Track fusion between sensors at separate locations depends on the ability to estimate sensor biases to produce a single integrated picture. Approximating the nonlinear nature of the coordinate system with linear offsets is insufficient for tracking across the full volume of the sensors. In this research, a method to compute biases in sensor coordinates is expanded from the intial two dimensional approach to three dimensions. In addition, tracks are associated and fused together in a Track-Oriented Multiple Hypothesis Tracker, leveraging an Adaptive Semi-Greedy Search algorithm to effectively observe the track pattern across each sensor to compute these biases. The best hypothesis is reported as the current state, with high quality hypotheses kept from frame to frame at depth$N$. The performance of the bias estimation is assessed against the root mean squared error (RMSE) and normalized estimation error squard (NEES) for both the biases as well as the produced system tracks. A complex scenario with closely-spaced objects and maneuvering targets was selected to provide robust testing of the existing algorithm, demonstrating that it is effective beyond the initial simple scenario it was evaluated against.

The Interacting Multiple Model (IMM) estimator is well accepted as the best algorithm for tracking maneuvering targets, when the computational cost is considered. The IMM estimator includes a model-conditioned estimator for each kinematic model and the switching between modes or models is assumed to be a finite state Markov chain. The two-model configuration of the IMM estimator, the most commonly used version, typically includes either two nearly constant velocity (NCV) motion models or one NCV model and one nearly constant acceleration (NCA) model. In this paper, the design of these two configurations of the IMM estimator is considered. In this case, design refers to the selection of the motion models (i.e., NCV or NCA) and the corresponding process noise variances. The design methods are first considered for single coordinate tracking with measurements of position, and simulation results are given to illustrate the effectiveness of the design methods. Then, the design methods are applied to radar tracking, and simulation results are given to demonstrate the effectiveness of the design methods.

When performing track correlation and fusion in conjunction with bias estimation for sensor registration, the pattern match bias estimation is usually performed by modeling the biases as additive constants to the tracks in Cartesian space. Since sensor biases actually occur in sensor polar or spherical coordinates, the bias model of adding constants to the tracks can only be applied to a group of somewhat closely-spaced tracks before the linear assumption of the biases in Cartesian coordinates breaks down. A methodology to estimate sensor biases in the native coordinate frame in which they occur is presented, along with simulation results that illustrate its performance. Modeling the biases in sensor coordinates allow for tracks throughout the field of view to be used for sensor bias estimation, producing better sensor registration and track picture. In this research, sensor tracks are transmitted to a fusion center, where track correlation, bias estimation, and fusion are performed. Murty’s K-best hypotheses algorithm is utilized to generated the top K hypotheses for track-to-track correlation. Each hypothesis produces an estimate of the sensor biases. The correlation hypotheses are corrected for their sensor bias estimates and new correlation scores are computed, and the biascorrected correlation hypotheses are ranked to find the best. The best hypothesis is selected as the most recent system track picture. The system tracks produced by the best hypothesis are correlated against the previous system track picture to maintain system track number continuity. The performance of the bias estimation is assessed against the root mean squared error (RMSE) and normalized estimation error squared (NEES) errors of the estimated biases versus the true biases. A scenario with four tracks and two sensors is used to demonstrate the observability of these biases. The results show that the biases as applied to the remote sensor are observable and mitigated, allowing for a more accurate track picture.

In addition to the typical random errors that vary between consecutive measurements, the measurements for most all sensors used for target tracking include bias errors that remain relatively fixed during a target tracking episode and are typically characterized by an a priori mean and covariance. Since the bias errors are approximately fixed during a tracking episode, those errors violate the typical assumption of the measurement errors being white noise. Inflating the measurement covariance of the random errors by adding the bias covariance gives track covariances that poorly represent the true errors. The Schmidt-Kalman filter can be used to prevent the track covariances from becoming artificially too small. However, the Schmidt-Kalman filter produces a track covariance that encompasses the random and bias errors. In this paper, the authors formulate the target tracking as a least-square estimation (LSE) problem and show that the track covariance due to the bias errors can be isolated from the track covariance due the random errors. The authors utilize Monte Carlo simulations to verify and illustrate the accuracy of isolation of the bias and random covariances.

In this paper, methods are developed and evaluated for the correlation of Gaussian mixture tracks from two sensors. The hypothesis likelihoods for the case of a single target are given using the minimum mean square error and the maximum likelihood estimates of common origin between two Gaussian mixtures. A correlation test is developed as a likelihood ratio of the single target hypothesis to the hypothesis of two separate targets. The negative log likelihood cost is formulated and used in an optimal assignment method to perform track-to-track correlation for multiple targets between two sensors. Simulations were performed to compare the minimum mean square error and maximum likelihood approaches with Gaussian mixture tracks to a baseline method using unbiased converted measurements for sensors with a given probability of detection and bias significance. Results are shown to compare the performance of the correlation methods with respect to probability of correct correlation and root mean squared error versus track density for several different aspect angles between two sensors.

Summary form only given. The topic of multisensor tracking has been of interest to researchers for more than 30 years. However, the development and successful fielding of multisensor tracking systems have lagged significantly behind the research activities. In this presentation, a historical perspective of the key algorithmic developments associated with multisensor tracking will be given and their significance will be discussed. The technological advancements that have enabled the recent realizations of multisensor tracking will be discussed along with technological shortfalls that are limiting the realization of better multisensor tracking systems. Some thoughts on the future direction for multisensor tracking will be summarized.

Closely-spaced (but resolved) targets pose a challenge for measurement-to-track data association algorithms. Since the Mahalanobis distances between measurements collected on closely-spaced targets and tracks are similar, several elements of the corresponding kinematic measurement-to-track cost matrix are also similar. Lacking any other information upon which to base assignments, it is not surprising that data association algorithms make mistakes. This paper compares five methods for incorporating amplitude information to improve data association for multi-target tracking with Rayleigh targets. Two simple scenarios are used to demonstrate the impact of each method on measurement-to-track data association. None of the five methods perform best across the board. The analysis suggests that selection of a method for incorporating target amplitude information should be application-dependent