Maneuvering target tracking in multi-radar cooperative detection data fusion

In the multisensor passive location, the direction of arrival (DOA) and time differences of arrival (TDOA) are the most useful detection data. Applying multiple sensors to locate and track a maneuvering target is, in fact, a nonlinear uncertain problem. The extended Kalman filter (EKF) is usually used for maneuvering target tracking; however, this algorithm cannot achieve accurate estimation for uncertain or nonlinear systems. In order to increase the accuracy of locating and tracking of a maneuvering target, this paper proposes a novel EKF-cerebellar-model-articulation-controller (EKF-CMAC) multisensor data fusion algorithm for a 3-D maneuvering target. By combining the EKF with an adaptive CMAC, the tracking error of a maneuvering target can be much reduced. The Monte Carlo numerical simulation results illustrate that the proposed algorithm can achieve high accuracy for locating and tracking a maneuvering target.

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An interacting Fuzzy-Fading-Memory-based Augmented Kalman Filtering method for maneuvering target tracking

A tracklet is the estimate of a target state or track that is equivalent to an estimate based only a few measurements. Typically, tracklets are considered to reduce the communications costs between sensors and remote global or fusion trackers. The literature includes several methods for computing tracklets. Some of the methods compute tracklets from measurements, while others compute tracklets from the sensor-level tracks. Some of the methods ignore or omit process noise from the modeling, while others methods attempt to address the presence of process noise. The tracking of maneuvering targets requires the inclusion of process noise. When a tracklet that was developed for nonmaneuvering targets (i.e., no process noise) is used for tracking maneuvering targets, the errors of the tracklet will be somewhat cross-correlated with data from other sensors for the same target, and it is referred to as a quasi-tracklet. Due to some important practical considerations, the impact of maneuvering targets on the performance of tracklets has not been thoroughly addressed in the literature. An investigation that includes the critical practical considerations requires computer simulations with realistic target maneuvers and pertinent evaluation criteria (i.e., computation of errors). In this paper, some of the practical issues concerning the use of tracklets for tracking maneuvering targets are discussed, and the results from a simulation study of the impact of target maneuvers on tracking with tracklets are given. The study considered a fusion tracker receiving tracklets from multiple sensors at dispersed locations and targets maneuvering with either random accelerations or deterministic maneuvers. Tracklets from measurements and tracklets from tracks were studied. Since process noise was added to sensor and fusion trackers to account for target maneuvers, the tracklet methods studied are technically quasi-tracklets. A novel technique is used to compare the performance of tracklets for targets maneuvering randomly with that for targets performing deterministic maneuvers.

This work focuses on filters for nonlinear dynamic systems with nonlinear measurements. The Recursive Nonlinear Least Square Error (RNLS) filter has been recently derived for the state estimation of nonlinear dynamic systems. The RNLS is optimal under the LMSE criterion. Performances of RNLS, EKF and SDDRE-based filters are compared on a common basis. The Pareto formalism is used as a tool for such comparison on a common basis. The comparison is performed for a $6^{\mathrm {t}\mathrm {h}}$ order nonlinear system. This system models a tracking target that performs a coordinated turn/barrel-roll maneuver with unknown turning rate, measured by radar in polar coordinates. It is demonstrated by simulations that the RNLS filter is the optimal filter with respect to the quadratic criterion it is designed for. This places the RNLS filter as a vital candidate estimator of nonlinear systems.

This is a part of Part VI (nonlinear filtering) of a series of papers that provide a comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I [52] and Part II [48] deal with target motion models. Part III [49], Part IV [50], and Part V [51] cover measurement models, maneuver detection based techniques, and multiple-model methods, respectively. This part surveys approximation techniques for point estimation of nonlinear dynamic systems that are general, applicable to a wide spectrum of nonlinear filtering problems, especially those in the context of maneuvering target tracking. Three classes of such techniques are survey here: function approximation, moment approximation, and stochastic model approximation.

The accurate estimation of battery State of Charge (SOC) is a key technology in the research of electric vehicle battery management systems. In order to solve the problem of inaccurate noise estimation in nonlinear systems, an improved Cauchy robust correction-Sage Husa extended Kalman filtering (CRC-SHEKF) algorithm is proposed for high-precision SOC estimation of lithium-ion batteries in new energy vehicles. Considering the polarization effect of the battery, the FFRLS algorithm is used for online parameter identification of the Dual Polarization model. Using robust data correction methods, the Cauchy robust function is simplified for real-time correction of the covariance matrix Q of system state noise and the covariance matrix R of the observed noise in the filtering process and combined with SHEKF for SOC estimation. The experimental results show that under different temperature conditions and complex working conditions, the proposed CRC-SHEKF algorithm has the minimum mean absolute error (MAE), root mean square error (RMSE), and maximum error (MAX). Under the condition of the Beijing bus dynamic stress test (BBDST) at 15 °C, the MAE, RMSE, and MAX of the CRC-SHEKF algorithm are 0.392 %, 0.716 %, and 0.945 %, with the computing time of only 4.839 s. The algorithm proposed in this article has high accuracy and robustness, and has practical application value, providing a reference for the application of lithium battery condition monitoring.

ABSTRACTIn cognitive radio (CR), the single‐threshold cooperative detection is sensitive to the noise power. In this paper, a double‐threshold cooperative detection scheme based on weighed combination is proposed to improve the global detected performance of the cooperative detection. The double thresholds that are decided by the restriction probability are adopted to divide the energy statistics observed by CRs into three subareas, and the detected performance of the CRs locating in the confusion area is fortified through the weighed data fusion by the coordinator. The CRs in the other two areas can make local binary decisions because of their exact sensing performance and then forward their 1‐bit decisions to the coordinator that will combine all the received decisions to give the final decision on the presence of the primary user. In this paper, both the periodic single‐band and wideband cooperative detections are investigated, and the probabilities of spectrum utilization by these two patterns are respectively analyzed. The simulation shows that the detection probability of the proposed scheme is better than that of the single‐threshold cooperative detection with decision fusion but slightly worse than that of the single‐threshold cooperative detection with data fusion. However, the average number of the bits sent to the coordinator by the proposed algorithm is less than that by the data fusion, and it may increase by the decreasing of the restriction probability. The simulation also shows that the periodic wideband sensing can obtain higher spectrum utilization than the periodic single‐band sensing. Copyright © 2012 John Wiley & Sons, Ltd.

Concerning low accuracy even divergence of maneuvering target tracking due to inaccurate tracking model and statistical property, an adaptive Square Root Cubature Kalman Filter (SCKF) is proposed based on the standard SCKF and modified Sage-Husa estimator. The proposed algorithm can estimate the statistical parameters of unknown system noises online, and restrain the tracking error caused by unknown system noises effectively; hence it is applied to maneuvering target tracking. The simulation is preformed latterly and experimental results show that comparing with the standard SCKF algorithm, the adaptive SCKF can achieve better accuracy and stability for maneuvering target tracking while the system noises is unknown and time variation.

A systematic frequency domain method for nonlinear analysis, design and estimation of nonlinear systems is established based on the discussions in the previous chapters. Firstly, this method allows accurate determination of the linear and nonlinear components in system output spectrum of a given nonlinear system described by NDE, NARX or NBO (nonlinear block-oriented) models, with some simulation or experiment data. These output spectrum components can then be used for system identification or nonlinear analysis for different purposes such as fault detection etc. Secondly, the OFRF discussed before is expressed into a much improved polynomial function, referred to here as nonlinear characteristic output spectrum (nCOS) function, which is an explicit expression for the relationship between nonlinear output spectrum and system characteristic parameters of interest including nonlinear parameters, frequency variable, and input excitation magnitude (not just nonlinear parameters as that in Chaps. 7 and 8 ) with a more generic parametric structure. With the accurate determination of system output spectrum components in the previous step, the nCOS function can therefore be accurately determined up to any high orders, with less simulation trials and computation cost compared with a pure simulation based study or traditional theoretical computation. These results can provide a useful approach for qualitative and quantitative analysis and design of nonlinear dynamics in the frequency domain.

This paper presents a Hybrid Particle Filter based RNN method for state estimation of non-linear dynamical system with knowledge of its input and output measurements. Particle filters are sequential Monte Carlo methods based on point mass (or particle) representations of probability densities, which is used to train Recurrent Neural Networks for estimation problems. The performance this method is compared with EKF based estimation and RNN based estimation. An Induction motor is considered as typical non-linear system and is implemented in MATLAB environment.

Detect and tracking of maneuvering target is a complicated dynamic state estimation problem whose difficulty is increased in case of high noise environments or low signal-to-noise ratio (SNR). In this case, the track-before-detect filter (TBDF) that uses unthresholded measurements considers as an effective method for detecting and tracking a single target under low SNR conditions. Nevertheless, the performance of the algorithm will be affected with severe loss because of the mismatching of target model during maneuver. In this paper, to resolve the target maneuvers, we propose an application of particle filtering which depends on track before detect (PF - TBD) algorithm in order to track the maneuvering target. We employ the Constant Acceleration (CA) model and Coordinate Turn model (CT). Our simulation results show that the detection and tracking of maneuvering target performance of TBD-PF has been improved using the proposed algorithm.

To solve the problem of missing measurements in highly manoeuvring target tracking, an expected‐mode‐augmentation‐based unscented Kalman filter with missing measurements compensation (EMA‐MMCUKF) is designed based on the variable‐structure multiple model method. In the proposed EMA‐MMCUKF, the random missing measurements are described by the Bernoulli distribution, and the one‐step prediction is used as the compensation. Based on the proposed EMA‐MMCUKF, a Bayesian estimation‐based expected‐mode‐augmentation unscented Kalman filter with missing measurements compensation (BE‐EMA‐MMCUKF) is proposed for adaptive estimation of the sensor measurement reception rate, where the unknown measurement reception rate can be estimated by fully utilising prior information. Simulation results demonstrate that the proposed EMA‐MMCUKF can effectively track the manoeuvring target at different measurement reception rates. Moreover, when the sensor prior information differs significantly from the true measurement reception rate, the proposed BE‐EMA‐MMCUKF can effectively estimate the unknown sensor measurement reception rate and improve the accuracy of manoeuvring target tracking compared with non‐estimation of the sensor measurement reception rate.

It's important to design an appropriate tracking gate when tracking maneuvering target in clutter environment. Usually, the size of traditional tracking gate is some certain fixed value, which can't meet the requirements under unknown tracking status. For this problem, we present an adaptive tracking gate design mentod based on Markov chain in this paper. It is clear that the tracking gate is directly related to the residual error. Therefore, we propose a novel method to forecast the residual error and design tracking gates adaptively based on the residual prediction accordingly. Due to the improved prediction accuracy of resuidual error, the degined tracking gate shows better performance when tracking maneuvering targets. Simulation results demonstrate the proposed method.

When tracking maneuvering targets with a nearly constant acceleration (NCA) Kalman filter with discrete white noise acceleration (DWNA), the selection of the process noise variance is complicated by the fact that the process noise errors are modeled as white Gaussian, while target maneuvers are deterministic or highly correlated in time. In recent years for nearly constant velocity (NCV) Kalman filters, the deterministic tracking index was introduced and used to develop a relationship between the maximum acceleration of the target and the process noise variance that minimizes the maximum mean squared error (MMSE) in position. A lower bound on the process noise variance was also expressed in terms of the maximum acceleration and deterministic tracking index. In this paper, the design methods for NCV Kalman filters with DWNA are extended to develop design methods for NCA Kalman filters with DWNA tracking maneuvering targets. The effectiveness of the design methods are illustrated via Monte Carlo simulations.