Adaptive EKF-CMAC-Based Multisensor Data Fusion for Maneuvering Target

To estimate system errors of direction of arrival (DOA) and time difference of arrival (TDOA) with two stations locating system, a system error estimation model is build through analyzing the locating principle. In succession sequential least squares estimation (SLSE) is studied. And then he estimation process is given. At last convergence and accuracy of SLSE are researched through Monte Carlo simulation. And target tracks which are before and after the bias registration are compared. Theoretical analyses and simulation shows that the SLSE can exactly and effectively estimate system errors of DOA and TDOA locating system.

Multi-radar cooperative detection has significant advantages in maneuvering target tracking because of its wide coverage, good detection performance and strong survivability. As a key technology in cooperative detection of networking, multisource data fusion requires high precision and stability of fusion tracking. In this paper, Centralized Kalman Filter method is used to build a mathematical model by expanding dimension and direction finding quantity, and a new method is proposed to solve the fusion tracking problem of multi-radar in cooperative detection of maneuvering target by extending the estimation of nonlinear system for single sensor to the data fusion estimation of multi-sensor nonlinear system.

A new collaborative detection method of TDOA(Time Difference of Arrival) and DOA(Direction of arrival) has been proposed for weak signals. The fuzzy evidence theory is used to fuse the peaks of TDOA correlation and the spatial spectrum of MUSIC(Multiple Signal Classification).In the paper, the TODA/DOA collaborative detector is designed, the basic probability assignment fuction(mass fuction or BPA fuction) and the membership fuction are used to express the uncertainty of the TDOA and DOA information. Then the information is fused by the D-S combination formula and the rule of game probability distribution is used to get the detection result. At last, the simulation results show that the performance of the detector is better than the traditional TDOA correlation detector.

Direction of arrival (DOA) estimation from time differences of arrival (TDOA) is used in several array systems. In some cases, practical constraints may necessitate the use of heavily quantized TDOA estimates. The quantization in TDOAs also causes the directional estimate to be quantized. This paper proposes a lattice-based representation for the quantization scenario. This representation can be used to evaluate the performance of a given array when TDOAs are quantized and it also provides guidelines for optimum array design. In addition, the lattice structure can be used for evaluating the reliability of DOA estimates when other errors are present

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© 2014 IEEE. This paper investigates the performance of wireless capsule endoscopy (WCE) tracking within the irregular environment of digestion system by integrating directional-of-arrival (DOA), time-of-arrival (TOA) [or time-difference-of-arrival (TDOA)], and inertial measurement unit (IMU) measurements using the extended Kalman filter (EKF). Here, an emitter transmits a signal to the WCE, and the WCE replies the signal back to a set of antenna arrays. Then, the round trip TOA, and the DOA of the signals transmitted from WCE to the antenna arrays are measured. The digestion system path model is considered unknown, and the movement model between two consecutive signals is assumed linear. Simulations are conducted to investigate the EKF performance as a function of the number of antenna arrays, antenna array configuration, and TOA (and TDOA) estimation accuracy. In addition, the impact of IMU on the localization performance will be studied. Simulations confirm the proposed WCE tracking performance. In addition, the computational complexity of the EKF with respect to the number of antenna array is studied.

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.

Three different algorithms based on the multimodel partitioning approach are designed for the problem of tracking a manoeuvering target utilizing aircraft derived data (ADD) measurements of heading. These are subsequently evaluated with respect to their performance in the sense of the mean square estimation error by means of simulations in three realistic tracking scenarios. The extend Kalman filter (EKF) for this problem has been used as a measure of comparison for the performance of all algorithms. The results indicate the improvement in performance obtained when using adaptive techniques to track manoeuvering targets instead of the non-adaptive EKF.

Particle filters can provide better results than Kalman filter based algorithms in the problems where the nonlinear elements are dominant. In this study the problem of tracking an airborne platform using radar measurements, which has nonlinearities both in measurement and system models is considered. Different models are used for different target maneuvers; extended Kalman filter (EKF), interacting multiple model (IMM) structure and multiple model particle filter are compared on a sample scenario.

Object-Oriented Environment for Assessing Tracking Algorithms

In a multipath communication scenario, it is often relevant to estimate the directions and relative delays of each multipath signal. We present an effect algorithm for the simultaneous estimation of these parameters by re-iterative super-resolution, beamforming, and MUSIC-like searching techniques. The algorithm first separates and estimates direction of arrival (DOA) of the multipath signals. Linear constrained minimum power beamforming is used to obtain the transmitted time function of the desired signal in certain incident angle. Then, time difference of arrival (TDOA) for the incident signals are mapped into phase shifts in the frequency domain. Using the DFT of the desired signal and the received data, we can separate the phase shifts in the frequency domain due to time delay by MUSIC-like searching. At the same time, the pairing of the estimated DOA and TDOA is automatically determined. Computer simulations illustrating the performance of the proposed algorithm with the Cramer–Rao bound are included.

The estimation of Direction of Arrival (DoA) of guided ultrasonic waves is an important task in many Structural Health Monitoring (SHM) applications. The aim is to locate sources of elastic waves which can be generated by impacts or defects in the inspected structures. In this paper, the array geometry and the shape of the piezo-sensors are designed to optimize the DoA estimation on a pre-defined angular sector, from acquisitions affected by noise and interference. In the proposed approach, the DoA of a wave generated by a single source is considered as a random variable that is uniformly distributed in a given range. The wave velocity is assumed to be unknown and the DoA estimation is performed by measuring the Differences in Time of Arrival (DToAs) of wavefronts impinging on the sensors. The optimization procedure of sensors positioning is based on the computation of the DoA and wave velocity parameters Cramér-Rao Matrix Bound (CRMB) with a Bayesian approach. An efficient DoA estimator is found based on the DToAs Gauss-Markov estimator for a three sensors array. Moreover, a novel directive sensor for guided waves is introduced to cancel out undesired Acoustic Sources impinging from DoAs out of the given angles range. Numerical results show the capability to filter directional interference of the novel sensor and a considerably improved DoA estimation performance provided by the optimized sensor cluster in the pre-defined angular sector, as compared to conventional approaches.

The extended Kalman filter (EKF), one of the most popular state estimators for nonlinear uncertain dynamic systems, is considered here in its continuous-discrete (CD) form. The CD version enhances the EKF's accuracy but requires robust and computationally efficient numerical integration methods. The purpose of this article is to present several new efficient integration methods suited to the implementation of the CD-EKF and to compare them to classical implementations such as the Dormand–Prince 4/5 and Euler methods. They are first applied on two proposed nonlinear parameter estimation testbenches and to the stiff Curtiss and Hirschfelder model for state and parameter estimation. Then, they are applied to the estimation of the iron losses of a high-speed permanent-magnet synchronous motor. The results show that for highly nonlinear dynamic systems or for a low to medium sampling frequency, the implicit implementation of the EKF provides a better accuracy and convergence safety. For less nonlinear functions and a higher sampling frequency, classical explicit methods obviously give similar results in a lower computation time.

We study the nonlinear filtering of a discrete-time hybrid stochastic system with small observation noise; here, the system dynamics (target tracking) is coupled with a finite state unknown parameter process (target maneuvers). To estimate the state of the unknown process, a fixed sample size and a sequential type statistical tests in terms of quadratic variation are used. Such an estimate is in turn used to construct nonlinear filters that are shown to be asymptotically optimal as the observation noise goes to zero. We also discuss the related numerical issues and present the corresponding numerical experiments. In particular we show that our QVT algorithm performs better than the popular IMM algorithm in the standard case where the switchings (maneuvers) are not too frequent. We show that if the switching is completely observable, then the extended Kalman filter becomes a special case of our filter; we similarly recover the Picard filter also

This paper presents a 3-D search grid allocation algorithm for source localization and beam steering. Data-driven beam-steering systems can benefit from algorithm designs that restrict the processing to a finite set of predefined direction of arrival (DoA) grid points. However, this scenario requires careful definition of the distribution of the search grid points to achieve high spatial filtering performance. The non-linear relationship that exists between time-delay of arrival (TDoA) and degree-oriented DoA means that a generic uniform grid distribution approach is not adequate for accurate localization of the source direction in the time domain. The aim of the proposed search grid allocation algorithm is to maximize the efficiency of localization by exploiting the sensitivity variation in temporal and spatial aspects. Experimental results show that the proposed search grid allocation outperforms the uniform grid allocation in terms of TDoA accuracy and steering vector estimations.