Kalman Filter For Target Tracking Research Articles

GMM-Based Distributed Kalman Filtering for Target Tracking Under Cyberattacks

GMM-Based Distributed Kalman Filtering for Target Tracking Under Cyberattacks

Self-Tuning Process Noise in Variational Bayesian Adaptive Kalman Filter for Target Tracking

Many practical systems, such as target tracking, navigation systems, autonomous vehicles, and other applications, are usually applied in dynamic conditions. Thus, the actual noise statistics characteristics of these systems are generally time varying and unknown, which will deteriorate the state estimation accuracy of the Kalman filter (KF) and even cause filter diverging. To address this issue, this paper proposes an adaptive process noise covariance (Qk)-based variational Bayesian adaptive Kalman filter (AQ-VBAKF) algorithm. Firstly, the adaptive factor is introduced to self-tune the process noise covariance; the adaptive factor is obtained based on the innovation sequences, which can adapt to the input measurement values. Then, the VB solution is applied to approximate the time variant and unknown measurement noise covariance. Therefore, this proposed algorithm can adjust the process noise covariance and the measurement noise covariance simultaneously based on the variable input signals, which can improve the self-adaptive ability of the state estimation filter in dynamic conditions. According to the dynamic target tracking test results, the proposed AQ-VBAKF outperforms several other existing filtering methods in estimation accuracy, robustness, and computational efficiency.

A Review of Non-Linear Kalman Filtering for Target Tracking

Target Tracking (TT) with Non-Linear (NL) Kalman Filtering (NLKF) has recently become a hot research hotspot, particularly in the field of Marine Engineering and air traffic control. This paper presents a comprehensive investigation of NLKF algorithms, with emphases on a proposed theoretical framework to significantly improve its implementation results with regards to accuracy and efficiency. Further, the proposed framework demonstrates potential superior performance in terms of robustness, convergence speed, effective computation and tracking accuracy, comparatively with prior state-of-the-art NLKF techniques. It is anticipated that this study will be beneficial to researchers studying Kalman Filtering (KF) algorithms and also serve as the bedrock for future research, especially for those pursuing their career in Electronics and Information Engineering. Some conclusions and possible research directions of NLKF are proposed in the end.

Adaptive Transformed Unscented Simplex Cubature Kalman Filter for Target Tracking

ABSTRACT This paper peruses the Doppler-bearing passive target tracking (DBT), a task that is subject to the nonlinear measurement equations. These measurement equations form the target tracking a nontrivial duty. In order to defeat these problems, many studies have reported various nonlinear estimation techniques, such as sigma-point Kalman filters. These types of filters have demonstrated superior performances for nonlinear system parameter estimation. Consequently, the main contribution of this paper is to improve the performance of the sigma-point Kalman filters. Thereafter, this paper introduces a transformed unscented simplex cubature Kalman filter, in which adaptation techniques are utilized to reduce the dependency of the proposed filter performance on the priori statistical information of the process and measurement noises. Moreover, the proposed filter is initialized by the range-parameterised Unscented Kalman filter. The Cramér–Rao lower bound (CRLB) determines the best accessible accuracy of estimators, which is utilized in this paper to accredit the performance of the proposed filter. The validity and feasibility of the proposed filter are confirmed by Monte Carlo simulations.

Locality-Sensitive Non-Linear Kalman Filter for Target Tracking

Target tracking (TT) with non-linear kalman filtering (NLKF) has recently become a very popular research area, particularly in the field of marine engineering and air traffic control. Contemporary NLKF algorithms have been very effective, in particular, with extensions and merging with a reduced root mean square error (RMSE) value. However, there are a number of issues that confront NLKF approaches, notably weakness in robustness, convergence speed, and tracking accuracy due to large initial error and weak observability. Furthermore, NLKF algorithms significantly results in error for high non-linear systems (NLS) because of the propagation of uncertainty. Again, there is a problem of estimating future states as a result of white noise. To handle these issues, the authors propose a novel non-linear filtering algorithm, called locality-sensitive NLKF (LSNLKF) that incorporates locality-sensitive adaptors into the structure of an integrated NLKF. They are the extended kalman filter (EKF) and the unscented kalman filter (UKF) for TT.

Cascaded Kalman filter for target tracking in automotive radar

In automotive anti-collision radar system, the accuracy of velocity is one of the key indicators to measure radar performance. For frequency modulation continuous wave radar with chirp sequence, which is commonly used in automotive radar, there are two problems that are velocity ambiguity and measurement error. In order to address these problems, a cascaded Kalman filter algorithm is proposed in this article, which is able to overcome velocity ambiguity and improve the accuracy of measurements. In addition, the problem of velocity ambiguity can be solved just in the data processing and it avoids transmitting staggered or multiple pulse repetition frequency signal. The simulation results show that this algorithm can effectively improve the tracking performance.

Adaptive Unscented Kalman Filter for Target Tracking with Unknown Time-Varying Noise Covariance.

The unscented Kalman filter (UKF) is widely used to address the nonlinear problems in target tracking. However, this standard UKF shows unstable performance whenever the noise covariance mismatches. Furthermore, in consideration of the deficiencies of the current adaptive UKF algorithm, this paper proposes a new adaptive UKF scheme for the time-varying noise covariance problems. First of all, the cross-correlation between the innovation and residual sequences is given and proven. On this basis, a linear matrix equation deduced from the innovation and residual sequences is applied to resolve the process noise covariance in real time. Using the redundant measurements, an improved measurement-based adaptive Kalman filtering algorithm is applied to estimate the measurement noise covariance, which is entirely immune to the state estimation. The results of the simulation indicate that under the condition of time-varying noise covariances, the proposed adaptive UKF outperforms the standard UKF and the current adaptive UKF algorithm, hence improving tracking accuracy and stability.

Centralized Fusion Based on Interacting Multiple Model and Adaptive Kalman Filter for Target Tracking in Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UASNs) play an important role in the ocean's protection. They can realize real-time data collection, monitoring, exploration, and many other underwater applications by connecting and coordinating seafloor sensors and underwater vehicles. To achieve these application objectives, such as fishes tracking in biological monitoring field and submarines tracking in military field, target tracking is one of the key techniques. This paper presents a centralized fusion algorithm based on the interacting multiple models and the adaptive Kalman filter (IMMCFAKF) for target tracking in UASNs. Specifically, by introducing an adaptive forgetting factor into the optimal centralized fusion Kalman filter algorithm, the optimal centralized fusion adaptive Kalman filter (CFAKF) algorithm is obtained first. Then, combining the superiorities of both the optimal CFAKF algorithm and the conventional IMM algorithm, the optimal IMMCFAKF is achieved. The numerical simulations are provided to demonstrate the effectiveness of the proposed optimal IMMCFAKF algorithm.

Location ambiguity resolution and tracking method of human targets in wireless infrared sensor network

Human tracking has attracted extensive attention by using low-cost pyroelectric infrared sensor network in recent years. This paper presents a location ambiguity resolution and tracking method for human targets in wireless, distributed and binary infrared sensor network. The tracking system can detect the human targets in the detection space, and activate the sensor detection lines dynamically. A bearing-crossing location method is designed. The intersections of all activated detection lines are called primary measurement points for human location, and some of them are false measurement points. The ambiguity of this bearing-crossing location method is discussed and a two-level bearing-crossing algorithm is proposed based on quartic K-means clustering and joint cost function. For the first level, an anti-logic algorithm is designed to get the initial effective measurement points, then these points are assigned to different targets using K-means clustering. For the second level, the final effective points are obtained by using a special joint cost function, and they are assigned to different targets using K-means clustering once again to get the final locating results. The cost value is used as a weight to adjust the covariance parameter in Kalman filter for target tracking as well. The experimental results show that the average tracking error of human targets is less than 0.8 m in a 10 m × 10 m space, which verify the proposed location ambiguity resolution and tracking method.

Adaptive Unscented Kalman Filter for Target Tracking in the Presence of Nonlinear Systems Involving Model Mismatches

In order to improve filtering precision and restrain divergence caused by sensor faults or model mismatches for target tracking, a new adaptive unscented Kalman filter (N-AUKF) algorithm is proposed. First of all, the unscented Kalman filter (UKF) problem to be solved for systems involving model mismatches is described, after that, the necessary and sufficient condition with third order accuracy of the standard UKF is given and proven by using the matrix theory. In the filtering process of N-AUKF, an adaptive matrix gene is introduced to the standard UKF to adjust the covariance matrixes of the state vector and innovation vector in real time, which makes full use of normal innovations. Then, a covariance matching criterion is designed to judge the filtering divergence. On this basis, an adaptive weighted coefficient is applied to restrain the divergence. Compared with the standard UKF and existing adaptive UKF, the proposed UKF algorithm improves the filtering accuracy, rapidity and numerical stability remarkably, moreover, it has a good adaptive capability to deal with sensor faults or model mismatches. The performance and effectiveness of the proposed UKF is verified in a target tracking mission.

Constrained Extended Kalman Filter for Target Tracking in Directional Sensor Networks

The target tracking problem in directional sensor networks (DSNs) is attracting increasing attention. Unlike the traditional omnidirectional sensor, a directional sensor has a special angle of view. It can offer direction information rather than just the sensing signal measurement with respect to the detected target. The existing tracking approaches in DSNs always separately consider the direction and measurement information; they hardly promise the tracking performance of minimum variance. In this paper, the field of view of directional sensor is approximated to a rectangle; as such the constrained area in which the target is bound to be is constructed. Then, the target tracking problem is formulated as a constrained estimation problem, and a constrained extended Kalman filter (CEKF) tracking algorithm integrating the direction and measurement information is presented; its structural and statistical properties are rigorously derived. It is proved that CEKF is the linear unbiased minimum variance estimator, and CEKF can yield a smaller error covariance than the unconstrained traditional extended Kalman filter using only sensor measurements. Simulation results show that the CEKF has superior tracking performance for directional wireless networks.

A novel ELM based adaptive Kalman filter tracking algorithm

In order to avoid the filter divergence problem in target tracking caused by the unknown or changing statistical characteristic of the noise in Kalman filter, a novel ELM based adaptive Kalman filter tracking algorithm is proposed in this paper. By learning the difference between the theoretical covariance and practical covariance of the innovation which is defined as measurement residue through ELM, the adaptive factor of the covariance matrix of the observation noise was obtained. Then the covariance matrix of the observation noise can be adjusted online according to the ELM learning data. Simulation results showed that the proposed algorithm can improve the estimation accuracy and the robustness of the Kalman filtering for target tracking. It is also applied in the gaze tracking system for pupil tracking and shows satisfactory results.

Sparsity-Promoting Extended Kalman Filtering for Target Tracking in Wireless Sensor Networks

In this letter, we study the problem of target tracking based on energy readings of sensors. We minimize the estimation error by using an extended Kalman filter (EKF). The Kalman gain matrix is obtained as the solution to an optimization problem in which a sparsity-promoting penalty function is added to the objective. The added term penalizes the number of nonzero columns of the Kalman gain matrix, which corresponds to the number of active sensors. By using a sparse Kalman gain matrix only a few sensors send their measurements to the fusion center, thereby saving energy. Simulation results show that an EKF with a sparse Kalman gain matrix can achieve tracking performance that is very close to that of the classical EKF, where all sensors transmit to the fusion center.

Unscented extended Kalman filter for target tracking

A new method of unscented extended Kalman filter (UEKF) for nonlinear system is presented. This new method is a combination of the unscented transformation and the extended Kalman filter (EKF). The extended Kalman filter is similar to that in a conventional EKF. However, in every running step of the EKF the unscented transformation is running, the deterministic sample is caught by unscented transformation, then posterior mean of nonlinearity is caught by propagating, but the posterior covariance of nonlinearity is caught by linearizing. The accuracy of new method is a little better than that of the unscented Kalman filter (UKF), however, the computational time of the UEKF is much less than that of the UKF.

Radar Target Tracking via Robust Linear Filtering

In this letter, we provide a robust version of a linear Kalman filter for target tracking based on a measurement conversion technique on the nonlinear radar measurements. We prove that the state estimation error is bounded in a probabilistic sense. We compare our approach with the current state of the art in converted radar measurement-based linear filtering.

Error Model-converted Measurement and Error Model-modified Extended Kalman Filters for Target Tracking

Two-filter schemes have been evaluated to handle the polar measurements using error model (for bias correction and measurement noise covariance computation)for target-tracking application. It is assumed that a good reference source of target information is available. Schemes based on error model converted measurement Kalman filter (ECMKF) and error model modified extended-Kalman filter (EMEKF) algorithms are presented. Also some comparison with CMKF(debiased) is given. It is inferred that EMEKF gives better performance compared to other filters. Features of EMEKF is carried out wrt to processing order of radar measurement channels.

Effect of common process noise on two-sensor track fusion

In a multisensor environment for surveillance systems, each sensor tracks multiple targets. It is assumed that sensors are equipped with optimal Kalman filters for target tracking. These tracks are correlated because the common process noise resulting from target maneuver enters the estimate of the state vector of the target being tracked. To obtain better quality tracks, the tracks from each sensor are associated using the nearest neighbor criterion for track matching and then kinematic track fusion is performed using the matched tracks. For this purpose, the cross-correlation matrix between tracks is introduced in the test statistic to test the hypothesis that the two tracks originated from the same target. It is shown that the probability distribution of correct track association is increased if the cross-covariance matrix introduced in the test statistic is positive. Necessary and sufficient conditions for the existence, uniqueness, and positivity of the cross-covariance matrix are derived. In addition, an expression for the steady-state cross-covariance matrix is obtained, which is shown to be a function of the parameters of the two filters associated with the candidate tracks being fused. It is shown that for two identical sensors, if the cross-covariance matrix is to be positive definite, certain restrictions on steady-state performance of the individual Kalman filters must be placed. Other measures of performance on the effect of cross correlation on kinematic track fusion are also discussed.

Analysis of suboptimal filtering algorithms for target tracking

The extended Kalman filter has been applied extensively to the tracking of non-maneuvering targets. Although the filter tracks these targets accurately, its computational burden is generally heavy. In this regard, several suboptimal filtering schemes have been proposed in the literature. In this paper, the authors point out some of the shortcomings of the Baheti filter and present modifications to arrive at a computationally more efficient algorithm with improved performance. The proposed filter is compared with several suboptimal Kalman filtering schemes which require less computational burden than the extended Kalman filter while yielding near optimal performance during both transient and steady state filtering. It is shown that these suboptimal filters are viable alternatives to the extended Kalman filter for target tracking. In addition, an off-line technique for decoupling of the filtering algorithms based upon the correlation coefficient analysis is included in this paper.

Efficient Approximation of Kalman Filter for Target Tracking

A Kalman filter in the Cartesian coordinates is described for a maneuvering target when the radar sensor measures range, bearing, and elevation angles in the polar coordinates at high data rates. An approximate gain computation algorithm is developed to determine the filter gains for on-line microprocessor implementation. In this approach, gains are computed for three uncoupled filters and multiplied by a Jacobian transformation determined from the measured target position and orientation. The algorithm is compared with the extended Kalman filter for a typical target trajectory in a naval gun fire control system. The filter gains and the tracking errors for the proposed algorithm are nearly identical to the extended Kalman filter, while the computation requirements are reduced by a factor of four.