SBIR-STTR Award

A situation awareness system must combine information from many different sensors and platforms and construct a composite air picture for the commander. Sensors transmit the target position information over data-links. Due to the large volume of data transmitted over these links, it is essential to conserve the bandwidth of such links by not sending position updates at all times. This proposal seeks to develop an algorithm for minimizing bandwidth demands by using predictive Kalman filters which are widely used in speech compression. With this technique, the sensor updates are estimated as a function of previous updates, with the predictor coefficients changing as the signal statistics change. The difference between the actual and estimated signal statistics change. The difference between the actual and estimated signal is known as the residual signal. This algorithm will minimize the energy or variance of this signal so that they can be quantized with fewer levels than the original input. Thepredictor coefficients are calculated by a Kalman filter algorithm. T he Kalman filter is used to identify a predictable component in the signal which can be described by a few parameters. Bandwidth is conserved by not sending updates if a predicted position is within position oerror requirements, and if maximum bandwidth allications are exceeded.

Keywords:
Situation Awareness, Surveillance, Kalman Filter, Bandwidth Compression, Data Link, Target Tracking

Situation awareness systems must process information from various platforms, and distribute it to participating units through communication links. Such systems are improtant for effective monitoring, assessment, and mission planning. Due to the large volume of situation awareness data transmitted over the communication links, it is essential to converse bandwidth. In Phase I, considerable bandwidth saving was demonstrated using simulated and recorded test data, by implementing two predictive error encoding algorighms (Linear Adaptive and Linear Regression) which do not require transmission of target position updates at all times. This proposal for Phase II seeks to extend these algorithms by introducing more realistic communication channels that will include time delay, channel noise, asynchronous transmission, etc. In order to demonstrate the level of maturity and proof of marketability, this software will be tested for a wide range of scenarios using simulated GPS data. This software will also be integrated with Commander's Decision Aid, Distributed Fact Base, and Route Checker. Such software will provide a synchronization matrix of tasks and schedule of key actions as wella s graphical overlays including assembly areas, unit boundaries, routes, phase lines, etc. Extent of bandwidth conservation will be determined by testing the software with the System Performance Model.

Keywords:
Situational Awareness; Bandwidth Conservation; Predictive Error Encoding; Surveillance; Kalman Filte