Initial Target Position Research Articles

The effect of real-world and retinal motion on speed perception for motion in depth.

For motion in depth, even if the target moves at a constant speed in the real-world (physically), it would appear to be moving with acceleration on the retina. Therefore, the purpose of this study was to determine whether real-world and retinal motion affect speed perception in depth and to verify the influence of eye movements on both motion signals in judging speed in depth. We used a two-alternative forced-choice paradigm with two types of tasks. One stimulus moved at a constant speed in the real-world (world constant task) with three conditions: 80-60 cm (far), 60-40 cm (middle), and 40-20 cm (near) from the participant. The other stimulus moved at a constant speed on the retina (retinal constant task) with three conditions: 4-8 deg (far), 8-12 deg (middle), and 12-16 deg (near) as the vergence angle. The results showed that stimulus speed was perceived faster in the near condition than in the middle and far conditions for the world constant task, regardless of whether it was during fixation or convergence eye movements. In contrast, stimulus speed was perceived faster in the order of the far, middle, and near conditions for the retinal constant task. Our results indicate that speed perception of a visual target approaching the observer depends on real-world motion when the target position is relatively far from the observer. In contrast, retinal motion may influence speed perception when the target position is close to the observer. Our results also indicate that the effects of real-world and retinal motion on speed perception for motion in depth are similar with or without convergence eye movements. Therefore, it is suggested that when the visual target moves from far to near, the effects of real-world and retinal motion on speed perception are different depending on the initial target position.

Predictability of Arctic sea ice drift in coupled climate models

Abstract. Skillful sea ice drift forecasts are crucial for scientific mission planning and marine safety. Wind is the dominant driver of ice motion variability, but more slowly varying components of the climate system, in particular ice thickness and ocean currents, bear the potential to render ice drift more predictable than the wind. In this study, we provide the first assessment of Arctic sea ice drift predictability in four coupled general circulation models (GCMs), using a suite of “perfect-model” ensemble simulations. We find the position vector from Lagrangian trajectories of virtual buoys to remain predictable for at least a 90 (45) d lead time for initializations in January (July), reaching about 80 % of the position uncertainty of a climatological reference forecast. In contrast, the uncertainty in Eulerian drift vector predictions reaches the level of the climatological uncertainty within 4 weeks. Spatial patterns of uncertainty, varying with season and across models, develop in all investigated GCMs. For two models providing near-surface wind data (AWI-CM1 and HadGEM1.2), we find spatial patterns and large fractions of the variance to be explained by wind vector uncertainty. The latter implies that sea ice drift is only marginally more predictable than wind. Nevertheless, particularly one of the four models (GFDL-CM3) shows a significant correlation of up to −0.85 between initial ice thickness and target position uncertainty in large parts of the Arctic. Our results provide a first assessment of the inherent predictability of ice motion in coupled climate models; they can be used to put current real-world forecast skill into perspective and highlight the model diversity of sea ice drift predictability.

Mislocalization after inhibition of saccadic adaptation.

Saccadic eye movements are often imprecise and result in an error between expected and actual retinal target location after the saccade. Repeated experience of this error produces changes in saccade amplitude to reduce the error and concomitant changes in apparent visual location. We investigated the relationship between these two plastic processes in a series of experiments. Following a recent paradigm of inhibition of saccadic adaptation, in which participants are instructed to look at the initial target position and to continue to look at that position even if the target were to move again, our participants nevertheless perceived a visual probe presented near the saccade target to be shifted in direction of the target error. The location percept of the target gradually shifted and diverged over time from the executed saccade. Our findings indicate that changes in perceived location can be the same even when changes in saccade amplitude differ according to instruction and can develop even when the amplitude of the saccades executed during the adaptation procedure does not change. There are two possible explanations for this divergence between the adaptation states of saccade amplitude and perceived location. Either the intrasaccadic target step might trigger updating of the association between pre- and post-saccadic target positions, causing the localization shift, or the saccade motor command adjusts together with the perceived location at a common adaptation site, downstream from which voluntary control is exerted upon the executed eye movement only.

Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm

The problem of passive localization using an unmanned aerial vehicle (UAV) swarm is studied. For multi-UAV localization systems with limited communication and observation range, the challenge is how to obtain accurate target state consistency estimates through local UAV communication. In this paper, an ant colony pheromone mechanism-based passive localization method using a UAV swarm is proposed. Different from traditional distributed fusion localization algorithms, the proposed method makes use of local interactions among individuals to process the observation data with UAVs, which greatly reduces the cost of the system. First, the UAVs that have detected the radiation source target estimate the rough target position based on the pseudo-linear estimation (PLE). Then, the ant colony pheromone mechanism is introduced to further improve localization accuracy. The ant colony pheromone mechanism consists of two stages: pheromone injection and pheromone transmission. In the pheromone injection mechanism, each UAV uses the maximum likelihood (ML) algorithm with the current observed target bearing information to correct the initial target position estimate. Then, the UAV swarm weights and fuses the target position information between individuals based on the pheromone transmission mechanism. Numerical results demonstrate that the accuracy of the proposed method is better than that of traditional localization algorithms and close to the Cramer–Rao lower bound (CRLB) for small measurement noise.

Visual guidance of locomotor interception is based on nulling changes in target bearing (not egocentric target direction nor target-heading angle).

In two experiments we studied how participants steer to intercept uniformly moving targets in a virtual driving task under hypotheses-differentiating conditions of initial target eccentricity and target motion. In line with our re-analysis of findings from earlier studies, in both experiments the observed interception behavior could not be understood as resulting from reliance on (changes in) egocentric target direction nor from reliance on (changes in) target-heading angle. The overall pattern of results observed was however compatible with a control strategy based on nulling changes in the target's bearing angle. The presence of reversals in movement direction under specific combinations of target eccentricity and motion conditions indicated that the information used was not purely rate-of-change (i.e., first-order) based but carried traces of an influence of initial target position. In Experiment 2 we explicitly tested the potential role of early reliance on perceived egocentric target direction by examining the effects of a 10° rotation of the visual scene (i.e., of both target and environment). While such a rotation gave rise to minor changes in the moment of initiation of the first steering action, contrary to predictions it did not affect the characteristics of the direction-reversal phenomenon. We conclude that the visual guidance of locomotor interception is best understood as resulting from nulling changes in the target's bearing angle, with such nulling perhaps best conceived as being fractional-order (rather integer-order) driven.

Robust Measurement-Driven Cardinality Balance Multi-Target Multi-Bernoulli Filter.

The multi-target tracking filter under the Bayesian framework has strict requirements on the prior information of the target, such as detection probability density, clutter density, and target initial position information. This paper proposes a novel robust measurement-driven cardinality balance multi-target multi-Bernoulli filter (RMD-CBMeMBer) for solving the multiple targets tracking problem when the detection probability density is unknown, the background clutter density is unknown, and the target’s prior position information is lacking. In RMD-CBMeMBer filtering, the target state is first extended, so that the extended target state includes detection probability, kernel state, and indicators of target and clutter. Secondly, the detection probability is modeled as a Beta distribution, and the clutter is modeled as a clutter generator that is independent of each other and obeys the Poisson distribution. Then, the detection probability, kernel state, and clutter density are jointly estimated through filtering. In addition, the correlation function (CF) is proposed for creating new Bernoulli component (BC) by using the measurement information at the previous moment. Numerical experiments have verified that the RMD-CBMeMBer filter can solve the multi-target tracking problem under the condition of unknown target detection probability, unknown background clutter density and inadequate prior position information of the target. It can effectively estimate the target detection probability and the clutter density.

A shadow of the repulsive Rutherford scattering in the laboratory frame

The paper explores the Rutherford scattering shadow in an entire class of comoving frames—inertial frames moving along the initial projectile direction—of which the laboratory frame, where the target is initially at rest, is a representative example. The paper is a continuation of the previous work addressing the scattering shadow in the fixed-target and the center-of-mass frame. It is shown that the transition from these frames is technically quite involved, due to the scattering shadow forming at an infinite distance from an initial target position. The central procedure involves solving the 5th degree polynomial as a part of an associated extremization procedure. The shadow existence itself is subject to certain conditions, dependent on a given comoving frame. A new and unexpected phenomenon is found within a certain set of comoving frames, including the laboratory frame itself. It consists of a phase transition between an entirely smooth type of shadow and the one characterized by a formation of a sharp edge.

Defender-Aware Attacking Guidance Policy for the Target–Attacker–Defender Differential Game

Deep reinforcement learning was used to train an agent within the framework of a Markov decision process (MDP) to pursue a target, while avoiding a defender, for the target–attacker–defender (TAD) differential game of pursuit and evasion. The aim of this work was to explore the games where the previous attacking guidance methods found in literature failed to capture the target. The reward function of the MDP presented by this work allowed for an attacking agent to learn a policy that expanded the number of cases where the target is captured beyond the former limit of success through the application of the twin delayed deep deterministic policy gradient algorithm. The strategy developed using artificial intelligence expands the target capture guidance approach to enable the attacker to avoid the defender in states where the two agents are in close proximity. Initial target positions within a limited set were considered with fixed values for agent velocities and attacker and defender initial positions to evaluate the attacker’s learned behavior in comparison with the optimal point capture guidance laws for target capture in the TAD game.

Optimal Multiplicative Generalized Coordinated Search Technique to Find a D-Dimensional Random Walker

This paper presents a new interesting search model that minimises the expected value of the total detection cost of the d-dimensional random walk target with maximum probability. This technique is called a generalised coordinated linear search technique with multiple searchers. The target may be in one of the d-directions (dimensions) inside the space. We study this technique from a probabilistic and optimisation point of view where each direction is considered as a cylinder and it is searched by two searchers. They start the searching process from any point rather than the origin. The target's initial position is a random variable vector with a known probability distribution. We show the existence of a finite search plan by using the analytical methods. To minimise the expected value of the first meeting time between one of the searchers and the target, we should discuss the existence of the optimality conditions for this search plan and then find the optimal search plan. A numerical example illustrates the effectiveness of this technique.

Unmanned Aerial Vehicle Path Planning using Bat Algorithm

Unmanned Aerial Vehicles (UAV) was introduced after World War II. In 1980’s UAV consider as important weapon system. Initially UAV needs initial position and target position. In this paper bat algorithm is proposed with mixed objective constraints which helps in directing the UAV. The process is initialized by generating the initial population of bat. Then by updating the population size and generation of bat the fitness value with minimum frequency is found that helps to avoid convergence among UAV. Finally the evaluation which gives minimum frequency is considered as optimal solution.

Integration of Eye-Centered and Landmark-Centered Codes in Frontal Eye Field Gaze Responses.

The visual system is thought to separate egocentric and allocentric representations, but behavioral experiments show that these codes are optimally integrated to influence goal-directed movements. To test if frontal cortex participates in this integration, we recorded primate frontal eye field activity during a cue-conflict memory delay saccade task. To dissociate egocentric and allocentric coordinates, we surreptitiously shifted a visual landmark during the delay period, causing saccades to deviate by 37% in the same direction. To assess the cellular mechanisms, we fit neural response fields against an egocentric (eye-centered target-to-gaze) continuum, and an allocentric shift (eye-to-landmark-centered) continuum. Initial visual responses best-fit target position. Motor responses (after the landmark shift) predicted future gaze position but embedded within the motor code was a 29% shift toward allocentric coordinates. This shift appeared transiently in memory-related visuomotor activity, and then reappeared in motor activity before saccades. Notably, fits along the egocentric and allocentric shift continua were initially independent, but became correlated across neurons just before the motor burst. Overall, these results implicate frontal cortex in the integration of egocentric and allocentric visual information for goal-directed action, and demonstrate the cell-specific, temporal progression of signal multiplexing for this process in the gaze system.

Tracking small targets in infrared image sequences under complex environmental conditions

An infrared small target tracking algorithm based on multiple cues fusion and Kalman filter is proposed for complex environmental conditions in this paper. Firstly, Kalman filter is utilized to predict the initial target position which is set to the center of region of interest (ROI). Secondly, the gray, contrast, and motion cues in the ROI are extracted to generate the confidence map to locate the small target. At last, the fusion weights and the target models are updated, and the estimated target position is used as a measurement of Kalman filter. The results of experiment on different infrared image sequences have verified that the robustness and accuracy of tracking can be improved effectively by our proposed algorithm for complex environmental conditions.

Target location of unmanned aerial vehicles based on the electro-optical stabilization and tracking platform

Target location is a major function of unmanned aerial vehicles (UAV). In aerial photogrammetry, due to the large object distance, various slight measurement errors will be magnified, resulting in a significant decrease in target location precision. In order to improve the precision of target location, an innovative method of UAV target location is designed. This method utilizes the feature of the airborne electro-optical stabilization and tracking platform (EOSTP) which can track a ground target and keep it in the center of the camera’s field of view to make multiple observations of one target point. The nonlinear system model of the target space position is built according to the geometric relationship between the line-of-sight (LOS) pointing vector and the target point, and then the Cubature Kalman Filter (CKF) algorithm is used to estimate the target position. Considering that CKF algorithm is easily affected by the initial value errors, the initial target position estimation method based on digital elevation model (DEM) approximation is proposed. The validity of the location algorithm is verified via simulation experiments and the results of actual flight test show that this algorithm can improve the location accuracy of ground target in rough terrain area obviously.

Research on Probability of Detection of Submarine Time Limit Based on Continuous Time within Finite Region

The sonar detection of underwater submarines in a certain sea area was modeled by using the sonar in the sea area. The problem of continuous detection time of underwater large random targets was studied based on the fixed detection points. According to the characteristics of the target’s motion, this paper puts forward the research method of detecting the probability of finding the time limit of the random moving target by fixed point detection, establishes the random constant velocity target distribution model under the target initial position distribution, uses this model to carry on the duration. The effective probing time and the redundant probing time exist in the probing and continuous probing times. The probabilistic trend of the probing probability under the probing limit time decreases with the increase of the distance between the probing point and the distribution center. The effective probing time length increases with the probing point The distance from the center generally increases, but there is no linear rule.

Online Model Updating and Dynamic Learning Rate-Based Robust Object Tracking.

Robust visual tracking is a significant and challenging issue in computer vision-related research fields and has attracted an immense amount of attention from researchers. Due to various practical applications, many studies have been done that have introduced numerous algorithms. It is considered to be a challenging problem due to the unpredictability of various real-time situations, such as illumination variations, occlusion, fast motion, deformation, and scale variation, even though we only know the initial target position. To address these matters, we used a kernelized-correlation-filter-based translation filter with the integration of multiple features such as histogram of oriented gradients (HOG) and color attributes. These powerful features are useful to differentiate the target from the surrounding background and are effective for motion blur and illumination variations. To minimize the scale variation problem, we designed a correlation-filter-based scale filter. The proposed adaptive model’s updating and dynamic learning rate strategies based on a peak-to-sidelobe ratio effectively reduce model-drifting problems by avoiding noisy appearance changes. The experiment results show that our method provides the best performance compared to other methods, with a distance precision score of 79.9%, overlap success score of 59.0%, and an average running speed of 74 frames per second on the object tracking benchmark (OTB-2015).

An optimal two-stages search plan for a random walk target motion in the plane

This paper addresses the problem of searching for a moving target in the plane by a single autonomous sensor platform unmanned air vehicle (UAV). This sensor consists of a search team from two-searchers. The target moves in the plane with autocollimator linear motion (one-dimensional random walk) either for x-axis or y-axis. The search consists of two stages, the broad search and investigating search. In a broad search the sensor wishes to find the target's initial position, which is given by the value of the two independent random variables (X, Y ) and they have joint symmetric probability density function f(x, y). In an investigation search stage, the search team will be unmanned to detect the target on one of two real lines intersected at the target's initial position. It is desired to search in an optimal manner in each stage to minimise the expected value of the first meeting time between one of the searchers and the target, assuming circular normal distributed estimates of its initial position.

Extended social force model‐based mean shift for pedestrian tracking under obstacle avoidance

It has been shown that mean shift tracking algorithm can achieve excellent results in pedestrian tracking task. It empirically estimates the target position of current frame by locating the maximum of a density function from the local neighborhood of the target position of previous frame. However, this method only considers its past trajectory without considering the influence of pedestrian environment when applying to pedestrian tracking. In practical, pedestrians always keep a safe distance away from obstacles when programming their paths. To address the issue of obstacle avoidance, this paper proposes a novel extended social force model‐based mean shift tracking algorithm in which pedestrian environment is full taken in consideration. Firstly, an extended social force model is presented to quantify the interaction between pedestrian and obstacle by means of force. Furthermore, directional weights and speed weights are introduced to adjust the strength of the force in terms of the difference of individual perspectives and relative velocities. Finally, the initial target position is predicted by Newton's laws of motion and then the Mean Shift method is integrated to track target position. Experiment results show that this algorithm achieves an encouraging performance when obstacles exist.

Effect of electromagnetic pulse transverse inhomogeneity on ion acceleration by radiation pressure

During ion acceleration by radiation pressure, a transverse inhomogeneity of an electromagnetic pulse leads to an off-axis displacement of the irradiated target, limiting the achievable ion energy. This effect is analytically described within the framework of a thin foil target model and with particle-in-cell simulations showing that the maximum energy of the accelerated ions decreases as the displacement from the axis of the target's initial position increases. The results obtained can be applied to the optimization of ion acceleration by the laser radiation pressure with mass-limited targets.

Underwater Target Detection Based on Electric-Field Sensor Line Array

The electric-field, caused by corrosion and prevention of corrosion method was used to overcome the deficiency for acoustic detection system in underwater target detection. The research on electric-field detection was based on electric-field sensor linear array and digital signal processing technology. Firstly, the electric-field distribution feature was simulated by electric-dipole model in shallow sea. The electric-field sensor vertical line arrays were laid regularly to obtain electric-field signal. Then, the generalized likelihood ratio (GLRT) method was used to process target detection and parameter estimation. Finally, the simulation experiment was done and indicated that the method could accurately realize parameter estimation on target initial position, velocity and direction, etc all when the signal-to-noise ratio (SNR) was greater than 0dB. The error became larger as the SNR reduced. The GLRT still could detect target when the SNR reduced to-10dB and partial parameter estimation error became larger.

A SEARCH ALLOCATION GAME WITH PRIVATE INFORMATION OF INITIAL TARGET POSITION

This paper deals with a two-person zero-sum search game, in which a searcher distributes search resource to detect a target and the target moves to evade the searcher. The game includes private information of an initial position of the target and a detection probability of the target as payoff. The searcher estimates the initial position with a probability distribution. We model the problem as an incomplete-infor game, and propose a convex programming formulation and a linear programming one to derive an optimal distribution of search resource of the searcher and an optimal target strategy of selecting paths. However, the number of paths exponentially increases as the number of time points becomes larger. To cope with the combinatorial explosion, we propose a new approach using Markov movement strategy of the target. By some numerical examples, we analyze players' optimal strategies and evaluate the value of information of the target initial position.