The Advanced Linked Extended Reconnaissance & Targeting (ALERT) Technology Demonstration (TD) project is addressing key operational needs of the future Canadian Army's Surveillance and Reconnaissance forces by fusing multi-sensor and tactical data, developing automated processes, and integrating beyond line-of-sight sensing. We discuss concepts for displaying and fusing multi-sensor and tactical data within an Enhanced Operator Control Station (EOCS). The sensor data can originate from the Coyote's own visible-band and IR cameras, laser rangefinder, and ground-surveillance radar, as well as beyond line-of-sight systems such as a mini-UAV and unattended ground sensors. The authors address technical issues associated with the use of fully digital IR and day video cameras and discuss video-rate image processing developed to assist the operator to recognize poorly visible targets. Automatic target detection and recognition algorithms processing both IR and visible-band images have been investigated to draw the operator's attention to possible targets. The machine generated information display requirements are presented with the human factors engineering aspects of the user interface in this complex environment, with a view to establishing user trust in the automation. The paper concludes with a summary of achievements to date and steps to project completion.
A single, stationary observer cannot determine a unique target track with bearings-only measurements. In the land environment, for tactical reasons, the observer typically remains stationary but can measure the target range by a laser rangefinder (LRF). Bearings-only tracking of a non-maneuvering target is a non-linear problem. Solutions by iteration or the extended Kalman filter suffer from a high computation load and possible filter divergence. In contrast, the pseudo-linear formulation permits the application of a linear Kalman filter but the range estimate has a bias, which eliminates through instrumental variables. The development in showed that even though a target track is indeterminate due to a stationary observer, a unique target heading is still available from the bearings-only measurements. Then after an LRF range measurement, Rl, future estimates of target position and velocity become determinant. This paper gives a new tracking scheme for a stationary observer that gives the range estimate as a function of Rl, the target heading and bearings. The estimation equation comes from the trigonometric Law of Sines and is simple to implement. The estimator is unbiased and simulation experiments have shown that the estimates are close to the Cramer-Rao Lower Bound.
While there are many techniques for Bearings-Only Tracking (BOT) in the ocean environment, they do not apply directly to the land situation. Generally, for tactical reasons, the land observer platform is stationary; but, it has two sensors, visual and infrared, for measuring bearings and a laser range finder (LRF) for measuring range. There is a requirement to develop a new BOT data fusion scheme that fuses the two sets of bearing readings, and together with a single LRF measurement, produces a unique track. This paper first develops a parameterized solution for the target speeds, prior to the occurrence of the LRF measurement, when the problem is unobservable. At, and after, the LRF measurement, a BOT formulated as a least squares (LS) estimator then produces a unique LS estimate of the target states. Bearing readings from the other sensor serve as instrumental variables in a data fusion setting to eliminate the bias in the BOT estimator. The result is recursive, unbiased and decentralized data fusion scheme. Results from two simulation experiments have corroborated the theoretical development and show that the scheme is optimal.
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