An experimental implementation of a particle-based dynamic sensor steering method for tracking and searching for space objects

Hobson, Tyler A.; Clarkson, I. Vaughan L.

doi:10.1109/icassp.2014.6855162

Cited by 3 publications

(3 citation statements)

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“…The most straightforward approach to this initial search problem is a maximum-probability approach [11,15,16]. This approach points the sensor at the most probable candidate direction to capture the object in the FOV.…”

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“…For analysis of the maximum-probability approach and the information-theoretic approach, we consider the same LEO-based single-sensor single-object search problem studied in the literature [17]. Following the convention in the past literature [11,[15][16][17][18], a binary sensor model is used where the sensor provides a binary detection measurement, i.e., either a detection or no detection measurement related to the presence of the object in the FOV; known nonzero probabilities for false alarms and missed detections are assigned. This simple model is expected to reflect the context of the initial sensor search, where our goal is to obtain observations of the object as soon as possible and as accurately as possible.…”

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“…This simple model is expected to reflect the context of the initial sensor search, where our goal is to obtain observations of the object as soon as possible and as accurately as possible. In addition, we also follow the literature [11,[15][16][17][18] and use a particle representation for the object's state probability distribution; these particles provide dual roles in approximating the distribution as well as discretizing the control search space defining how to steer the sensor at each observational time step.…”

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Analysis of Information-Theoretic Initial Sensor Search Method for Space Situation Awareness

Beeson

Tomita

et al. 2021

Journal of Guidance, Control, and Dynamics

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Over the years, the number of resident space objects has grown dramatically, and thus there is a growing need for characterizing the environment and potential threats in space to ensure safe space activities. To this end, the technologies to manage sensors to detect, identify, and predict orbiting small objects has become critical for space situational awareness (SSA) [1]. A large body of literature approached this sensor management problem by focusing on the assignment of sensors to objects [2][3][4][5][6][7][8]. These investigations have generally focused on the global SSA problem, with a goal to answer the question: given a network of sensors and prior probability distributions for the entire population of space objects, which sensors should be tasked to observe which objects?Distinct from global SSA problems, another class of problems is related to a spacecraft's ability to maintain awareness of its own local environment using onboard sensors and being provided no or little cueing information. This class of problems can be referred to as local SSA. A motivating example is a single spacecraft with an angles-only sensor that has been provided limited cueing information on a single space object. The cueing information takes the form of a description of the probability density function of the object's orbit. The problem at hand is to task the spacecraft's sensor to search for the object. Under the typical assumptions of global SSA tasking methods, a single sensor and a single object is a trivial tasking problem. Under the assumptions in this Note, however, this problem is challenging because the projection of the object's orbit into the angles-only measurement space is assumed to be much larger than the sensor's field of view (FOV) [9,10]. This scenario is illustrated in Fig. 1. Many sensor measurements will result in no detection of the object. Whereas sensor-tasking methods for global SSA often neglect the information contained in

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