Pseudo-linear Filter Research Articles

Adaptive event-triggered pseudolinear consensus filter for multi-UAVs bearings-only target tracking

An improved version of the pseudolinear filter is proposed, namely an adaptive event-triggered instrumental variable-based pseudolinear information consensus filter, with a special focus on three-dimensional(3D) unbiased target tracking with multiple unmanned aerial vehicles (UAVs) bearings-only measurements under communication faults. To deal with the unknown communication faults, a credibility-based weighted average consensus rule is constructed, which considers the network topology and the information quality of UAVs. Furthermore, an adaptive event-triggered mechanism is designed to reduce the computation burden and communication bandwidth. The selective-angle-measurement strategy (SAMS)-based instrumental variable is integrated into the pseudolinear information consensus filter to achieve unbiased estimates. It is proved that the estimates of all UAVs will eventually achieve consistency, and its estimation errors are bounded. Finally, numerical simulations are given to verify the advantages of the proposed filtering algorithm in terms of enhanced accuracy and faster convergence rates.

A New Pseudolinear Filter for Bearings-Only Tracking without Requirement of Bias Compensation.

In bearings-only tracking systems, the pseudolinear Kalman filter (PLKF) has advantages in stability and computational complexity, but suffers from correlation problems. Existing solutions require bias compensation to reduce the correlation between the pseudomeasurement matrix and pseudolinear noise, but incomplete compensation may cause a loss of estimation accuracy. In this paper, a new pseudolinear filter is proposed under the minimum mean square error (MMSE) framework without requirement of bias compensation. The pseudolinear state-space model of bearings-only tracking is first developed. The correlation between the pseudomeasurement matrix and pseudolinear noise is thoroughly analyzed. By splitting the bearing noise term from the pseudomeasurement matrix and performing some algebraic manipulations, their cross-covariance can be calculated and incorporated into the filtering process to account for their effects on estimation. The target state estimation and its associated covariance can then be updated according to the MMSE update equation. The new pseudolinear filter has a stable performance and low computational complexity and handles the correlation problem implicitly under a unified MMSE framework, thus avoiding the severe bias problem of the PLKF. The posterior Cramer–Rao Lower Bound (PCRLB) for target state estimation is presented. Simulations are conducted to demonstrate the effectiveness of the proposed method.

High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis

In this paper, a novel magnetic field-based sensing system employing statistically optimized concurrent multiple sensor outputs for precise field-position association and localization is presented. This method capitalizes on the independence between simultaneous spatial field measurements at multiple locations to induce unique correspondences between field and position. This single-source-multi-sensor configuration is able to achieve accurate and precise localization and tracking of translational motion without contact over large travel distances for feedback control. Principal component analysis (PCA) is used as a pseudo-linear filter to optimally reduce the dimensions of the multi-sensor output space for computationally efficient field-position mapping with artificial neural networks (ANNs). Numerical simulations are employed to investigate the effects of geometric parameters and Gaussian noise corruption on PCA assisted ANN mapping performance. Using a 9-sensor network, the sensing accuracy and closed-loop tracking performance of the proposed optimal field-based sensing system is experimentally evaluated on a linear actuator with a significantly more expensive optical encoder as a comparison.

Effect of biased estimation on radar-to-ESM track association

The track association problem of radar and electronic support measure (ESM) has been considered in the literature for several years. This problem is crucial for radar-to-ESM track fusion and is complicated by the presence of individual systematic errors and measurement errors. In order to improve the track association of radar and ESM sensors, a pseudo-linear filtering algorithm is proposed to estimate the target states and improve the stability of the filter. It is found that, however, the correct probability of radar-to-ESM track association decreases as the radar measurement error decreases, when the pseudo-linear filter is used for ESM sensor filtering. In view of the strange phenomenon, this paper analyzes the reason for it by using the statistic theory and further performs Monte Carlo simulation to verify the analysis.

Pseudo-linear estimator for bearings-only passive target tracking

The pseudo-linear extended Kalman tracking filter previously developed for passive target tracking using bearings-only measurements required the use of a null target state vector as an initial estimate to obtain convergence for all types of scenario. The pseudo-linear estimator is projected in such a way that it does not require any initial estimate at all and at the same time offers all the features of the extended Kalman filter based pseudo-linear filter; namely sequential processing, flexibility to adopt the variance of each measurement, etc. The algorithm is tested in Monte Carlo simulations and its results are presented for two typical scenarios at various noise levels.

A closed-form solution to bearings-only target motion analysis

Bearings-only target motion analysis is a nonlinear state estimation problem in which the noise corrupted angle of arrival measurements of an emitted signal are used to obtain estimates of the source's range, bearing, course, and speed. The estimation process is complicated by unusual observability properties that render the quality of the estimate highly dependent on both the measurement noise levels and the source-observer geometry. Solutions that use recursive Kalman filtering approach or batch-style algorithms have been reported. The nonlinear batch style estimators for this process require iterative solution methods and under certain scenarios can be sensitive to initial conditions. Pseudolinear solutions that alleviate some of the difficulties with the iterative batch algorithms have been proposed. Although early versions of the pseudolinear filter suffered from biased estimates, subsequent improvements appear to have reduced the bias problem. This paper discusses a new pseudolinear solution based on the observable parameters from individual data segments defined by periods of constant observer velocity (termed legs). This solution is a true closed-form solution to the bearings-only target motion analysis problem. Although theoretically interesting, the technique does suffer under conditions of poor observability. A practical pseudolinear estimate, that does not suffer from the same observability problems, is developed and related to the first solution. Algorithm performance results, obtained from computer simulation, are presented. For the scenarios examined, the technique provides good state estimates under conditions of high observability. As observability conditions deteriorate, the solution does develop biases. However, it may still be useful for initializing an iterative nonlinear batch-style estimation algorithm.

Biased Estimation Properties of the Pseudolinear Tracking Filter

Estimation bias in the pseudolinear filter applied to bearings-only target tracking is discussed. Approximate expressions for the pertinent error terms are developed and subsequently used to predict tracking performance under realistic operating conditions. It is shown that once own-ship executes a maneuver, only the estimated range vector remains biased; the corresponding velocity vector becomes asymptotically unbiased. Further investigation reveals that this range bias is highly dependent upon geometry and can be altered by additional own-ship maneuvers. Experimental data are presented to support these findings.