Maneuvering Target Tracking Method Research Articles

A Method to Track Moving Targets Using a Doppler Radar Based on Converted State Kalman Filtering

Strong nonlinearity between Doppler measurement and target motion in Doppler radar target tracking leads to the inadequate utilization of measurement information and limited tracking accuracy. We solved this problem by combining converted state Kalman filtering and the Interacting Multiple Model. This maneuvering target tracking method is suitable for Doppler measurement. First, we converted the target motion in the Cartesian coordinate to the polar coordinate. Then, we expanded the measurement equation to include Doppler measurement, making target motion linearly related to the Doppler radar observation vectors and allowing efficient utilization of measurement information. Next, we used unscented transformation to calculate the statistical characteristics of the process noise in the polar coordinate. This process helps to reduce the noise error caused by the coordinate system transformation in the original converted state Kalman filter. Finally, the system effectively tracks targets that may perform maneuvers with unknown motion during actual tracking. Using the converted state Kalman filter with Doppler measurement as a sub-filter, an Interacting Multiple Model tracking method can be constructed to adjust the model probabilities without going through nonlinear transformation. Simulation results show that the technique can achieve effective target tracking in Doppler measurement application scenarios and has higher tracking accuracy in non-maneuvering and maneuvering scenarios.

A Data-Driven Maneuvering Target Tracking Method Aided With Partial Models

A Data-Driven Maneuvering Target Tracking Method Aided With Partial Models

A modified adaptive Kalman filtering method for maneuvering target tracking of unmanned surface vehicles

The filtering methods are crucial for an unmanned surface vehicle (USV) to realize target tracking. Due to the poor observation caused by the strong vibration during the navigation of the USV, the target tracking accuracy of the traditional filtering method has been significantly degraded. A modified strong tracking-based expended Sage-Husa adaptive robust Kalman filter (MST-ESHARKF) algorithm is proposed to overcome this problem in this paper. In the proposed algorithm, a modified fading factor for the strong tracking Kalman filter is introduced to eliminate disturbance-induced filter divergence. In addition, the adaptive factor of robust Kalman filtering is designed to balance the predicted and observed states dedicated to improving the robustness of the algorithm. Finally, the biased and unbiased estimators for measurement and process noise covariances are merged, and the measurement noise covariance matrix’s interval is constrained, resulting in a simultaneous evaluation of measurement and process noise covariance matrices with improved dependability of the proposed algorithm. The simulation and experiment results show that the proposed MST-ESHARKF outperforms the existing filters in target tracking.

Sensor cooperative scheduling method for low-altitude maneuvering target tracking in complex environment

PurposeContinuous and stable tracking of the low-altitude maneuvering targets is usually difficult due to terrain occlusion and Doppler blind zone (DBZ). This paper aims to present a non-myopic scheduling method of multiple radar sensors for tracking the low-altitude maneuvering targets. In this scheduling problem, the best sensors are systematically selected to observe targets for getting the best tracking accuracy under maintaining the low intercepted probability of a multi-sensor system.Design/methodology/approachFirst, the sensor scheduling process is formulated within the partially observable Markov decision process framework. Second, the interacting multiple model algorithm and the cubature Kalman filter algorithm are combined to estimate the target state, and the DBZ information is applied to estimate the target state when the measurement information is missing. Then, an approximate method based on a cubature sampling strategy is put forward to calculate the future expected objective of the multi-step scheduling process. Furthermore, an improved quantum particle swarm optimization (QPSO) algorithm is presented to solve the sensor scheduling action quickly. Optimization problem, an improved QPSO algorithm is presented to solve the sensor scheduling action quickly.FindingsCompared with the traditional scheduling methods, the proposed method can maintain higher target tracking accuracy with a low intercepted probability. And the proposed target state estimation method in DBZ has better tracking performance.Originality/valueIn this paper, DBZ, sensor intercepted probability and complex terrain environment are considered in sensor scheduling, which has good practical application in a complex environment.

Performance comparison of CKF and UKF in passive state noise of UAV

Interacting multiple model (IMM) is widely used in target tracking, and is recognized as the most effective method for maneuvering target tracking. In order to remove the motion state noise in UAV passive location, this paper uses the idea of target tracking algorithm for reference. By setting the observation station data points in advance, introducing detection factors into CV and CT models, and then constructing measurement equations through geometric kinematics and fusing IMM algorithm, the filtering performance of IMM-CKF-CV-CT and IMM-UKF-CV-CT under typical s-maneuver in two-dimensional plane is studied. The simulation results of MATLAB show that IMM-CKF-CV-CT has higher accuracy and stronger stability than IMM-UKF.

A Bank of Decentralized Extended Information Filters for Target Tracking in Event-Triggered WSNs

This paper presents a hierarchical estimation method for maneuvering target tracking in event-triggered wireless sensor networks. First, several process noise covariances are chosen to characterize the dynamic characteristic of the target in the presence of maneuvers, and a bank of decentralized extended information filters (DEIFs) are used to generate state estimates of the target. Second, the estimates from the DEIFs are combined by covariance intersection (CI) to obtain an improved state estimate while still maintaining a consistent estimate. Thus, the DEIF and the CI methods form complementary advantages by satisfying the requirement of the consistency in the hierarchical estimation framework. Finally, both simulations and experiments of a target tracking example demonstrate that the proposed method is more suitable for applications to the maneuvering target tracking and it achieves a more satisfactory performance than the conventional DEIF method.

Hybrid Sequential Fusion Estimation for Asynchronous Sensor Network-Based Target Tracking

This brief presents a sequential fusion estimation method for maneuvering target tracking in asynchronous wireless sensor networks. The modeling error caused by asynchronous sampling and communication uncertainties is considered and compensated for by introducing a time-varying fading factor into the unscented Kalman filter (UKF). A square root form of the unscented strong tracking filter (SR-USTF) based on QR decomposition is proposed to improve the stability and performance of the USTF. Moreover, a hybrid sequential fusion estimation method is presented to estimate the state of the target, and the proposed sequential fusion estimation method combines the superiorities of both the SR-USTF and the conventional UKF, and is able to deal with communication uncertainties such as time delay and packet loss in a unified framework. Both simulations and experiments of an E-puck robot tracking example are provided to demonstrate the effectiveness and superiorities of the proposed sequential fusion estimation method.

Auxiliary truncated particle filtering with least-square method for bearings-only maneuvering target tracking

In the paper, a novel auxiliary truncated particle filtering for bearings-only maneuvering target tracking (ATPF-BOT) is proposed. In the proposed algorithm, a modified prior probability density function (PDF) is derived to solve the modeling uncertainty problem, which can simultaneously incorporate current measurement information and target characteristic information. Meanwhile, the proposal distribution is jointly designed by using the prior PDF and the modified prior PDF. Moreover, the proposal distribution is approximately calculated based on adaptive least square method so as to apply the ATPF algorithm for bearings-only maneuvering target tracking, and a practical algorithm is also developed. The experiment results show that the proposed algorithm is computationally efficient and successfully implemented in bearings-only target tracking systems.

Shipborne radar maneuvering target tracking based on the variable structure adaptive grid interacting multiple model

The trajectory of a shipborne radar target has a certain complexity, randomness, and diversity. Tracking a strong maneuvering target timely, accurately, and effectively is a key technology for a shipborne radar tracking system. Combining a variable structure interacting multiple model with an adaptive grid algorithm, we present a variable structure adaptive grid interacting multiple model maneuvering target tracking method. Tracking experiments are performed using the proposed method for five maneuvering targets, including a uniform motion — uniform acceleration motion target, a uniform acceleration motion — uniform motion target, a serpentine locomotion target, and two variable acceleration motion targets. Experimental results show that the target position, velocity, and acceleration tracking errors for the five typical target trajectories are small. The method has high tracking precision, good stability, and flexible adaptability.

Maneuvering Target Tracking Algorithm Based on Improved "Self-Adaptive Turning Rate" Model

Multi-model (interactive multi-model )tracking is an effective method for maneuvering target tracking, and turning model is widely studied and applied in multi-model tracking modeling and model set establishment. A detailed study is carried out on the self-adaptive turning model modeling method for maneuvering target tracking. Also a summary is made about the method of establishing tracking model by calculating the turning rate. The shortcomings of literature [4、6]'s self-adaptive turning model in actual application is also pointed out, with two modeling methods adopting average turning rate presented accordingly. The simulation test proves the necessity of improvement and the validity of the new model.

Novel Approach for Nonlinear Maneuvering Target Tracking Based on Input Estimation

In this paper, a new method for maneuvering target tracking (MTT) based on nonlinear input estimation (IE) is innovated and is employed for tracking of maneuvering targets. Proposed method augments the states and unknown inputs (maneuvers) in a higher order state space realization and estimates both of them simultaneously. The concurrent estimation of states and inputs eliminates the maneuver detection delay which is popular in the conventional IE methods. The proposed method has excellent performance for both maneuvering and non-maneuvering stages. Also, a model with highly nonlinear dynamics for maneuvering targets is given and is used in the numerical simulations to analyze the performance of the proposed maneuvering target tracking method. Furthermore the proposed method is compared with a conventional IE method and the simulation results show the effectiveness of the proposed method.

Maneuvering Target Tracking Method based on Unknown but Bounded Uncertainties

AbstractIn this paper a new maneuvering target tracking method based on a model with unknown but bounded uncertainties is proposed. Firstly, a model with unknown but bounded uncertainties is proposed. Then, an algorithm for tracking the target in both maneuvering and non-maneuvering cases is generated. The proposed method can be used for a large diversity of input types. Also, this method does not require maneuver detection and covariance resetting that are necessary in previous maneuvering target tracking methods. Therefore, it doesn't consume any time for maneuver detection and covariance resetting. Also simulation results are provided to confirm the theoretical development.

Method for maneuvering target video frequency tracking based on inductive factor of posture information

A method for maneuvering target tracking based on inductive factor of posture information is proposed. A distinguished characteristic of video frequency tracking is that it can capture the target posture changes from its picture easily, and the posture change means the motive model of the target will change. This information is very important to predict the trace of maneuvering target. Based on this idea, the quantified values of the target posture change are obtained using Hough algorithm, this key values are defined as inductive factor of posture information, and then, the multiple grey trace predict models are established and the degrees of fuzzy subordinate values for every model are calculated with the inductive factor, the maneuvering extent values are determined by a new analysis method of stochastic differential equations for each model used to modify the degree of fuzzy subordinate values, these constitute the weighted values for every grey predict collection. Finally, the synthesis predicting weighted result is obtained. The experimental results show that the new method is superior to the conventional algorithm.