Motion Parameter Space Research Articles

Diagnosing the Stellar Population and Tidal Structure of the Blanco 1 Star Cluster

Abstract We present the stellar population, using Gaia DR2 parallax, kinematics, and photometry, of the young (∼100 Myr), nearby (∼230 pc) open cluster, Blanco 1. A total of 644 member candidates are identified via the unsupervised machine learning method StarGO to find the clustering in the five-dimensional position and proper motion parameter (X, Y, Z, , μ δ ) space. Within the tidal radius of 10.0 ± 0.3 pc, there are 488 member candidates, 3 times more than those outside. A leading tail and a trailing tail, each of 50–60 pc in the Galactic plane, are found for the first time for this cluster, with stars further from the cluster center streaming away faster, manifest stellar stripping. Blanco 1 has a total detected mass of 285 ± 32 M ⊙ with a mass function consistent with a slope of α = 1.35 ± 0.2 in the sense of , in the mass range of 0.25–2.51 M ⊙, where N is the number of members and m is stellar mass. A minimum spanning tree (ΛMSR) analysis shows the cluster to be moderately mass segregated among the most massive members (≳1.4 M ⊙), suggesting an early stage of dynamical disintegration.

Timing and Parameter Optimization for One-time Motion Problem Based on Reinforcement Learning

Baseball hitting, swatter swing and football catching, there are many tasks can be seen as a one-time action, whose goal is to control the timing and parameters of the action to achieve optimal results. Many one-time motion problems are difficult to obtain the optimal policy through model solving, and model-free reinforcement learning has advantages for such problems. However, although reinforcement learning has developed rapidly, there is currently no universal one-time motion problem algorithm architecture. Decomposing the one-time motion problem into the action timing problem and the action parameter problem, we construct a suitable reinforcement learning method for each of them. We design a combination mechanism that allows the two modules to learn simultaneously by passing the estimated value between the two modules while interacting with the environment. We use REINFORCE + DPG to solve the problem of continuous motion parameter space, and use REINFORCE + Q learning to solve the problem of discrete motion parameter space. To testing the algorithm model, we designed and realized an aircraft bombing simulation environment. The test results show that the algorithm can converge quickly and stably, and is robust to different time step and observation errors.

A Subspace Hybrid Integration Method for High-Speed and Maneuvering Target Detection

Long-time integration is an effective method to improve target detection performance in a noisy background. However, when detecting high-speed and maneuvering targets by long-time integration, it is easy to encounter the across range unit and across Doppler unit effects, which deteriorate the detection performance of algorithms with low computational complexity, such as moving target detection and hybrid integration (HI). The generalized Radon–Fourier transform (GRFT) detector has proven to have the best detection performance. However, the GRFT has a high computational burden for an ergodic search in a multidimensional motion parameter space and thus is hardly employed in real engineering applications. In this paper, we propose subspace HI (SHI) to achieve a good balance between detection performance and computational complexity. SHI first divides the parameter space into several equidimensional subspaces and moves them to the center of the coordinate system. Then, HI is implemented on all the subspaces, and all the HI results are finally fused. Through parameter space division and subspace movement, the values of the parameters in the subspaces are reduced, which increases the subaperture length, i.e., the time that the target echoes stay in one range-Doppler unit. By increasing the subaperture length, SHI gradually improves the detection performance with an increase in the computational complexity. Conversely, by shortening the subaperture length, the computational complexity of SHI can be reduced at the expense of the detection performance. Compared with HI and the GRFT, SHI achieves a better compromise between detection performance and computational complexity.

Short‐Range Multitarget Motion Parameter Estimation Method Based on Hough Transform

This study proposes a novel short-range multitarget motion parameter estimation method based on Hough transform. The proposed method can be used for multitarget detection, motion parameter estimation, and data association. In our proposed method, the measured radial distance and Doppler frequency versus time data is mapped to the motion parameter space by Hough transform. The motion parameter space data is binarized to determine the number of targets. The unsupervised nearest neighbor clustering technique is used to determine the search space of targets. The maximum value in each search space is estimated as the motion parameter of the corresponding target. Simulation results show that the proposed method has higher parameter estimation accuracy than that of conventional methods.

A New Coherent Integration Method for Frequency Jittering Radar

For the frequency jittering radar, the problems of range migration and phase jittering among different pulses may occur during the long-time integration, which will affect the target energy integration. Therefore, a new coherent integration algorithm, namely, Frequency jittering based Radon-Fourier transform (FJ-RFT), is proposed. Based on jointly searching in the motion parameter space, the problems mentioned above can be simultaneously resolved and the coherent integration can be then realized. The Cramer-Rao lower bound (CRLB) and the Blind speed sidelobes (BSSL) of the proposed FJ-RFT are analyzed in detail. Finally, numerical experiments are provided to demonstrate the effectiveness of the proposed method.

A Coherent Integration Method via Radon-NUFrFT for Random PRI Radar

To deal with the problems of range cell migration (RCM) and spectrum spread during the integration time induced by the motion of a target, this paper proposes a new coherent integration method based on Radon nonuniform fractional Fourier transform (NUFrFT) for random pulse repetition interval radar. In this method, RCM is eliminated via searching in the motion parameter space and the spectrum spread is resolved by NUFrFT along the searching trajectory. Simulation results demonstrate the effectiveness of the proposed algorithm.

A Fast Algorithm of Generalized Radon-Fourier Transform for Weak Maneuvering Target Detection

The generalized Radon-Fourier transform (GRFT) has been proposed to detect radar weak maneuvering targets by realizing coherent integration via jointly searching in motion parameter space. Two main drawbacks of GRFT are the heavy computational burden and the blind speed side lobes (BSSL) which will cause serious false alarms. The BSSL learning-based particle swarm optimization (BPSO) has been proposed before to reduce the computational burden of GRFT and solve the BSSL problem simultaneously. However, the BPSO suffers from an apparent loss in detection performance compared with GRFT. In this paper, a fast implementation algorithm of GRFT using the BSSL learning-based modified wind-driven optimization (BMWDO) is proposed. In the BMWDO, the BSSL learning procedure is also used to deal with the BSSL phenomenon. Besides, the MWDO adjusts the coefficients in WDO with Levy distribution and uniform distribution, and it outperforms PSO in a noisy environment. Compared with BPSO, the proposed method can achieve better detection performance with a similar computational cost. Several numerical experiments are also provided to demonstrate the effectiveness of the proposed method.

High-dimensional camera shake removal with given depth map.

Camera motion blur is drastically nonuniform for large depth-range scenes, and the nonuniformity caused by camera translation is depth dependent but not the case for camera rotations. To restore the blurry images of large-depth-range scenes deteriorated by arbitrary camera motion, we build an image blur model considering 6-degrees of freedom (DoF) of camera motion with a given scene depth map. To make this 6D depth-aware model tractable, we propose a novel parametrization strategy to reduce the number of variables and an effective method to estimate high-dimensional camera motion as well. The number of variables is reduced by temporal sampling motion function, which describes the 6-DoF camera motion by sampling the camera trajectory uniformly in time domain. To effectively estimate the high-dimensional camera motion parameters, we construct the probabilistic motion density function (PMDF) to describe the probability distribution of camera poses during exposure, and apply it as a unified constraint to guide the convergence of the iterative deblurring algorithm. Specifically, PMDF is computed through a back projection from 2D local blur kernels to 6D camera motion parameter space and robust voting. We conduct a series of experiments on both synthetic and real captured data, and validate that our method achieves better performance than existing uniform methods and nonuniform methods on large-depth-range scenes.

Fixation Simplifies 3D Motion Estimation

Fixation is defined as holding the gaze direction towards the same environmental point through time. It was proven in the past that fixation reduces the number of unknowns in passive visual navigation from five to four. In this paper, we show that fixation further simplifies 3D motion estimation because it decouples the motion parameter space. We project the spherical motion field in two latitudinal directions with respect to two different poles of the image sphere. The first projection enables the computation of the longitude of the translation direction and the torsion. The second projection gives the angle between the direction of fixation and translation, as well as the time to collision to the fixated scene point. Both computational steps are based on one-dimensional searches along meridians of the image sphere. The observer may move with all six degrees of freedom. We do not use the efference copy of the fixational rotation of the camera. Performance of the algorithm is tested on real world sequences with fixation accomplished either off-line or during the recording using an active camera. A comprehensive review of most theories on advantages of fixation clarifies the differences to our approach.

Mechanism concept retrieval using configuration space

When designing mechanisms, making use of examples in past designs and handbooks should lead to cost reduction by promoting the sharing of parts and subassemblies among the products as well as reduction of time and effort. At present, however, the process of surveying design examples is left almost entirely to human designers and little computerised aid has been developed. We propose a computerised method of retrieving mechanism concepts from a library by specifying a required behaviour using qualitative configuration space as a retrieval index. First, mechanism concepts and their kinematics characteristics are described and stored in a computerised library using qualitative configuration spaces accompanined by additional information such as motion type and motion transmission direction. To retrieve mechanism concepts which realise specific kinematic behaviour, designers specify the required behaviour as timing charts of given input and intended output motions. Motion types, motion transmission direction, and motion speed dependence of the input and output motions can also be specified. Computer programs translate the required timing charts into required locus patterns in motion parameter space, and then available mechanism concepts to realise the behaviour are retrieved based on pattern matching between the qualitative configuration spaces and the locus patterns. The method is implemented as an experimental computer program written in Prolog and applied to simple mechanism design problems as examples to confirm the effectiveness of the approach.

Estimating motion and structure from correspondences of line segments between two perspective images

Presents an algorithm for determining 3D motion and structure from correspondences of line segments between two perspective images. To the author's knowledge, this paper is the first investigation of use of line segments in motion and structure from motion. Classical methods use their geometric abstraction, namely straight lines, but then three images are necessary for the motion and structure determination process. In this paper the author shows that it is possible to recover motion from two views when using line segments. The assumption used is that two matched line segments contain the projection of a common part of the corresponding line segment in space, i.e., they overlap. Indeed, this is what the author uses to match line segments between different views. This assumption constrains the possible motion between two views to an open set in motion parameter space. A heuristic, consisting of maximizing the overlap, leads to a unique solution. Both synthetic and real data have been used to test the proposed algorithm, and excellent results have been obtained with real data containing a relatively large set of line segments.

Mechanism Concept Retrieval Using Configuration Space.

When designing mechanisms, designers now can use computational tools to confirm kinematic behavior of mechanisms after they have designed and described the mechanism structure. As for the process of designing the mechanism to realize the kinematic behavior they want, however, the process is still up to the designer's intuition and little computerized aid has been developed. This paper proposes a computerized method of mechanism concept retrieval by behavioral specification. Behavioral characteristics of mechanism concepts are described as configuration spaces and stored in a mechanism concept database. The kinematic behavior a designer wants to realize is described as a locus pattern in a motion parameter space. Then available mechanism concepts to realize the behavior can be retrieved based on pattern matching between the configuration spaces and the locus pattern. The method is implemented as an experimental computer program and applied to simple mechanism design problems as examples.