The feasibility of Song and Speyer's modified gain extended Kalman filter using bearings-only measurements is explored for underwater applications. A much simpler version of the modified function introduced by Galkowski and Islam is considered for the development of this algorithm. This algorithm estimates target motion parameters and detects target manoeuvre, using zero mean chi-square distributed random sequence residuals, in sliding window format. During the period of target manoeuvring, the covariance of the process noise is increased sufficiently in such a way that the disturbance in the solution is less. When a target manoeuvre is completed, the covariance of process noise is lowered. The performance of this algorithm is evaluated in Monte Carlo simulation and results are shown for various typical geometries.

The battery pack performance and expected lifespan are crucial in electric vehicle applications. Balancing the charge on a battery pack connected in series and parallel is crucial due to manufacturing discrepancies and distinct performance of each cell in a standard battery pack. In this paper, a switched-resistor passive balancing-based method is proposed for balancing cells in a battery management system (BMS). The value of the available voltage at the battery cell terminals is balanced using resistors in an electrical circuit, and the excess voltage is eliminated. The cell balancing outcome demonstrates that the electrical circuit can maintain an even voltage across each cell. The procedure of balancing involves individually adjusting each cell’s level of charge. Passive balancing releases energy as heat by draining charge from cells that have too much charge. A passive cell balancer is a cost-effective solution and easy to install, but due to thermal loss from a resistor, it has a low energy efficiency for cell balancing and necessitates a lengthy balancing process. This passive cell balancer is an effective and reliable method for low-power devices and portable applications such as electrical vehicles. The power limits during charging and discharging are estimated using the bisection method.

The pseudo-linear extended Kalman tracking filter previously developed for passive target tracking using bearings-only measurements required the use of a null target state vector as an initial estimate to obtain convergence for all types of scenario. The pseudo-linear estimator is projected in such a way that it does not require any initial estimate at all and at the same time offers all the features of the extended Kalman filter based pseudo-linear filter; namely sequential processing, flexibility to adopt the variance of each measurement, etc. The algorithm is tested in Monte Carlo simulations and its results are presented for two typical scenarios at various noise levels.

The unscented transformation coupled with certain parts of the classic Kalman filter, provides a more accurate ?method than the Extended Kalman Filter for nonlinear state estimation. Using bearings-only measurements, the unscented Kalman Filter algorithm estimates target motion parameters and detects target maneuver, using zero mean chi-square distributed random sequence residuals, in a sliding window format. During target maneuvering, the covariance of the process noise is sufficiently increased in such a way that the disturbance in the solution is minimized. When target maneuver is completed, the covariance of process noise is lowered. The performance of this algorithm is evaluated using Monte Carlo simulation and results are presented.

Machine learning, a rapidly growing field, has attracted numerous researchers for its ability to automatically learn from and make predictions based on data. This manuscript presents an innovative approach to estimating the covariance matrix of noise in radar measurements for target tracking, resulting from collaborative efforts. Traditionally, researchers have assumed that the covariance matrix of noise in sonar measurements is present in the vast majority of literature related to target tracking. On the other hand, this research aims to estimate it by employing deep learning algorithms with noisy measurements in range, bearing, and elevation from radar sensors. This collaborative approach, involving multiple disciplines, provides a more precise and accurate covariance matrix estimate. Additionally, the unscented Kalman filter was combined with the gated recurrent unit, multilayer perceptron, convolutional neural network, and long short-term memory to accomplish the task of 3D target tracking in an airborne environment. The quantification of the results was achieved through the use of Monte Carlo simulations, which demonstrated that the convolutional neural network performed better than any other approach. The system was simulated using a Python program, and the proposed method offers higher accuracy and faster convergence time than conventional target tracking methods. This is a demonstration of the potential that collaboration can have in research.

In the maritime environment, object motion analysis using bearings-only measurements in a two-dimensional plane is carried out. The observer usually performs S-maneuver on the line of sight for range observability. This traditional S-maneuver is not preferable always. So, in this research work, a procedure is tried out to understand the scenario online and recommend the maneuver accordingly. Object motion parameters are estimated based on one selected acceptance criteria. Online observer maneuver recommendations are carried out to obtain the solution fast. The algorithm is realized using Monte-Carlo simulation and evaluated against number of scenarios.