The term benchmark originates from the chiseled horizontal marks that surveyors made, into which an angle-iron could
be placed to bracket ("bench") a leveling rod, thus ensuring that the leveling rod can be repositioned in exactly the same
place in the future. A benchmark in computer terms is the result of running a computer program, or a set of programs, in
order to assess the relative performance of an object by running a number of standard tests and trials against it. This
paper will discuss the history of simulation benchmarks that are being used by multiple branches of the military and
agencies of the US government. These benchmarks range from missile defense applications to chemical biological
situations. Typically, a benchmark is used with Monte Carlo runs in order to tease out how algorithms deal with
variability and the range of possible inputs. We will also describe problems that can be solved by a benchmark.
Proc. SPIE. 4048, Signal and Data Processing of Small Targets 2000
KEYWORDS: Target detection, Radar, Signal to noise ratio, Detection and tracking algorithms, Monte Carlo methods, Electronic filtering, Algorithm development, Performance modeling, Systems modeling, Data fusion
In a previous paper, the authors proposed a new general and systematic electronic counter-countermeasure (ECCM) technique called the Decomposition and Fusion (D&F) approach. This ECCM is implemented within the multiple target-tracking framework for protection against range- gate-pull-off (RGPO) and range false target ECM techniques. The original formulation left open the specific multiple target tracking framework. In this paper, we develop a specific implementation of the D&F technique and evaluate it within the Benchmark 2 Problem environment. Simulation results are presented showing the track-loss rejection capabilities and the track accuracy performance of the D&F technique.
Range deception, such as range-gate-pull-off (RGPO) is a common electronic countermeasure (ECM) technique used to defeat or degrade tracking radars. Although a variety of heuristic approaches/tricks have been proposed to mitigate the impact of this type of ECM on the target tracking algorithms, none of them involve a systematic means to reject the countermeasure signals. This paper presents a general and systematic approach, called Decomposition and Fusion (DF) approach, for target tracking in the presence of range deception ECM and clutter. It is effective against RGPO, range-gate-pull-in, and range false target ECM techniques for a radar system where the deception measurements have virtually the same angles as the target measurement. This DF approach has four fundamental components: (a) decomposing the validated measurements by determination of range deception measurements using hypothesis testing; (b) running one or more tracking filters using the detected range deception measurements only; (c) running a conventional tracking-in-clutter filter using the remaining measurements; (d) fusing the tracking filters by a probabilistically weighted sum of their estimates. Several algorithms within the DF approach are discussed.