Sensor management of LEO constellation using modified binary particle swarm optimization

Analyzing the space-borne observation platform's line-of-sight measurement error (SOPLME) is of great significance for the improvement of the space-borne optical tracking system. In this regard, we consider the space-borne observation platform (SOP) as a multi-body system and build both the target imaging model from the inertial space to the optical camera image plane and the line-of-sight measurement model. The relationship between the SOPLME and the satellite's orbit error, attitude error, platform angular vibration error, inner and outer frame rotation error, and position quantization error of target image point is deduced theoretically. However, since there are many error sources and the modeling process is highly nonlinear, the amplification of the output range caused by the ''wrapping effect'' may occur when using the interval calculation method to analyze the output range of the SOPLME. To solve this problem, we propose a novel method of interval analysis by using statistical information. This method combines the convenience of interval representation with the high accuracy of probability calculation, which can effectively avoid the overestimation problem caused by the inherent ''wrapping effect'' of the interval calculation method. Firstly, we describe the parameters of each error source with the interval constructed by upper and lower bounds. Secondly, the optimal Latin hypercube sampling (LHS) matrix of error source parameters is designed by the quasi-optimization method. Then we use the Gaussian mixture model (GMM) to approximate the distribution function of the SOPLME. Finally, the output range of the SOPLME is estimated by Taylor expansion. The simulation test verifies the reliability of the model and interval calculation method in this paper. The simulation example shows that the proposed method can get a more reliable error output interval with less sampling amount, which is suitable for analyzing the dynamic process of the output interval of the SOPLME. INDEX TERMS Space-borne observation platform, line-of-sight measurement error, interval analysis, Latin hypercube sampling, Gaussian mixture model.

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“…The mapping process of target imaging is divided into two parts: According to equations (1), (2), and 3, we can get:…”

Section: B Imaging Model and Line-of-sight Measurement Error Model Omentioning

confidence: 99%

“…Space Surveillance System (SBSS) [1], the Space Tracking and Surveillance System (STSS) [2], the Hubble Space Telescope (HST) [3], and the James Webb Space Telescope (JWST) [4]. At the same time, the photoelectric tracker is used in SBSS, STSS, and JWST to reduce the cost of satellite control.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Statistical Information for Interval Analysis: A Method for Analyzing the Interval Uncertainty of Line-of-Sight Measurement Error of Space-Borne Observation Platforms

Ding

Yang

et al. 2020

IEEE Access

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“…In general, the space-based infrared sensor obtains the lineof-sight (LOS) measurement information of the target relative to the satellite by effectively suppressing the background clutter and using the contrast between the target and the background. In [32], we have developed the infrared sensor measurement model, as shown in Fig. 1.…”

Section: Infrared Sensor Measurement Modelmentioning

confidence: 99%

Sensor management of LEO constellation based on covariance control

Qin¹,

Liang²

2019

Journal of Systems Engineering and Electronics

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This paper studies the multi-sensor management problem for low earth orbit (LEO) infrared warning constellation used to track a midcourse missile. A covariance control approach, which selects sensor combinations or subset based on the difference between the desired covariance matrix and the actual covariance of each target, is used for sensor management, including some matrix metrics to measure the differentia between two covariance matrices. Besides, to meet the requirements of the space based warning system, the original covariance control approach is improved. Simulation results demonstrate that the covariance control approach is able to provide a better tracking performance by providing a well-designed desired covariance and balance tracking performance goals with system demands.

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“…There are three main sensor management methods: covariance matrix‐based method [10–12], information gain‐based method [13, 14], and risk control‐based method [15–17]. For example, Qin and Liang in [11, 12] addressed the scheduling problem of low earth orbit (LEO) infrared constellation to minimise the tracking error of midcourse ballistic missiles with limited sensor resources. In [13], for wireless sensor networks, a sensor selection algorithm is given based on the Fisher information matrix.…”

Section: Introductionmentioning

confidence: 99%