The problem of association of measurements acquired by passive bearings-only sensors in two dimensional (2D) plane is addressed in this paper. This problem can be formulated mathematically as a multidimensional assignment (MDA) problem with two steps of cost calculation and optimization. Compared with the optimization step, the cost calculation consumes more time (at least 80%; of the total time for solving the MDA problem). In order to reduce the computational requirements of the assignment costs of infeasible associations, a practical coarse gating strategy is proposed. First, two bearing measurements from different sensors are used to predict the bearing measurements of other sensors. Then, infeasible associations can be identified using gates centered on the predicted bearing measurements. This strategy is also extended to the measurement data association of heterogeneous sensors. Numerical examples verify the effectiveness of the proposed strategy.

Bearing-only and ground moving target indicator (GMTI) filtering are important practical nonlinear filtering problems that have been widely studied. The bearing-only filtering problem is regarded as a challenging nonlinear filtering problem. The degree of nonlinearity (DoN) of these problems were previously studied using differential geometry based parameter-effects and intrinsic curvatures. In this paper we analyze these two problems using a recently proposed measure of nonlinearity (MoN) for state estimation. We compute the conditional MoN (unnormalized and normalized) using an unscented Kalman filter (UKF) and a particle filter (PF). Numerical results from Monte Carlo simulations show that the normalized MoN values for these two problems are quite small, ~ 10-4and the normalized MoN values for the GMTI filtering are slightly higher than those for the bearing-only filtering. For each problem, we also compute the root mean square (RMS) position and velocity errors and posterior Cramer-Rao lower bound (PCRLB) to asses the state estimation accuracy.

Bearing-only filtering algorithms used in submarine tracking commonly assume that the target and ownship move in the same plane. In real-world scenarios, the target and ownship may actually move in different planes, since it may be advantageous for the ownship to do so. Tracking of the target for this scenario can be accomplished by using passive bearing and elevation measurements. Then the algorithm for angle-only filtering in 3D used in passive ranging with an infrared search and track sensor can be applicable to the submarine tracking problem. Advances in sensor technology and signal processing would allow a future sensor system to collect bearing and elevation measurements by an ownship with improved accuracy. In this paper, we analyze the tracking accuracy of a submarine tracking scenario by using angle-only filtering in 3D while varying the height difference between the target and ownship planes. We use an extended Kalman filter, unscented Kalman filter, range-parametrized UKF, and particle filter to compare the state estimation accuracy. Our results show that the height of the target can be estimated accurately by the proposed algorithms for small height differences between the target and ownship.

Ground target tracking using electro-optical and infrared video sensors onboard unmanned aerial vehicles has drawn a great deal of interest in recent years due to the evolution of inexpensive video sensors and platforms. We present algorithms for geolocation using pixel location measurements which are based on the perspective transformation and includes radial and tangential lens distortions. The covariance of geolocation error takes into account the errors in pixel location, intrinsic and extrinsic camera parameters, and terrain height. Pixel coordinates of the optical center, focal distances along the X and Y axes, lens distortion parameters, and the skew parameter constitute the intrinsic camera parameters. The extrinsic camera parameters include the sensor position and sensor attitude relative a local east-north-up coordinate frame. Numerical results are presented using simulated data. Our results show that the errors in the Euler angles used to represent the sensor attitude is the dominant source of geolocation error.

Common ground target dynamic models include the nearly constant velocity (NCV), nearly constant acceleration (NCA), and nearly constant turn (NCT) models. Most of the papers on the interacting multiple model (IMM) estimator use a constant Markov chain transition probability matrix (TPM) corresponding to a constant measurement sampling interval. However, a multi-sensor ground target tracking system usually employs ground moving target indicator radar, electro-optical, infrared, video, acoustic, and seismic sensors, for which the sampling intervals are different. Modeling such systems requires using a variable sampling interval in the IMM estimator, which in turn requires the use of a non-constant TPM. An analytic expression for the TPM with variable sampling interval exists for two dynamic models. When the number of dynamic models is greater than two, the TPM can be numerically calculated efficiently. We present the technical approach for the IMM estimator with variable sampling intervals. Preliminary numerical results are presented for a maneuvering target with the NCV, NCA, and NCT models using 200 Monte Carlo simulations