that must be generated and scored. These advantages greatly speed up performance and enable the algorithm to run in real-time on low cost embedded hardware. We show application of our algorithm to visual multi-target tracking(MTT) in the presence of parallax and demonstrate its real-time performance on a 640 × 480 video sequence captured on a UAV. Video results are available at https://youtu.be/Hh K-p2 h XNn U.

A new algorithm of bearings-only multi-target tracking of bistatic system

Much research mainly focuses on the batch processing method （e.g. maximum likelihood method） when bearings-only multiple targets tracking of bistatic sonar system is considered. In this paper, the idea of recursive processing method is presented and employed, and corresponding data association algorithms, i.e. a multi-objective ant-colony-based optimization algorithm and an easy fast assignment algorithm are developed to solve the measurements-to-measurements and measurements-to-tracks data association problems of bistatic sonar system, respectively. Monte-Carlo simulations are induced to evaluate the effectiveness of the proposed methods.