Moving Emitter Classification

One of the problems that plague emitter location systems is the presence of data from emitters that are in motion. If this motion is not detected, erroneous location estimates and filled data files result. An algorithm to classify an emitter as moving or stationary is presented along with extensive illustrations and results based upon simulated data.     

Application of Linear Statistical Models to Radar Location Techniques

The theory of linear statistical models is implemented to obtain an algorithm which accurately locates radar sites. True bearing and navigation data are used as input. The linear model developed is adaptable and allows removal of bearing errors that are nonrandom, or systematic. The model may be written in recursive form and used for real-time applications.     

Position Location: Triangulation Versus Circulation

Position location algorithms, using data from electronic surveillance systems have, for the most part, been based upon statistical analysis of the points of intersection of the lines of bearing. These triangulation ion algorithms may well minimize the effect of random measurement errors on the location estimate, but few existing algorithms account for systematic (bias) errors. An algorithm based upon intersecting circles which eliminates certain types of systematic error is presented with simulation data utilized to compare the position location estimates obtained from both triangulation and circulation algorithms.     

Navigation by Back Triangulation

The development of electronic surveillance systems that can accurately measure the direction of arrival of signals from ground radar stations has opened the possibility of using statistical techniques to determine navigation information for the measuring vehicle. The method of combining "bearing" observations from several radar sites for navigational purposes, referred to as "back triangulation", will be discussed and demonstrated by simulation.