Mr. James L. Grantham Profile

James L. Grantham

at Computer Sciences Corp

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Publications (1)

Proceedings Article | 7 August 2002 Paper

Passive relative ranging applied to sensor-to-sensor data association

James Grantham, Steven Brahm, Randolph Zounes, Ellis Whitt

Proceedings Volume 4728, (2002) https://doi.org/10.1117/12.478516

KEYWORDS: Sensors, Error analysis, Ranging, Data modeling, Performance modeling, Infrared sensors, Radar, Imaging infrared seeker, Point spread functions, Detection and tracking algorithms

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Ballistic Missile Defense (BMD) effectiveness depends on a system's capability to acquire, track, identify, and engage threat missiles. The probability of a successful engagement can be improved by performing multiple-sensor data fusion, especially if the participating sensor systems are based on both radar frequency (RF) and infrared (IR) phenomenology. In this paper, we apply this observation to the Target Object Map (TOM) correlation problem for the standard configuration of a kill vehicle (with a single or multicolor IR seeker) receiving uplinks from a ground based radar. Specifically, we examine the application of a relative ranging technique that augments the angles-only track information of a passive IR sensor with non-parametric range-ranking of the threat complex. Since data association performance is significantly better for three-dimensional (3-D) matching that for two-dimensional (2-D) matching, the idea is to take advantage of relative range-ranking information of the threat complex to potentially improve performance. Numerous techniques that attempt to extract absolute range estimates from a passive IR sensor have been investigated by researchers in the BMD community and it is understood that range information allows for improved threat tracking, radiant intensity estimates, and data association performance. However, extracting absolute target range estimates from irradiance measurements is extremely difficult because of the presence of data uncertainties/ambiguities, environment and sensor noises, and small angular rates of tracked objects. Passive Relative Ranging (PRR) is distinct in that it focuses on the relative range-ranking of objects; knowledge that one object is closer than a second object, while not relevant for improving track or intensity estimation performance, can possibly improve the performance of sensor-to-sensor object assignment. The proposed PRR technique is based on the physical range-squared relationship between intensity and measured focal plane irradiance and the derived fact that threat objects at closer ranges with similar closing velocities have greater irradiance derivatives. This paper presents the theory behind PRR and presents preliminary performance results of the proposed PRR technique for statistically simulated data. Results are compared to those theoretically achievable, as determined by the Cramer-Rao Lower Bound (CRLB).

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