Exoatmospheric Target Research Articles

Semi-Analytical Midcourse Guidance for Coordinated Interception of Multiple Exo-Atmospheric Targets

The simultaneous interception of multiple exo-atmospheric threat targets can leave no buffer time for the targets and greatly reduces targets’ penetration probability. This paper proposes centralized and decentralized two midcourse guidance methods to solve the coordinated interception of multiple exo-atmospheric targets. First, the free-time cooperative interception problem minimizing the total impulses is established, where the nonlinear dynamics of the interceptors and targets, the initial impulse constraints, and the terminal interception constraints are considered. Second, the necessary conditions for the optimal cooperative interception problem are derived and then approximated using higher-order Taylor expansions, which result in a series of analytical nonlinear equations. Then, the semi-analytical centralized guidance mode is designed to achieve simultaneous interception of targets by directly solving the analytical nonlinear equations. To improve computational efficiency, the decentralized cooperative guidance mode is further proposed by using the developed iterative freezing trajectory method, where each interceptor uses information from other interceptors in the previous iteration for prediction and distributed computation. Numerical examples verify the effectiveness and robustness of the proposed two guidance methods. Results show that the computational efficiency of the decentralized guidance mode is more efficient compared with the centralized midcourse guidance mode.

Detection and Tracking of Weak Exoatmospheric Target with Elliptical Hough Transform

An elliptical Hough transform (EHT) algorithm is proposed in the framework of track-before-detect (TBD) for joint detection and tracking of weak exoatmospheric targets. The new approach exploits the fact that when restricted to a two-body problem, the exoatmospheric target often follows an elliptical orbit, and thus the Hough transform integrated with orbital geometry information would have better detection performance. The relationship between the original radar measurements in data space and the elliptical parameters in parameter space is explicitly derived with multiple steps of coordinate transformation. It is found that the data points mapping into the parameter space essentially represent a quartic curve. An EHT-based algorithm is then designed, and orbit planarity is also taken into account to reduce the effect of noise accumulation. The influences of primary and secondary thresholds and the signal-to-noise ratio (SNR) on the detection performance are compared by simulations. Additionally, a real radar tracking dataset from a scientific satellite on 28 May 2017 is used to investigate the efficiency of the method. By adding some imaginary clutter to the raw orbit, the results indicate that it is very effective in detecting the real satellite trajectory in a low signal-to-noise ratio (SNR) environment. The advantage of the new method lies in it can not only simultaneously detect and track weak exoatmospheric targets but also can predict the trajectory by using these available detected parameters.

Reinforcement Metalearning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loop

This Paper uses Reinforcement Meta-Learning to optimize an adaptive integrated guidance, navigation, and control system suitable for exoatmospheric interception of a maneuvering target. The system maps observations consisting of strapdown seeker angles and rate gyroscope measurements directly to thruster on/off commands. Using a high fidelity six-degree-of-freedom simulator, this Paper demonstrates that the optimized policy can adapt to parasitic effects including seeker angle measurement lag, thruster control lag, the parasitic attitude loop resulting from scale factor errors and Gaussian noise on angle and rotational velocity measurements, and a time-varying center of mass caused by fuel consumption and slosh. Importantly, the optimized policy gives good performance over a wide range of challenging target maneuvers. Unlike previous work that enhances range observability by inducing line of sight oscillations, this Paper’s system is optimized to use only measurements available from the seeker and rate gyros. Through extensive Monte Carlo simulation of randomized exoatmospheric interception scenarios, this Paper demonstrates that the optimized policy gives performance close to that of augmented proportional navigation with perfect knowledge of the full engagement state. The optimized system is computationally efficient and requires minimal memory and should be compatible with today’s flight processors.

Single Space Based Sensor Bias Estimation Using a Single Target of Opportunity

Sensor registration refers to the estimation/compensation of systematic (bias) errors, in contrast to the random errors from sensor noise. Various methods have been proposed for bias estimation of multiple optical sensors using common targets of opportunity. However, the proposed solutions required the use of multiple (two or more) optical sensors and the need to solve the data association problem arising from the fusion of measurements from multiple sensors. In order to remove these constraints, we provide in this paper a new methodology using a single exoatmospheric target of opportunity seen in a single satellite borne sensor's field of view to estimate the sensor's biases simultaneously with the state of the target. The satellite-based sensor sees the target from a changing direction as a function of its position, allowing the target in this nonlinear tracking system to be observable. The sensor provides the line of sight (LOS) measurements of azimuth and elevation to the target. Sensor pointing calibration is the key precondition for accurate tracking of a target in a space-based system. Statistical tests on the results of simulations, and the evaluation of the Cramer–Rao lower bound (CRLB) on the covariance of the bias estimates show that this method is statistically efficient.

Recurrent neural networks for discrimination of exo-atmospheric targets based on infrared radiation signature

Exo-atmospheric infrared (IR) target discrimination is an important research problem in space attack and defense. The different micro-motion states of the targets result in respective characteristics in the obtained IR radiation intensity sequences, and this difference is difficult to describe intuitively and extract effectively. Few methods can effectively contact the data with the micro-motion model, resulting in a low classification accuracy and difficult to meeting actual application requirements. We set up four types of targets by constructing the micro-motion model of the exo-atmospheric targets, including the warhead and heavy decoy, with spinning and coning motion; and the light decoy and debris, with tumbling motion, to get the IR radiation intensity sequences. We use random projection to improve the discrimination power of recurrent neural network, and to classify the time series of IR radiation intensity. Experimental results demonstrate that random projection recurrent neural network (R-RNN) is more effective than several other typical algorithms in time series classification (TSC) task, which can achieve an excellent target discrimination. We also analyze the effect of noise on the performance of the algorithm.

외기권 표적 요격을 위한 제어시간 구속조건을 가지는 일반화된 유도법칙

외기권 표적 요격을 위한 제어시간 구속조건을 가지는 일반화된 유도법칙

Micromotion dynamics and geometrical shape parameters estimation of exoatmospheric infrared targets

The micromotion dynamics and geometrical shape are considered to be essential characteristics for exoatmospheric targets discrimination. Many methods have been investigated to retrieve the micromotion features using radar signals returned from targets of a given shape. We explore a way of jointly estimating micromotion dynamics and geometrical shape parameters from the infrared (IR) signals of targets in remote detection distance. It is found that the micromotion dynamics of the target would induce a periodic fluctuating variation on the IR irradiance intensity signature. In addition to the micromotion characteristics, the fluctuation could also reflect target structure properties, which offer a possible clue in extracting the features of micromotion dynamics and geometrical shape. Thus, the data model of target IR irradiance intensity signatures induced by micromotion patterns including spinning, coning, and tumbling is developed, and a method of parameters estimation based on joint optimization analysis techniques is proposed. Experimental results demonstrated that the parameters of target micromotion dynamics and geometrical shape can be effectively estimated using the proposed method, if the input signature contains multiple dominant frequency components.

Exo-atmospheric target discrimination using probabilistic neural network

Exo-atmospheric targets are especially difficult to distinguish using currently available techniques, because all target parts follow the same spatial trajectory. The feasibility of distinguishing multiple type components of exo-atmospheric targets is demonstrated by applying the probabilistic neural network. Differences in thermal behavior and time-varying signals of space-objects are analyzed during the selection of features used as inputs of the neural network. A novel multi-colorimetric technology is introduced to measure precisely the temporal evolutional characteristics of temperature and emissivity-area products. To test the effectiveness of the recognition algorithm, the results obtained from a set of synthetic multispectral data set are presented and discussed. These results indicate that the discrimination algorithm can obtain a remarkable success rate.

A track filter for reentry objects with uncertain drag

A filter was developed for maintaining track on ballistic missiles whose drag profiles are unknown or deviate significantly from prior predictions. The filter employs an innovative form of a seven-state Kalman filter in which object drag is included as a state to be estimated. Using measurements of range, azimuth, and elevation, the filter can track endo- and exo-atmospheric targets on a wide variety of trajectories without requiring a priori tuning to account for variations in reentry angle, drag history, measurement signal-to-noise ratio, etc. The filter was designed to be implemented at the millimeter wave (MMW) radar (a high-range-resolution, narrow beamwidth, Ka-band radar) located at Kwajalein Missile Range (KMR) in the Marshall Islands. Extensive testing and comparisons using a high fidelity simulation showed the new filter to be robust to a wide variety of trajectories and substantially better than track filters presently used at KMR. The filter was coded to run efficiently in real time, installed at the MMW radar, and successfully used to track an intercontinental ballistic missile (ICBM) with varying drag characteristics through exo-atmospheric and reentry phases. The filter yielded a more accurate and responsive track than possible with the previously used filter on a similar trajectory.

Filter sensitivity in exoatmospheric target vehicle tracking

An analysis of the orbit determination capabilities of the miniature seeker technology integration (MSTI) program was conducted. In the analysis an extended Kalman filter and an adaptive extended Kalman filter were used to estimate the position and velocity of an exoatmospheric target vehicle. A number of cases were investigated, including measurements from up to three spaced-based observers using angles-only measurements; angles plus range measurements; and angles, range, and range-rate measurements at each observation time. The target was tracked from the burnout point at 59 sec after launch through 180 sec after launch and final estimates for position and velocity were computed at 180 sec. Both the extended Kalman filter and the adaptive extended Kalman filter were found to be unsuitable for estimates using one satellite and angles-only measurements. The estimates were greatly improved when adding range measurements or a second observation satellite. The addition of a third observation satellite did not greatly improve the estimation results. It was also found that adding range-rate measurements improved the velocity results, but degraded the position estimates. In addition, the extended Kalman filter was determined to give good overall results, while the adaptive extended Kalman filter produced excellent results in certain cases and poor results in other. The results are graphically presented and conclusions are provided concerning use of the filters.

Satellite intercept targeting DSP algorithm design and evaluation

A unified processing approach for the detection and localization of satellites or other exoatmospheric targets is presented. Enhanced filtering and centroid determination algorithms were developed based on real-time implementation constraints, mission conditions including accuracy requirements, and signature structure. The signature structure consisted of target returns, high-frequency random noise, low-frequency structured noise, and contamination sources resulting from debris and cosmic events. Detailed signal processing analyses were performed to verify the required subsample accuracy and the sensitivity to hardware and system constraints. The resultant system design was functionally verified in a real-time breadboard processor.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>