Harris Corporation

Charles A Baird

Guy M Pelchat

Noel Collins

Stored digital terrain data are used as a parameter for a passive ranging exteded Kalman filter in a target range measurement system. The system accurately locates ground based targets using platform referenced passive sensors. The Kalman filter algorithm fuses angular target measurements (azimuth and elevation) from available sensors (FLIR, RFR, etc.) along with stored digital terrain data to obtain recursive least-square error estimates of target location. An iterative algorithm calculates the slant range to the intersection of the target's line of sight vector with the digital terrain data base. This calculated slant range is used as an input to the Kalman filter to complement the measured azimuth and elevation inputs. The Kalman filter uses the calculated range measurement to update the target location estimate as a function of terrain slope. The system arrives at a rapid solution by using the stored digital terrain data to provide estimates of range. The Kalman filter provides the framework for fusion, filtering of the measurement noise, and automatic triangulation when owncraft maneuvers improve observability. Results from a Monte Carlo simulation of the algorithm, using real terrain data, are presented. Measurement noise effects, and the more dominant terrain effects on the system estimation accuracy are analyzed.

Terrain aided passive range estimation

A drift correction mechanism for a vehicle (e.g. aircraft) navigation system makes use of a digital map data base, in order to correct for errors in position estimates, so that the estimated flight path of the aircraft will more closely track its actual flight path, and thereby effectively compensate for the long term error to which the navigation system is subjected. During the flight, altimeter readings are periodically carried out in order to generate elevation values that are representative of the actual elevation of the terrain over which the aircraft is flying. These elevation values are then used as inputs to a digital map database, from which elevation profile contour lines of respective elevation values may be derived. At the time that a respective altimeter reading is effected, the position estimate output of the aircraft's navigation system is also sampled. This position estimate is then compared with the contour line that passes through the actual position of the aircraft at the time the altimeter measurement is made. The separation between the navigation system's position estimate and that geographical position on the contour line which is closest to the position estimate is then used as a correction factor to update the position estimate to a set of corrected geographical coordinates that more closely approximate the coordinates of the actual flight path of the aircraft.

Elevation map-referenced mechanism for updating vehicle navigation

A navigation system contains a modified Kalman filter processor which continuously receives both TERCOM and SITAN control information so that the operation of the SITAN processing is effectively continuously optimized. The system employs an over flight terrain data storage map to which position and altitude signals are coupled for extracting both elevation and slope information from the stored map. The extracted elevation and slope data are coupled, together with the outputs of baromatic and radar altimeter sensors and estimated altitude and position data outputs from the navigation unit, to a correlation/modified Kalman filter processor. Elevation profile data accessed from the map is correlated with elevation profile signals derived from aircraft on-board sensors, in order to find that flight path on the stored map, parallel to, but displaced from the flight path indicated by the navigation unit, for which successive elevation data values correspond most closely to the elevations measured by the altimeter sensors. In this correlation processing operation the 'most likely' path is selected by defining a performance index associated with each path and selecting that path with the best performance index. The result of the correlation processing provides a position fix to be combined with the position estimates in the modified Kalman filter.

Map-aided navigation system employing TERCOM-SITAN signal processing

An adaptive interference reduction system for removing cross talk from a dual polarization system comprising a first adjustable filter for varying the phase and the weight of a first input signal, a second adjustable filter for varying the phase and the weight of a second input signal, a first adder for adding this first input signal and the weighted and phase adjusted output from the second adjustable filter, a second adder for adding the second input signal and the weighted and phase adjusted output from the first adjustable filter, a first noise-to-signal measuring device for generating a signal proportional to the noise-to-signal ratio in the output signal from the first adder, a second noise-to-signal measuring device for generating a signal proportional to the noise-to-signal ratio in the output signal from the second adder, and a dither-type control logic for dithering in-phase and quadrature square-waves in accordance with the signals generated by the first and second noise-to-signal measuring devices to control the first and second adjustable filters. This system is further characterized in that the first and second noise-to-signal measuring devices comprise a slow response control circuit for maintaining the average value of the input signal from the adder at a reference value, and means for filtering this averaged value and detecting the power fluctuations remaining in the signal and generating a signal proportional to these fluctuations. Additionally, the first and second adjustable filters may be structured to have transfer functions across a predetermined bandwidth of W.sub.0 [1+W.sub.1 .omega.+W.sub.2 .omega..sup.2 ] or W.sub.0 [1+W.sub.1 .omega.+W.sub.2 .omega..sup.2 +W.sub.3 .omega..sup.3 ], where W.sub.0 , W.sub.1, W.sub.2 and W.sub.3 are signal weights and .omega. represents the frequency variation of the input signal from its carrier.

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Melbourne Beach, FL, United States of America

Charles A Baird