Track Breakages Research Articles

A broken-track association method for robust multi-target tracking adopting multi-view Doppler measurement information

Due to reasons such as target maneuvering and track crossing, mistaken track association may be caused. When the target is in Doppler blind zone, it often leads to the loss of measurement information, which is reflected in the situation as track breakage. In response to the demand of track repair in the case of track breakage, we propose a robust multi-target broken-track association method that comprehensively utilizes multi-view Doppler measurement information. Firstly, based on the derivation of measurement coordinate transformation bias, the multi-sensor Doppler measurement after error correction is fused to obtain the heading velocity and heading acceleration measurement of the target. Secondly, the multi-scan association cost function based on the heading velocity and heading acceleration measurement is constructed, and the optimal correlation sequence is obtained by minimizing the complexity of the cost function. Then, for the case of missing measurements, the optimal association sequence is used to extrapolate the missing measurements, thereby accomplishing the correlation among the broken track segment, the extrapolated measurement and the optimal correlation sequence. Thirdly, we design a multi-scan GLMB smoother to perform forward prediction and backward smoothing on the above correlation results to improve the smoothness of the track. Simulation and actual experimental results suggest that our proposed method can effectively deal with the track breakage situation of dense targets, particularly in track integrity, track accuracy and robustness compared with the existing approaches.

AIS aided marine radar target tracking in a detection occluded environment

Radar signal occlusion due to islands and large ships occurs frequently in maritime environments, degrading the performance of marine radar target detection and tracking. In order to address the common track breakages and tracking inaccuracies during occlusion, both prior environment knowledge from electronic navigation chart (ENC) and information from automatic identification systems (AIS) are exploited. An AIS-aided integrated probabilistic data association (A-IPDA) algorithm is proposed for maritime target tracking in an occluded environment. It recursively identifies target occlusion based on environmental knowledge and applies a tailored track existence probability transition matrix for track existence estimation during occlusion, meanwhile rich AIS information is exploited to maintain target tracking performance. Experiments based on both simulated data and real data show that A-IPDA has the ability to counter long period target occlusion in complex maritime environments, where track continuity and tracking accuracy can be well maintained.

GraphSTGAN: Situation understanding network of slow-fast high maneuvering targets for maritime monitor services of IoT data

With the rapid growth of the maritime Internet of Things (IoT) devices for Maritime Monitor Services (MMS), maritime traffic controllers could not handle a massive amount of data in time. For unmanned MMS, one of the key technologies is situation understanding. However, the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult, and pose significant challenges to maritime situation understanding. In order to comprehend the situation accurately and thus offer unmanned MMS, it is crucial to model the complex dynamics of multi-agents using IoT big data. Nevertheless, previous methods typically rely on complex assumptions, are plagued by unstructured data, and disregard the interactions between multiple agents and the spatial-temporal correlations. A deep learning model, Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN), is proposed in this paper, which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and interactions. Extensive experiments show the effectiveness and robustness of the proposed method.

A CFD-DEM Based Numerical Investigation of Debris Flow on Ballasted Railway Track

Debris flow occurring in mountainous areas can cause issues to railway tracks. Debris flow may cause large track deformation and even track breakage and introduce server ballast fouling afterwards. After the flash of a debris flow, the fine particles can be retained in the ballast layer and significantly reduce track drainage, leading to lower bearing capacity and a higher risk of track lateral stability problems. Moreover, these solid particles may deposit on the railway surface and endanger the train directly. Unfortunately, those debris flow introduced track issues have not been thoroughly investigated. This study presents a numerical investigation of the impact of the debris flow on the railway track. Various factors governing the debris flow are considered, including particle size and solid fraction. Besides, those factors affecting the ballast are also discussed, such as fouling condition and initial void ratio. A coupled computational fluid dynamics and discrete element method (CFD-DEM) approach is developed to capture the interactions between particles/particles, water/air, and particles/fluid. The results from this study may help the railway to improve track resilience before the debris flow and to improve maintenance strategy after the debris flow flashing.

RAILWAY TRACK CRACK DETECTION SYSTEM

Railways are large infrastructures, and in many countries they are the primary mode of transport. Because of their capacity, speed and reliability the railways have become a prime means of transportation. Only a small increase in railway efficiency has significant economic benefits for the rail industry. Therefore, a proper maintenance plan is required to control inspection frequency optimization and/or skill and efficiency improvements. Accidents that occur due to track breakage have been a major problem for the safety of life and timely service management of the railways. It is important to detect this breakage in real time before a train eventually reaches the damaged track and is subject to an accident. In this paper, the integration of ultrasonic crack detection method and complete station for eternal railway track geometry survey system into use and operation. This system consists of a GPS module, GSM modem, IR sensor, PIR sensor to put crack detection, contact intent and identification of any living being crossing the railroad track into operation. The GPS module and GSM modem lead to railway geometric parameter recognition and transmission of crack detection to nearby railway station. This paper also discusses the summation of a non-destructive ultrasonic test (NDT) and wireless sensor networks (WSNs) to hold the information in a continuous record without interruption in nobility during run-time. The PIR sensor is executed to keep manual patrolling and track finding of living beings away. This can work both during the night and the daytime. The summing up of both WSN and NDT technologies will form different advanced and trending applications to make wireless material scanning more cost-effective in real time.

Multi-Cattle tracking with appearance and motion models in closed barns using deep learning

Precision livestock monitoring promises greater management efficiency for farmers and higher welfare standards for animals. Recent studies on video-based animal activity recognition and tracking have shown promising solutions for understanding animal behavior. To achieve that, surveillance cameras are installed diagonally above the barn in a typical cattle farm setup to monitor animals constantly. Under these circumstances, tracking individuals requires addressing challenges such as occlusion and visual appearance, which are the main reasons for track breakage and increased misidentification of animals. This paper presents a framework for multi-cattle tracking in closed barns with appearance and motion models. To overcome the above challenges, we modify the DeepSORT algorithm to achieve higher tracking accuracy by three contributions. First, we reduce the weight of appearance information. Second, we use an Ensemble Kalman Filter to predict the random motion information of cattle. Third, we propose a supplementary matching algorithm that compares the absolute cattle position in the barn to reassign lost tracks. The main idea of the matching algorithm assumes that the number of cattle is fixed in the barn, so the edge of the barn is where new trajectories are most likely to emerge. Experimental results are performed on our dataset collected on two cattle farms. Our algorithm achieves 70.37%, 77.39%, and 81.74% performance on HOTA, AssA, and IDF1, representing an improvement of 1.53%, 4.17%, and 0.96%, respectively, compared to the original method.

A joint model and data driven track segment association algorithm for manoeuvring target tracking

In the field of manoeuvring target tracking, track breakage is a common issue due to strong manoeuvrability, missed detection, large measurement error, and long sampling interval, etc. In order to improve the track continuity of manoeuvring targets, a joint model and data-driven track segment association (JMDD-TSA) algorithm is proposed. First, the interacting multiple model (IMM) smoother is invoked to pre-process the track segments to obtain a smoothed state sequence. And the mode switching time (MST) is estimated by the IMM when a motion mode changing occurs in a track segment. Second, the estimated MST is used to select the partial smoothed states as the training data and the local Gaussian process (LGP) is proposed to predict and backtrack the track segments. Finally, a two-step association strategy is designed to assign and mend the candidate track segment pairs. Compared with the model-driven TSA and the data-driven TSA, the proposed algorithm can effectively improve the average track life and tracking accuracy of strong manoeuvring targets.

Track Segment Association Method Based on Bidirectional Track Prediction and Fuzzy Analysis

Due to sensor characteristics, geographical environment, electromagnetic interference, electromagnetic silence, information countermeasures, and other reasons, the phenomenon of track breakages occur in the process of aircraft track data processing. It leads to the change in target label attributes. In order to make the track segment association effect better, we studied several existing time series prediction methods, and proposed a track segment association method based on bidirectional Holt-Winters prediction and fuzzy analysis. This algorithm bidirectionally predicts and extrapolates track segments by the Holt-Winters method, and then uses the fuzzy track segment association algorithm to perform segment association and secondary association. The simulation results of this method show that the track segment association method based on Holt-Winters prediction and fuzzy analysis can effectively solve the track association problem where the target label attributes change before and after track breakage, demonstrating better association ability and robustness. Compared with the fuzzy association method without adding track prediction, our method generally improves the association accuracy by 35%.

A Cognitive Rail Track Breakage Detection System Using Artificial Neural Network

Abstract Rail track breakages represent broken structures consisting of rail track on the railroad. The traditional methods for detecting this problem have proven unproductive. The safe operation of rail transportation needs to be frequently monitored because of the level of trust people have in it and to ensure adequate maintenance strategy and protection of human lives and properties. This paper presents an automatic deep learning method using an improved fully Convolutional Neural Network (FCN) model based on U-Net architecture to detect and segment cracks on rail track images. An approach to evaluating the extent of damage on rail tracks is also proposed to aid efficient rail track maintenance. The model performance is evaluated using precision, recall, F1-Score, and Mean Intersection over Union (MIoU). The results obtained from the extensive analysis show U-Net capability to extract meaningful features for accurate crack detection and segmentation.

Continuous Tracking of Targets for Stereoscopic HFSWR Based on IMM Filtering Combined with ELM

High frequency surface wave radar (HFSWR) plays an important role in marine surveillance on account of its ability to provide wide-range early warning detection. However, vessel target track breakages are common in large-scale marine monitoring, which limits the continuous tracking ability of HFSWR. The following are the possible reasons for track fracture: highly maneuverable vessels, dense channels, target occlusion, strong clutter/interference, long sampling intervals, and low detection probabilities. To solve this problem, we propose a long-term continuous tracking method for multiple targets with stereoscopic HFSWR based on an interacting multiple model extended Kalman filter (IMMEKF) combined with an extreme learning machine (ELM). When the trajectory obtained by IMMEKF breaks, a new section of the track will start on the basis of the observation data. For multiple-target tracking, a number of broken tracks can be obtained by IMMEKF tracking. Then the ELM classifies the segments from the same vessel by extracting different features including average velocity, average curvature, ratio of the arc length to the chord length, and wavelet coefficient. Both the simulation and the field experiment results validate the method presented here, showing that this method can achieve long-term continuous tracking for multiple vessels, with an average correct track segment association rate of over 91.2%, which is better than the tracking performance of conventional algorithms, especially when the vessels are in dense channels and strong clutter/interference area.

Multiple hypothesis method for tracking move‐stop‐move target

This study presents a new approach to track the move-stop-move targets. In the presence of the radar Doppler blind area, a move-stop-move target may lead to track breakages, which has some adverse effects on estimating the number of targets in the surveillance area. The authors develop a mode switching-based multiple hypothesis tracking (MHT) method to solve this problem. In each association, MHT generates three types of hypotheses: moving and detected, moving and undetected and stopped. In the first two types of hypotheses, the target is in the mode of move and tracked with interacting multiple model (IMM)-extended Kalman filter (EKF) estimator, while in the third kind of hypotheses, the target is in the mode of stop and tracked by EKF with a stop model. The mode probabilities are calculated using the kinematic state statistics from the estimator and the Markov transfer probability of the target movement stage. The track discontinuity event in the classical log-likelihood ratio of MHT is modified as well. The results of simulation experiment show that the proposed MHT-based tracker outperforms the conventional MHT-based method when tracking move-stop-move target.

Damage Mechanism of Broad-narrow Joint of CRTSII Slab Track under Temperature Rise

There is two typical damage pattern at broad-narrow joint of CRTSII slab track breakage of narrow joint and fracture at junction between broad joint and narrow joint. This paper aims to study the damage mechanism and develop of broad-narrow joint of CRTSII slab track and put forward the methods to reduce the damage. Based on damaged plasticity model for concrete and cohesive zone model, the damage mechanism and development process of broad-narrow joint are analyzed in this study. The unequal width of broad joint and narrow joint, different concrete strength between broad-narrow joints and slab and slab integrity reducing due to the interface are the main reason for the typical damage. It is suggested to set the same width of broad joint and narrow joint, set the same strength of broad-narrow joint and slab and enhance the integrity of the slab by chipping or adding adhesives, among which the former two methods are more effective. Breakage of narrow joint is a gradual compression damage due to the lower concrete strength than slab. Fracture at the junction between narrow joint and broad joint is a mutational tension damage due to the unequal width of broad joint and narrow joint. The temperature gradient has significant effect on the compression damage, but small to the tension damage, it has the risk of complete destruction under extreme conditions.

Comprehensive Track Segment Association for Improved Track Continuity

Track breakages are common in target tracking due to highly maneuvering targets, association with false alarms or incorrect target-originated measurements, low detection probability, close target formations, large measurement errors, and long sampling intervals, among other causes. Existing track segment association (TSA) algorithms solve this breakage problem by predicting old track segments and retrodicting young track segments to a common time followed by two-dimensional (2-D) assignment. This approach presents two disadvantages. First, these algorithms predict or retrodict from the actual point of termination or beginning of their respective tracks, that is, they neither check if the cause of a track termination was incorrect association nor redress such an erroneous association. Second, these algorithms do not utilize the measurement information during the breakage period. Often, track terminations are due to incorrect measurement association. To solve the first problem, this paper proposes a 2-D assignment-based TSA algorithm that releases incorrectly associated measurements by going backward and forward in time along old and young track segments, respectively, and then performing prediction and retrodiction. Furthermore, to address both shortcomings in existing TSA algorithms simultaneously, we propose a novel multiframe assignment-based TSA algorithm that estimates the track during the breakage period, utilizing both unassociated and released measurements simultaneously. Moreover, the proposed algorithms can handle target maneuvers subject to a single turn during the breakage period. In the proposed solution, model parameters, such as starting time of the turn, ending time of the turn, and turn rate are obtained by maximizing the likelihood that a given measurement-tuple originated from the track couple under consideration. Simulation results demonstrate that the proposed TSA algorithm is superior to existing ones in terms of association accuracy and computational cost.

Novel Adaptive Peak Detection Method for Track Circuits Based on Encoded Transmissions

Track detection systems based on ultrasonic guided waves (UGWs) for continuous welded rails have achieved a rapid development in recent years. Though these systems can work properly in normal track conditions, UGW signals cannot penetrate the gap caused by a track breakage. To overcome this issue, a novel track circuit system based on a previous UGW system has been proposed in this paper. The 63-bit Kasami codes have been applied to the transmission scheme. The auto- and cross-correlation properties from Kasami codes have allowed the transmission detection at the receiver end, even under adverse conditions, such as noise or high attenuation. Furthermore, in previous track circuits, the correct detection and identification of the corresponding transmissions at the receivers had become a key aspect for the suitable and reliable operation of such type of systems. In this way, in addition to describing a novel track circuit, this paper focuses on the proposal of a novel adaptive peak detection algorithm to discriminate the main lobes in the auto-correlation functions at the receiver, thus making possible to correctly determine the track status in future works. This algorithm is mainly based on the discrete wavelet transform, on a moving average filter, as well as on the Hilbert Transform. Through the usage of a simulation model for the proposed track circuit as well as some outdoor experimental tests, the accuracy and the stability of the adaptive peak detection algorithm for the novel track circuit have been verified.

Method of target tracking with Doppler blind zone constraint

Doppler blind zone (DBZ) has a bad influence on the airborne early warning radar, although it has good detection performance for low altitude targets with pulse Doppler (PD) technology. In target tracking, the blind zone can cause target tracking breakage easily. In order to solve this problem, a parallel particle filter (PF) algorithm based on multi-hypothesis motion models (MHMMs) is proposed. The algorithm produces multiple possible target motion models according to the DBZ constraint. Particles are updated with the constraint in each motion model. Once the first measurement from the target which reappears from DBZ falls into the particle cloud formed by any model, the measurement-track association succeeds and track breakage is avoided. The simulation results show that on the condition of different DBZ ranges, a high association ratio can be got for targets with different maneuverability levels, which accordingly improves the tracking quality.

Track Segment Association for GMTI Tracks of Evasive Move-Stop-Move Maneuvering Targets

In a real tracking system, track breakages (TBs) can occur due to highly maneuvering targets, low detection probability, or clutter. In [27], a track segment association (TSA) approach was developed for an airborne early warning (AEW) system to improve track continuity by stitching broken track segments pertaining to the same target. However, this technique cannot provide satisfactory association performance in tracking with a ground moving target indicator (GMTI) radar ground moving targets employing evasive move-stop-move maneuvers. To avoid detection by a GMTI radar, targets can deliberately stop for some time before moving again. Since a GMTI radar does not detect a target when the radial velocity (along the line-of-sight from the sensor) falls below a certain minimum detectable velocity (MDV), the move-stop-move maneuvers of the targets usually lead to broken tracks as a result. We present a new TSA technique which employs a dummy track to formulate a complete association. By using an interacting multiple model (IMM) estimator with state-dependent mode transition probabilities (IMM-SDP) for track segment prediction (forward and backward), the proposed algorithm can effectively stitch both regular broken tracks and broken tracks due to targets' move-stop-move maneuvers. Comparisons are given to show the advantages of the proposed algorithm in broken tracks reduction and track continuity improvement.

New assignment-based data association for tracking move-stop-move targets

We present a new assignment-based algorithm for data association in tracking ground targets employing evasive move-stop-move maneuvers using ground moving target indicator (GMTI) reports obtained from an airborne sensor. To avoid detection by the GMTI sensor, the targets deliberately stop for some time before moving again. The sensor does not detect a target when the latter's radial velocity (along the line-of-sight from the sensor) falls below a certain minimum detectable velocity (MDV). Even in the absence of move-stop-move maneuvers, the detection has a less-than-unity probability (P/sub D/<1) due to obscuration and thresholding. Then, it is of interest, when a target is not detected, to develop a systematic technique that can distinguish between lack of detection due to P/sub D/<1 and lack of detection due to a stop (or a near stop). Previously, this problem was solved using a variable structure interacting multiple model (VS-IMM) estimator with a stopped target model (VS-IMM-ST) without explicitly addressing data association. We develop a novel assignment approach for move-stop-move targets that considers both the problem of data association as well as filtering. Typically, in assignment-based data association a measurement is used to denote the nondetection event. The use of the standard single-dummy assignment, which does not handle move-stop-move motion explicitly, can result in broken tracks. The new algorithm proposed here handles the evasive move-stop-move motion by introducing a second dummy measurement to represent nondetection due to the MDV. We also present a likelihood-ratio-based track deletion scheme for move-stop-move targets. Using this two-dummy data association algorithm, the track corresponding to a move-stop-move target is kept alive' during missed detections both due to MDV and due to P/sub D/<1. In addition, one can obtain reductions in both rms estimation errors as well as the total number of track breakages.

Track segment association, fine-step IMM and initialization with doppler for improved track performance

In this work we present a new track segment association technique to improve track continuity in large-scale target tracking problems where track breakages are common. A representative airborne early warning (AEW) system scenario, which is a challenging environment due to highly maneuvering targets, close target formations, large measurement errors, long sampling intervals, and low detection probabilities, provides the motivation for the new technique. Previously, a tracker using the interacting multiple model (IMM) estimator combined with an assignment algorithm was shown to be more reliable than a conventional Kalman filter based approach in tracking similar targets but it still yielded track breakages due to the difficult environment. In order to combine the broken track segments and improve track continuity, a new track segment association algorithm using a discrete optimization approach is presented. Simulation results show that track segment association yields significant improvements in mean track life as well as in position, speed, and course rms errors. Also presented is a modified one-point initialization technique with range rate measurements, which are typically ignored by other initialization techniques, and a fine-step IMM estimator, which improves performance in the presence of long revisit intervals. Another aspect that is investigated is the benefit of deep (multiframe or N-dimensional, with N > 2) association, which is shown to yield significant benefit in reducing the number of false tracks.

The Characterisation of Novel PWB Substrate Materials for Leadless Ceramic Chip Carrier Attachment

The problems associated with the direct attachment of leadless ceramic chip carriers (LCCCs) to conventional PWB structures subjected to ambient thermal and power (on/off) cycling are now widely documented. The reliability of LCCCs mounted on various novel substrate materials has been assessed here during ambient thermal cycling from −55° to 125°C and −20° to +70°C and during power heat cycling. Polyimide Kevlar material was rejected on the basis that microcracks, formed in the laminate during thermal cycling, propagated to the surface and resulted in copper track breakages. A flexible laminate interconnection structure for strain relief was found to be too cumbersome for most applications and gave only a small reliability improvement. Polyimide quartz and copper‐invar‐copper cored materials were both found to give a high degree of reliability in thermal cycling. The metal cored laminate is preferred because it is also a more rigid structure with the better thermal conductance needed for many high performance and reliability applications. An elastomer coated FR4 material also performed well during lower temperature thermal and power cycling tests and represents a reduced cost option for less severe environments.