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As autonomous underwater vehicle (AUV) adoption increases, operators demand advanced behaviors from commercial off‐the‐shelf systems. However, new behaviors can often only be deployed operationally once assured. This paper overviews research into expediting recovery to an operator's vessel through a custom homing behavior, demonstrating technology advancement in conjunction with test and evaluation activities. Advanced docking infrastructure and frameworks are still under development, yet current AUV operations require rapid and reliable recovery when mission factors change. Homing is achieved with a directional acoustic transponder providing range and bearing data to the AUV from the operator's vessel. A converted measurement Kalman filter processes range and bearing data that generates dynamic waypoints for the AUV through MOOS‐IvP as a backseat driver; a universal approach and filtering that is unique from prior AUV research. Results from simulations and field trials were analysed through a modular and experimental Test & Evaluation framework that was adopted specifically to help verify and validate the new AUV behavior, including systematic variations in recovery boat manoeuvres. The process includes documented use for the first time in AUV research of combinatorial screening (high throughput testing) and an average standardized residual metric to focus development early, encourage constructive iteration and build operational and engineering trust. Consistent homing was demonstrated with localization within 0.3 m and homing within 1.8 m of a moving digital acoustic transponder. [ABSTRACT FROM AUTHOR]

In this manuscript, an underwater target tracking problem with passive sensors is considered. The measurements used to track the target trajectories are (i) only bearing angles, and (ii) Doppler-shifted frequencies and bearing angles. Measurement noise is assumed to follow a zero mean Gaussian probability density function with unknown noise covariance. A method is developed which can estimate the position and velocity of the target along with the unknown measurement noise covariance at each time step. The proposed estimator linearises the nonlinear measurement using an orthogonal polynomial of first order, and the coefficients of the polynomial are evaluated using numerical integration. The unknown sensor noise covariance is estimated online from residual measurements. Compared to available adaptive sigma point filters, it is free from the Cholesky decomposition error. The developed method is applied to two underwater tracking scenarios which consider a nearly constant velocity target. The filter's efficacy is evaluated using (i) root mean square error (RMSE), (ii) percentage of track loss, (iii) normalised (state) estimation error squared (NEES), (iv) bias norm, and (v) floating point operations (flops) count. From the simulation results, it is observed that the proposed method tracks the target in both scenarios, even for the unknown and time-varying measurement noise covariance case. Furthermore, the tracking accuracy increases with the incorporation of Doppler frequency measurements. The performance of the proposed method is comparable to the adaptive deterministic support point filters, with the advantage of a considerably reduced flops requirement. [ABSTRACT FROM AUTHOR]

In bearings-only tracking applications, the standard bearing model ignores the propagation delay of signal, except in cases where the target speed is comparable to the signal speed. This paper provides a theoretical analysis of the performance degradation suffered by a maximum likelihood estimator (MLE) that neglects the signal propagation delay in the bearings-only fusion of heterogeneous sensors: one with negligible propagation delay and the other with non-negligible delay. By using a higher order Taylor-series based analysis, we derive approximate expressions for the bias and mean square error (MSE) of the MLE. The analysis shows that neglecting the propagation delay of a sensor (with non-negligible delay) in such bearings-only fusion problems leads to severe degradation in performance even when the signal speed is orders of magnitude higher than that of target. Simulation results confirm the validity of the theoretical predictions. Finally, a bias-compensated MLE is proposed that not only takes into account the propagation delay, but also compensates for the estimation bias. This bias-compensated MLE is nearly unbiased and exhibits an RMS error performance close to the Cramer Rao lower bound. [ABSTRACT FROM AUTHOR]

Only a few publications exist at present on heterogeneous track-to-track fusion (T2TF). A common limitation of the current work on heterogeneous T2TF is that the cross covariance due to common process noise cannot be computed. This is due to the fact that two local trackers use different dynamic models, and hence, it is difficult to account for the common process noise. We consider a heterogeneous T2TF problem in three dimension (3-D) using a passive infrared search and track (IRST) sensor and an active air moving target indicator (AMTI) radar with the nearly constant velocity motion of the target. The active AMTI tracker uses the Cartesian state vector with 3-D position and velocity, and the dynamic model is linear. A passive IRST tracker commonly uses modified spherical coordinates (MSCs) for the state vector, where the dynamic model is nonlinear. In this formulation, the common process noise is explicitly modeled in both dynamic models. Therefore, it is possible to take into account the common process noise. We use the cubature Kalman filter (CKF) in both trackers due to its numerical stability and improved state estimation accuracy over existing nonlinear filters. The passive tracker uses a range-parameterized MSC-based CKF, and the active tracker uses a Cartesian CKF. We perform T2TF using the information filter (IF), where each local tracker sends its information matrix and the corresponding information state estimate to the fusion center. The IF handles the common process noise in an approximate way. Results from Monte Carlo simulations show that the accuracy of the proposed IF-based T2TF is close to that of the centralized fusion with varying levels of process noise and communication data rate. [ABSTRACT FROM AUTHOR]

Accurate three-dimensional (3-D) position and velocity estimates of an aircraft are important for air traffic control (ATC) systems. An ATC 2-D radar measures the slant range and azimuth of an aircraft. Thus, a single measurement from a 2-D radar is not sufficient to calculate the 3-D position of an aircraft. Previous researchers have used the multiple-model-based height-parametrized (HP) extended Kalman filter with Cartesian state vector (HP-CEKF) with one or two 2-D radars for an aircraft with nearly constant velocity and altitude. However, the filter initialization algorithms contain errors. In this paper, in addition to the HP-CEKF, we present the HP Cartesian unscented Kalman filter (HP-CUKF) and HP Cartesian cubature Kalman filter (HP-CCKF). We also present two new nonlinear filters for the two-radar problem. The first filter uses modified spherical coordinates based HP-UKF (HP-MSCUKF) where the range and azimuth are components of the target state. The second filter uses a cubature Kalman filter with filter initialization by the bias-compensated pseudolinear estimator. We also consider the climbing motion of an aircraft with nearly constant climbing rate, which has not been studied before. All four aforementioned HP filters use the single-point track initiation algorithm. The state estimation accuracy of an aircraft is analyzed as a function of the distance of the aircraft from the radar(s). We compare the performance of the nonlinear filters with the posterior Cramér-Rao lower bound. The normalized computational times of all algorithms in all scenarios are presented. Our results show that accurate 3-D trajectory estimates of an aircraft can be obtained using one or two ATC 2-D radars. [ABSTRACT FROM PUBLISHER]

In this paper, we derive a novel batch Bayesian weighted instrumental variable estimator for the three-dimensional (3D) target motion analysis (TMA) problem using bearing and elevation measurements. Unlike most existing 3D estimators based on instrumental variables, the proposed approach is able to incorporate a priori information into the estimation process and is proven to be approximately asymptotically unbiased. An approximate asymptotic covariance matrix is also presented to evaluate the performance of the estimator. Simulation results show that the proposed estimator outperforms its non-Bayesian counterpart and has an estimation performance on par with the conventional iterative maximum a posteriori estimator, while being orders of magnitude faster in a passive sonar TMA problem. Moreover, it was shown that the proposed approach can provide an accurate initialization for recursive estimators. [ABSTRACT FROM AUTHOR]

The generalized labeled multi-Bernoulli (GLMB) is a family of tractable models that alleviates the limitations of the Poisson family in dynamic Bayesian inference of point processes. In this paper, we derive closed form expressions for the void probability functional and the Cauchy–Schwarz divergence for GLMBs. The proposed analytic void probability functional is a necessary and sufficient statistic that uniquely characterizes a GLMB, while the proposed analytic Cauchy–Schwarz divergence provides a tractable measure of similarity between GLMBs. We demonstrate the use of both results on a partially observed Markov decision process for GLMBs, with Cauchy–Schwarz divergence based reward, and void probability constraint. [ABSTRACT FROM PUBLISHER]

This paper considers the problem of three-dimensional target motion analysis using a combination of bearing, elevation, and time difference of arrival (TDOA) measurements. We propose a hybrid closed-form solution based on the weighted instrumental variables for this highly nonlinear problem. The proposed solution avoids the high computational complexity and convergence problems associated with iterative estimators. Furthermore, the simulation results suggest that the proposed estimator is nearly efficient, since its performance is very close to the best achievable performance as predicted by the Cramer-Rao lower bound. [ABSTRACT FROM PUBLISHER]

This letter presents a Gaussian-sum cubature Kalman filter with improved robustness compared to the original algorithm proposed by the authors in , which demonstrated excellent accuracy and efficiency for the bearings-only tracking problem. Modifications are made in the splitting and merging procedure of the Gaussian components in the algorithm. Simulation results confirm the improved robustness of the modified algorithm against the choice of threshold level for the splitting procedure. [ABSTRACT FROM AUTHOR]

The conventional GMPHD/CPHD filters require the PHD for target births to be a Gaussian mixture (GM), which is potentially inefficient because careful selection of the mixture parameters may be required to ensure good performance. Here we present approximations which allow part of the birth PHD to be uniformly distributed, obviating the need to use a large GM to model target births. The benefits of this approach are demonstrated by simulations on a bearings-only filtering scenario. [ABSTRACT FROM AUTHOR]

Typical multitarget tracking systems assume that in every scan there is at most one measurement for each target. In certain other systems such as over-the-horizon radar tracking, the sensor can generate resolvable multiple detections, corresponding to different measurement modes, from the same target. In this paper, we propose a new algorithm called multiple detection multiple hypothesis tracker (MD-MHT) to effectively track multiple targets in such multiple-detection systems. The challenge for this tracker, which follows the multiple hypothesis framework, is to jointly resolve the measurement origin and measurement mode uncertainties. The proposed tracker solves this data association problem via an extension to the multiframe assignment algorithm. Its performance is demonstrated on a simulated over-the-horizon-radar multitarget tracking scenario, which confirms the effectiveness of this algorithm. [ABSTRACT FROM PUBLISHER]

Herein is presented an efficient nonlinear filtering algorithm called the Gaussian-sum cubature Kalman filter (GSCKF) for the bearings-only tracking problem. It is developed based on the recently proposed cubature Kalman filter and is built within a Gaussian-sum framework. The new algorithm consists of a splitting and merging procedure when a high degree of nonlinearity is detected. Simulation results show that the proposed algorithm demonstrates comparable performance to the particle filter (PF) with significantly reduced computational cost. [ABSTRACT FROM AUTHOR]

This paper presents a succinct review of attempts in the literature to use game theory to model decision-making scenarios relevant to defence applications. Game theory has been proven as a very effective tool in modelling the decision-making processes of intelligent agents, entities, and players. It has been used to model scenarios from diverse fields such as economics, evolutionary biology, and computer science. In defence applications, there is often a need to model and predict the actions of hostile actors, and players who try to evade or out-smart each other. Modelling how the actions of competitive players shape the decision making of each other is the forte of game theory. In past decades, there have been several studies that applied different branches of game theory to model a range of defence-related scenarios. This paper provides a structured review of such attempts, and classifies existing literature in terms of the kind of warfare modelled, the types of games used, and the players involved. After careful selection, a total of 29 directly relevant papers are discussed and classified. In terms of the warfares modelled, we recognise that most papers that apply game theory in defence settings are concerned with Command and Control Warfare, and can be further classified into papers dealing with (i) Resource Allocation Warfare (ii) Information Warfare (iii) Weapons Control Warfare, and (iv) Adversary Monitoring Warfare. We also observe that most of the reviewed papers are concerned with sensing, tracking, and large sensor networks, and the studied problems have parallels in sensor network analysis in the civilian domain. In terms of the games used, we classify the reviewed papers into papers that use non-cooperative or cooperative games, simultaneous or sequential games, discrete or continuous games, and non-zero-sum or zero-sum games. Similarly, papers are also classified into two-player, three-player or multi-player game based papers. We also explore the nature of players and the construction of payoff functions in each scenario. Finally, we also identify gaps in literature where game theory could be fruitfully applied in scenarios hitherto unexplored using game theory. The presented analysis provides a concise summary of the state-of-the-art with regards to the use of game theory in defence applications and highlights the benefits and limitations of game theory in the considered scenarios. [ABSTRACT FROM AUTHOR]

Multitarget tracking has a long history spanning over 50 years and it refers to the problem of jointly estimating the number of targets and their states from sensor data. Today, multitarget tracking has found applications in diverse disciplines, including, air traffic control, intelligence, surveillance, and reconnaissance (ISR), space applications, oceanography, autonomous vehicles and robotics, remote sensing, computer vision, and biomedical research. During the last decade, advances in multitarget tracking techniques, along with sensing and computing technologies, have opened up numerous research venues as well as application areas. The multitarget tracking problem in the presence of false alarm and sensor probability of detection less than unity is much more complex than the standard filtering problem. Apart from process and measurement noises in the dynamic and measurement models, respectively, one has to contend with much more complex sources of uncertainty, such as the measurement origin uncertainty, data association, false alarm, missed detections, and births and deaths of targets. The goal of this special issue is to explore recent advances in the theory and applications of multitarget tracking with a focus on novel algorithms and methods. [ABSTRACT FROM PUBLISHER]