Current Statistic Research Articles

Vehicle Trajectory Prediction Method Based on “Current” Statistical Model and Cubature Kalman Filter

Vehicle motion trajectory prediction is the basis of vehicle collision early warning or vehicle conflict resolution. In order to improve the accuracy of trajectory prediction, a vehicle trajectory prediction method based on “current” statistical (CS) model and cubature Kalman filter (CKF) is proposed. This method considers the acceleration variation rules in the actual motion process of the vehicle in the state equation, so that the estimated value of the acceleration can be consistent with the real range. This condition overcomes the limitation of the general trajectory prediction model, which ignores the acceleration change, so it improved prediction accuracy. In addition, this method also avoids the large amount of computational resources required, being that some new methods describe the real acceleration fluctuations. The vehicle trajectory at the intersection that crossed by Yingbin Avenue and Qiche Avenue in Nanchang is selected to verify the tracking performance of Constant Acceleration-Unscented Kalman Filter (CA-UKF), Current Statistical-Unscented Kalman Filter (CS-UKF), and CS-CKF models. The results show that the CS-CKF model has superior prediction effectiveness than the CA-UKF and CS-UKF models, and it improves the accuracy of vehicle motion trajectory prediction.

Adaptive Markov IMM Based Multiple Fading Factors Strong Tracking CKF for Maneuvering Hypersonic-Target Tracking

Hypersonic targets have complex motion states and high maneuverability. The traditional interactive multi-model (IMM) has low tracking accuracy and a slow convergence speed. Therefore, this paper proposes a strong tracking cubature Kalman filter (CKF) adaptive interactive multi-model (AIMM) based on multiple fading factors. Firstly, this paper analyzes the structure of the CKF algorithm, introduces the fading factor of the strong tracking algorithm into the covariance matrix of the time update and measurement update, and adjusts the filter gain online and in real time, which can reduce the decline infilter accuracy caused by model mismatch. Secondly, Singer model, “current” statistical (CS) model, and Jerk model are selected in the model set of IMM and introduced singular value decomposition (SVD) decomposition to solve the problem that Cholesky decomposition cannot be performed in the CKF due to the model dimension expansion. Last, an adaptive algorithm for the Markov matrix in the IMM is proposed. The transition probability was adaptively modified by the value of the model likelihood function to enhance the proportion of matching models. The simulation results show that the proposed algorithm enhanced the proportion of matching models in the IMM and improved the tracking accuracy by 16.51% and the convergence speed by 37.5%.

A Particle PHD Filter for Dynamic Grid Map Building towards Indoor Environment

The PHD (Probability Hypothesis Density) filter is a sub-optimal multi-target Bayesian filter based on a random finite set, which is widely used in the tracking and estimation of dynamic objects in outdoor environments. Compared with the outdoor environment, the indoor environment space and the shape of dynamic objects are relatively small, which puts forward higher requirements on the estimation accuracy and response speed of the filter. This paper proposes a method for fast and high-precision estimation of the dynamic objects’ velocity for mobile robots in an indoor environment. First, the indoor environment is represented as a dynamic grid map, and the state of dynamic objects is represented by its grid cells state as random finite sets. The estimation of dynamic objects’ speed information is realized by using the measurement-driven particle-based PHD filter. Second, we bound the dynamic grid map to the robot coordinate system and derived the update equation of the state of the particles with the movement of the robot. At the same time, in order to improve the perception accuracy and speed of the filter for dynamic targets, the CS (Current Statistical) motion model is added to the CV (Constant Velocity) motion model, and interactive resampling is performed to achieve the combination of the advantages of the two. Finally, in the Gazebo simulation environment based on ROS (Robot Operating System), the speed estimation and accuracy analysis of the square and cylindrical dynamic objects were carried out respectively when the robot was stationary and in motion. The results show that the proposed method has a great improvement in effect compared with the existing methods.

Cyclist detection and tracking based on multi-layer laser scanner

The technology of Artificial Intelligence (AI) brings tremendous possibilities for autonomous vehicle applications. One of the essential tasks of autonomous vehicle is environment perception using machine learning algorithms. Since the cyclists are the vulnerable road users, cyclist detection and tracking are important perception sub-tasks for autonomous vehicles to avoid vehicle-cyclist collision. In this paper, a robust method for cyclist detection and tracking is presented based on multi-layer laser scanner, i.e., IBEO LUX 4L, which obtains four-layer point cloud from local environment. First, the laser points are partitioned into individual clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method based on subarea. Then, 37-dimensional feature set is optimized by Relief algorithm and Principal Component Analysis (PCA) to produce two new feature sets. Support Vector Machine (SVM) and Decision Tree (DT) classifiers are further combined with three feature sets, respectively. Moreover, Multiple Hypothesis Tracking (MHT) algorithm and Kalman filter based on Current Statistical (CS) model are applied to track moving cyclists and estimate the motion state. The performance of the proposed cyclist detection and tracking method is validated in real road environment.

Strong tracking SCKF based on adaptive CS model for manoeuvring aircraft tracking

A novel tracking algorithm is proposed by the integration of the adaptive current statistical (CS) model and the modified strong tracking (ST) square-root cubature Kalman filter (SCKF) for the manoeuvring aircraft tracking problem. Firstly, the acceleration recursion equation and the acceleration mean input estimation are combined in order to realise the adaptive adjustment of the CS model. Then, the introduced position of the fading factor is relocated from the orthogonality principle and a new formula is put forward. Additionally, the strong manoeuver detection function is established to adjust the manoeuvring frequency of the CS model. The simulation results show that the proposed algorithm possesses better tracking accuracy than the multiple-fading-factor SCKF based on the CS model, the SCKF-ST filter based on the modified CS model and the interacting-multiple-model (IMM)-SCKF. Moreover, the proposed algorithm decreases the runtime by 40% compared with the IMM-SCKF.

Robust adaptive filter for shipborne kinematic positioning and velocity determination during the Baltic Sea experiment

Information on trajectory and attitude is essential for analyzing gravimetric data collected on kinematic platforms. Usually, a Kalman filter is used to obtain high-accuracy positional and velocity information. However, this can be affected by measurement outliers and by state disturbances that occur frequently under a fast-changing environment. To overcome these problems, a robust adaptive Kalman filtering algorithm is applied for state estimates, which introduces an equivalent weight to resist measurement outliers and an optimal adaptive factor to balance the contributions of the kinematic model information and the measurements. In addition to the conventional robust estimator, an improved Current Statistical (CS) model is proposed. The improved CS model adopts a variance adaptive learning algorithm, and it can perform self-adaptation of acceleration variance with the innovation information; thus, it can overcome the shortcoming of lower tracking accuracy and avoid setting the maximum acceleration. Following a gravimetry campaign on the Baltic Sea, it is shown in theory and in practice that the robust adaptive Kalman filter is not only simple in its calculation but also more reliable in controlling the colored observation noise and kinematic state disturbance compared with the classical Kalman filter. The improved CS model performs best, especially when analyzing the positioning errors at the turns due to the target maneuvering. Compared to the CS model, the RMS values of the positional estimates derived from the improved CS model decrease by almost 30% in the horizontal direction, and no significant improvement in the vertical direction is found.

Adaptive Strong Tracking Square-Root Cubature Kalman Filter for Maneuvering Aircraft Tracking

A novel strong tracking square-root cubature Kalman filter (SCKF) based on the adaptive current statistical (CS) model is proposed aiming at the maneuvering aircraft tracking problem. The Jerk input estimation is introduced on the basis of the modified input estimation algorithm in order to make the connection with the state process noise and the state error covariance matrix. Thus, the online-adaptive adjustment of the CS model is achieved. Additionally, the introduced position of the fading factor is re-deduced and a novel calculation method is designed in order to overcome the invalidity problem of the traditional fading factor. Two simulation scenarios are conducted to verify the effectiveness of the proposed algorithm. The simulation results show that the proposed algorithm possesses better adaptability and tracking precision than the two state-of-the-art single model filters. Moreover, the proposed algorithm decreases the runtime by 40% while maintaining the comparable performance compared with the interacting-multiple-model SCKF.

Adaptive cubature Kalman filtering for distant speech tracking using a circular microphone array

A joint processing framework for distant speech tracking is proposed based on combination of an adaptive cubature Kalman Filter with a 80-element circular microphone array. The deconvolved convention beamforming (DCB) [T. C. Yang, 10.1109/JOE.2017.2680818] is carried out over the distant speech data for speech enhancement and bearing estimate. DCB provides superdirective beams and offers the same robustness as conventional beamforming. With the estimated speech bearing, a “current” statistical (CS) motion model is combined with a cubature Kalman filter to track the speech of interest. The CS model assumes a modified Rayleigh distribution for the acceleration probability density (of the moving source) whose mean value is the current acceleration. Based on this model, we utilize a zoom factor achieved by computing the norm of the tracking innovation to adjust the dynamic state model adaptively. Both numerical simulation and the outdoor experimental results show that the proposed framework in this paper can effectively track a maneuvering speech source.

Range tracking method based on adaptive “current” statistical model with velocity prediction

This study considers the problem of range tracking of a maneuvering target by an airborne digital array radar system. In the classic “current” statistical (CS) model, the maneuvering frequency α is not adaptive, and an acceleration limit has to be set. First, a new range tracking method called “adaptive CS model with velocity prediction” (ACSMVP) is proposed. This method utilizes the velocity prediction algorithm of cross product automatic frequency control with loop filter to measure the radial acceleration between the radar and the target. With the acceleration been obtained, the maneuvering frequency is derived, which is calculated in every tracking period. Second, an improved algorithm called “Sage–Husa-based ACSMVP” (SH-ACSMVP) is also proposed. In this algorithm, an online measuring noise covariance matrix is achieved by a simplified Sage–Husa estimator. Third, a Cramér-Rao lower bound of pulse-Doppler pulse train signal for range estimation is derived to assess the range tracking performance of the proposed algorithms. Simulations highlight the superior performance of our proposed algorithms over existing techniques.

A Study on Maneuvering Obstacle Motion State Estimation for Intelligent Vehicle Using Adaptive Kalman Filter Based on Current Statistical Model

The obstacle motion state estimation is an essential task in intelligent vehicle. The ASCL group has developed such a system that uses a radar and GPS/INS. When running on the road, the acceleration of the vehicle is always changing, so it is hard for constant velocity (CV) model and constant acceleration (CA) model to describe the motion state of the vehicle. This paper introduced Current Statistical (CS) model from military field, which uses the modified Rayleigh distribution to describe acceleration. The adaptive Kalman filter based on CS model was used to estimate the motion state of the target. We conducted simulation experiments and real vehicle tests, and the results showed that the estimation of position, velocity, and acceleration can be precise.

Tracking Algorithm Design and Comparison of High Speed High Maneuvering Target

Aiming at the problem that the traditional tracking method cannot track high speed high maneuvering target effectively, one modified fixed structure multiple model algorithm (M-FSMM) and one modified variable structure multiple model (M-VSMM) algorithm were proposed. The Constant Velocity (CV) model, Current Statistical (CS) and Modified Coordinate Turn (MCT) model were adopted in the M-FSMM algorithm, by means of Connected Graph (CG) thinking, the model connected graph was made up by models that can describe possible motion, the connected relation was set up and model self-adapting was designed to carry out the variable structure tracking that can quickly jump between models. Monte Carlo simulation results show that the two methods can track high speed high maneuvering target effectively, the computational quantum of M-FVMM algorithm is larger but the tracking accuracy and stability are better than the M-FSMM algorithm. They can be used to track near space hypersonic targets.

Airborne Single Observer Passive Tracking Algorithm Based on Improved CS Model

Aim at solving the problem that the target may exceed acceleration limit, a new passive tracking filtering algorithm based on “Current” Statistical (CS) model has been proposed. By designing nonlinear fuzzy membership functions, the new “Current” Statistical model filtering algorithm can adaptively adjust the acceleration upper and lower of CS model. Monte Carlo simulations of maneuvering targets show that the NCS algorithm has a better performance than the traditional CS algorithm.

Filtering Method for Pose Angles Based on CS-STF-IMM

In order to decrease the error of pose estimation based on image sequences, a filtering method for pose angles based on CS-STF-IMM was proposed. Firstly, current statistical (CS) model was used to establish state space model of pose transformation, which reflects the maneuver of target pose more truly. Then, strong tracking filter (STF) was introduced to overcome the shortcoming that Kalman filter can hardly track sudden maneuver. A parameters determination method based on least square fitting was proposed, which improves the performance of STF further. Based on these, CS-STF-IMM algorithm was established, which obviously improved the precision of pose estimation. Simulations showed that the proposed method has better overall performance, which is a feasible scheme for pose angles filtering.

Filtering Method for Pose Angles Based on CS-STF-IMM

In order to decrease the error of pose estimation based on image sequences, a filtering method for pose angles based on CS-STF-IMM was proposed. Firstly, current statistical (CS) model was used to establish state space model of pose transformation, which reflects the maneuver of target pose more truly. Then, strong tracking filter (STF) was introduced to overcome the shortcoming that Kalman filter can hardly track sudden maneuver. A parameters determination method based on least square fitting was proposed, which improves the performance of STF further. Based on these, CS-STF-IMM algorithm was established, which obviously improved the precision of pose estimation. Simulations showed that the proposed method has better overall performance, which is a feasible scheme for pose angles filtering.

Easy-hardware-implementation MMPF for Maneuvering Target Tracking: Algorithm and Architecture

In this paper, we present an easy-hardware-implementation multiple model particle filter (MMPF) for maneuvering target tracking. In the proposed filter, the sampling importance resampling (SIR) filter typically used for nonlinear and/or non-Gaussian application is extended to incorporating multiple models that are composed of a constant velocity (CV) model and a "current" statistical (CS) model, and the Independent Metropolis Hasting (IMH) sampler is utilized for the resampling unit in each model. Compared with the bootstrap MMPF, the proposed MMPF requires no knowledge of models and model transition probabilities for different maneuvering motions, and keeps a constant number of particles per model at all times. This allows a regular pipelined hardware structure and can be implemented in hardware easily. Furthermore, using the IMH sampler for the resampling unit avoids the bottleneck introduced by the traditional systematic resampler and reduces the latency of the whole implementation. Simulation results indicate that the proposed filter has approximately equal tracking performance with the bootstrap MMPF. Hardware architecture of the IMH sampler and its corresponding sample unit are presented, and a parallel architecture consisting of CV model processing element (PE), CS model PE and a central unit (CU) is described. The proposed architecture is evaluated on a Xilinx Virtex-II Pro FPGA platform for a maneuvering target tracking application and the results show many advantages of the proposed MMPF over existing approaches in terms of efficiency, lower latency, and easy hardware implementation.

The role of the home health nurse in sudden infant death syndrome

The home health nurse plays a vital role in providing supportive crisis intervention services to Sudden Infant Death Syndrome (SIDS) parents and extended family and friends. Current statis tical and epidemiological factors are discussed to prepare nurses to answer questions and coun sel families who have suffered the loss of a child to SIDS. Accurate information regarding the "Back to Sleep" campaign is important to counsel new parents and the newly bereaved parent. Terminology of SIDS and grief counseling must be carefully utilized. Through assessment and identification of normal grief patterns and complicated grief, the home health nurse can assist families and provide appropriate community resources. Telephone counseling and home visits are valuable tools that home health nurses may use in their role as community liaison between the SIDS Resource Center and other community resources. Key words: grief counseling, home health, nurse, SIDS