Tracking and Estimation of Multiple Cross-Over Targets in Clutter

Tracking problems, including unknown number of targets, target trajectories behaviour and uncertain motion of targets in the surveillance region, are challenging issues. It is also difficult to estimate cross-over targets in heavy clutter density environment. In addition, tracking algorithms including smoothers which use measurements from upcoming scans to estimate the targets are often unsuccessful in tracking due to low detection probabilities. For efficient and better tracking performance, the smoother must rely on backward tracking to fetch measurement from future scans to estimate forward track in the current time. This novel idea is utilized in the joint integrated track splitting (JITS) filter to develop a new fixed-interval smoothing JITS (FIsJITS) algorithm for tracking multiple cross-over targets. The FIsJITS initializes tracks employing JITS in two-way directions: Forward-time moving JITS (fJITS) and backward-time moving JITS (bJITS). The fJITS acquires the bJITS predictions when they arrive from future scans to the current scan for smoothing. As a result, the smoothing multi-target data association probabilities are obtained for computing the fJITS and smoothing output estimates. This significantly improves estimation accuracy for multiple cross-over targets in heavy clutter. To verify this, numerical assessments of the FIsJITS are tested and compared with existing algorithms using simulations

A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements

Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same; consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique. The proposed modification includes a new gating strategy to fuse and select measurements from two sensors which originate from the same target. The state estimation performance is evaluated in terms of Root Mean Squared Error (RMSE) for both position and velocity, whereas, track retention statistics are evaluated to gauge the performance of the proposed tracking algorithm. The results clearly show that the proposed technique improves track retention and and false track discrimination (FTD)

Track split smoothing for target tracking in clutter

The proposed integrated track split (ITS) smoothing utilize smoothing data association algorithm for tracking a target. We implemented two independent ITS filters; forward running ITS (fITS) filter and backward running ITS (bITS) filter. The novelty of the proposed algorithm is that the backward multi-track component predictions are applied to each forward track component prediction to produce multiple information fusion component predictions based on the data association technique. The information fusion state predictions are applied to all available measurements received in current scan to smooth track state estimations and target existence probabilities. A new technique is developed which applies smoothing estimates to compute the fITS estimate in the current scan. This efficiently leads forward path track to track a target in heavy clutter. The algorithm is known as fixed-interval smoothing ITS (FIsITS). The numerical simulation verifies the false track discrimination (FTD) capability of the FIsITS

Multi-scan smoothing for tracking manoeuvering target trajectory in heavy cluttered environment

An automatic target tracking algorithm must be capable of dealing with an unknown number of targets and their trajectory behaviour inside the surveillance region. However, due to target motion uncertainties, heavily populated clutter measurements and low detection probabilities of targets, the smoothing algorithms often fail to detect the true number of target trajectories. In this study, the authors discussed some deficiencies and insignificances of existing smoothing algorithms and proposed a new smoothing data association based algorithm called fixed-interval integrated track splitting smoothing (ITS-S). The proposed algorithm employ smoothing data association algorithm and compared with existing smoothing algorithms outperform in terms of target trajectory accuracy and false-track discrimination (FTD). However, existing algorithms fail to generate smoothed target trajectory and provides insignificant FTD performance in such difficult environments as illustrated in this simulation study. The ITS-S shows improved smoothing performance compared with that of existing algorithms for a manoeuvering target tracking in a heavily populated cluttered environment and low detection probabilities

Multi-Sonar Distributed Fusion for Target Detection and Tracking in Marine Environment

The multi-sonar distributed fusion system has been pervasively deployed to jointly detect and track marine targets. In the realistic scenario, the origin of locally transmitted tracks is uncertain due to clutter disturbance and the presence of multi-target. Moreover, attributed to the different sonar internal processing times and diverse communication delays between sonar and the fusion center, tracks unavoidably arrive in the fusion center with temporal out-of-sequence (OOS), both problems pose significant challenges to the fusion system. Under the distributed fusion framework with memory, this paper proposes a novel multiple forward prediction-integrated equivalent measurement fusion (MFP-IEMF) method, it fuses the multi-lag OOST with track origin uncertainty in an optimal manner and is capable to be implemented in both the synchronous and asynchronous multi-sonar tracks fusion system. Furthermore, a random central track initialization technique is also proposed to detect the randomly born marine target in time via quickly initiating and confirming true tracks. The numerical results show that the proposed algorithm achieves the same optimality as the existing OOS reprocessing method, and delivers substantially improved detection and tracking performance in terms of both ANCTT and estimation accuracy compared to the existing OOST discarding fusion method and the ANF-IFPFD method

Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter

This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms

Smoothing Linear Multi-Target Tracking Using Integrated Track Splitting Filter

Multi-target tracking (MTT) is a challenging issue due to an unknown number of real targets, motion uncertainties, and coalescence behavior of sensor (such as radar) measurements. The conventional MTT systems deal with intractable computational complexities because they enumerate all feasible joint measurement-to-track association hypotheses and recursively calculate the a posteriori probabilities of each of these joint hypotheses. Therefore, the state-of-art MTT system demands bypassing the entire joint data association procedure. This research work utilizes linear multi-target (LM) tracking to treat feasible target detections followed by neighbored tracks as clutters. The LM integrated track splitting (LMITS) algorithm was developed without a smoothing application that produces substantial estimation errors. Smoothing refines the state estimation in order to reduce estimation errors for an efficient MTT. Therefore, we propose a novel Fixed Interval Smoothing LMITS (FIsLMITS) algorithm in the existing LMITS algorithm framework to improve MTT performance. This algorithm initializes forward and backward tracks employing LMITS separately using measurements collected from the sensor in each scan. The forward track recursion starts after the smoothing. Therefore, each forward track acquires backward multi-tracks that arrived from upcoming scans (future scans) while simultaneously associating them in a forward track for fusion and smoothing. Thus, forward tracks become more reliable for multi-target state estimation in difficult cluttered environments. Monte Carlo simulations are carried out to demonstrate FIsLMITS with improved state estimation accuracy and false track discrimination (FTD) in comparison to the existing MTT algorithms

Extended smoothing joint data association for multi-target tracking in cluttered environments

In heavily cluttered environments, it is difficult to estimate the uncertain motion of an unknown number of targets with low detection probabilities. In particular, for tracking multiple targets, standard multi-target data association algorithms such as joint integrated probabilistic data association (JIPDA), face complexity and severely limited applicability due to a combinatorially increasing number of possible measurement-to-track associations. Smoothers refine the target estimates based on future scan information. However, in this complex surveillance scenario, existing smoothing algorithms often fail to track the true target trajectories. To overcome such difficulties, this study proposes a new smoothing joint measurement-to-track association algorithm called fixed-interval smoothing JIPDA for tracking extended target trajectories (FIsJIPDA). The algorithm employs two independent JIPDA filters: forward JIPDA (fJIPDA) and backward JIPDA (bJIPDA). fJIPDA tracks the target state forward in time and is computed after the smoothing is achieved. bJIPDA estimates the target state in the backward time sequence. The numerical simulation is performed in a heavily populated cluttered environment with low target-detection probabilities. The results show better target trajectory accuracy and false-track discrimination performance of FIsJIPDA compared with that of existing algorithms for tracking multiple extended targets

A Machine-Learning-Based Robust Classification Method for PV Panel Faults

Renewable energy resources have gained considerable attention in recent years due to their efficiency and economic benefits. Their proportion of total energy use continues to grow over time. Photovoltaic (PV) cell and wind energy generation are the least-expensive new energy sources in most countries. Renewable energy technologies significantly contribute to climate mitigation and provide economic benefits. Apart from these advantages, renewable energy sources, particularly solar energy, have drawbacks, for instance restricted energy supply, reliance on weather conditions, and being affected by several kinds of faults, which cause a high power loss. Usually, the local PV plants are small in size, and it is easy to trace any fault and defect; however, there are many PV cells in the grid-connected PV system where it is difficult to find a fault. Keeping in view the aforedescribed facts, this paper presents an intelligent model to detect faults in the PV panels. The proposed model utilizes the Convolutional Neural Network (CNN), which is trained on historic data. The dataset was preprocessed before being fed to the CNN. The dataset contained different parameters, such as current, voltage, temperature, and irradiance, for five different classes. The simulation results showed that the proposed CNN model achieved a training accuracy of 97.64% and a testing accuracy of 95.20%, which are much better than the previous research performed on this dataset