One-dimensional Kalman Filter Research Articles

Experimental study of passive detection of underwater targets across ice

This study deployed receivers and sources of sound on the Songhua River and the Arctic Ice Station—areas covered with ice—in field experiments to examine and verify the feasibility of the passive detection of narrowband signals of underwater targets across ice. The classic method for the detection of narrowband signals based on power spectrum estimation was used. However, the spectrum was noisy, making it difficult to establish a detection threshold, which in turn, affected the results of detection. To solve this problem, an α-comparative detector based on the one-dimensional Kalman filter was developed to smoothen the frequency domain and improve detection capability. The results of experimental data processing show that under special impulse noise interference in areas covered with ice, the proposed method of signal processing reduced signal interference in the frequency domain to enable a clearer observation of narrowband signals. The feasibility of using the proposed method for the passive detection of narrowband signals of underwater targets across ice was also verified.

Опыт использования фильтра Калмана для коррекции численного прогноза приземной температуры воздуха

A possibility of using the one-dimensional Kalman filter to improve the forecast of surface air temperature at an irregular grid of point is studied. This mechanism is tested using the forecasts obtained from different configurations of two different numerical weather prediction models. An algorithm for the statistical correction of numerical forecasts of surface air temperature based on the one-dimensional Kalman filter is constructed. Two methods are proposed for estimating the bias noise dispersion. The series of experiments demonstrated the effectiveness of the algorithm for the bias compensation.The most significantresults are achieved for the models with large bias or for long-range forecasts. At the same time, the use of the algorithm has little effect on the root-meansquare error of the forecast. Keywords: hydrodynamic model of the atmosphere, numerical weather prediction, statistical correction of numerical forecasts, Kalman filter

IP Core Based on the Kalman Filter Algorithm in the FPGA Implementation

This article discusses one-dimensional Kalman filter algorithm using FPGA hardware IP core implementation process. First of all, to program the FPGA matrix operations, implementation of double precision floating point. Then the Kalman filter algorithm programmed in MATLAB, to verify the correctness of the algorithm thinking. Finally the MATLAB language programming algorithm is converted into VHDL language. And call 64 a double precision floating point data algorithm realizes the design of 1-D Kalman filtration algorithm IP core, which make the Kalman filter meet the high precision as well as high speed to complete complex algorithm.

An Efficient Distance Estimation Algorithm for Indoor Sensor Network

Localization in sensor networks deals with the estimation of the position of the sensor node in a network for a given incomplete and inaccurate pair-wise distance measurements. Such distance data may be acquired by a sensor node by communicating with neighboring nodes called anchor nodes whose positions are known apriori. This paper proposes a Kalman filtering based distance estimation algorithm for indoor wireless sensor networks. In this paper, the distance of the unknown node is computed based on the Received Signal Strength (RSS) measurements. The effect of path loss and attenuation in the wireless medium are also considered in this proposed algorithm. The distance error is minimized using one-dimensional Kalman filter. The number of iterations in Kalman filter is limited using Cramer Rao Bound (CRB) value. A real-time experimentation is carried out to get Received Signal Strength value in indoor environment using zigbee series 1 RF module along with the associated X-CTU software of Maxstream. The proposed algorithm is simulated in MATLAB version 7. From the simulation results it is found that the proposed distance estimation algorithm gives accurate results.

A one‐dimensional Kalman filter for the correction of near surface temperature forecasts

AbstractA one‐dimensional Kalman filter is proposed for the correction of maximum and minimum near surface (2 m) temperature forecasts obtained by a Numerical Weather Prediction model. In our study we used only one parameter (observed temperature), employing in this way scalar system and observation equations, and a limited time interval (7 days). As a result, our algorithm can be easily run on any PC with only minor technical support. The corresponding results are rather impressive, since the systematic error of our time series almost disappears, and they show the merit of post‐processing even when only a simple method is applied. Copyright © 2002 Royal Meteorological Society.

Two-Dimensional Recursive Filtering of Noisy Images with Edge Preserving Properties

The filtering problem for noisy non homogeneous images is considered. An edge preserving, two-dimensional, recursive filtering algorithm, closely paralleling the one-dimensional Kalman filter and involving a very modest computational burden is derived. The filter is obtained as a combination of a one-dimensional Kalman predictor and of a one-dimensional fixed-interval smoother. Experimental results show the effectiveness of the new filtering scheme.

Recovery of the one and two dimensional windfields from chemical constituent observations using the constituent transport equation and an extended Kalman filter

The recovery of windfields from chemical constituents is a difficult, but potentially rewarding inverse problem. Advanced data assimilation methods, such as the extended Kalman filter, are theoretically capable of performing this task; but, at this time, little work has been undertaken. Daley (1994) constructed a one-dimensional extended Kalman filter (EKF) system to examine this problem. This work is extended here to include a more systematic examination of the one-dimensional case, together with experiments with non-divergent two-dimensional flow. The emphasis is on determining when wind recovery is likely to be successful or unsuccessful.

Spectral Characteristics of Kalman Filter Systems for Atmospheric Data Assimilation

Abstract In recent years, there has been increasing interest in the application of Kalman filter systems to atmospheric data assimilation. One important aspect of any data assimilation system is its filtering properties. This is examined by spectral decomposition of a simple one-dimensional Kalman filter system. It is shown that the second-moment error statistics of constant-coefficient linear systems observed everywhere on a regular grid are reduced to scalar systems by Fourier transforms. Under these conditions, the complete space and time behavior of the forecast and analysis error covariances can be explicitly determined from the model and observation error covariances and the initial forecast error covariance. The resulting solutions can then be examined by elementary dynamic systems analysis. The multivariate, inviscid, dissipative, unstable mode and nonstochastic cases are analyzed. The stationary solutions and the rate of convergence toward them are found and certain unstable, periodic, and quasi-...

Solution of the continuous Kalman filter equations using the transmission line method

A novel method is presented for the solution of the one-dimensional continuous Kalman filter equations implemented on a digital computer. The Kalman filter is implemented in an unusual way as a transmission line circuit. Estimated variables are regarded as discrete pulses bouncing to and from the nodes in the transmission line network at each time step. This approach is tested on a problem of voltage estimation in a passive electrical network in the presence of voltmeter errors. The transmission line method is compared with the results that are obtained using the Gear first- and third-order methods, the fourth-order Runge-Kutta method and the application of the discrete Kalman filter. It is found that the transmission line method has a tendency to yield less biased estimates than the other algorithms as well as providing smoother estimates for sampling times approaching the time constant of the circuit.

Shannon Information Retrieved from Overlapped Chromatographic Peaks with Kaiman Filter

A function (FUMI) which describes the Shannon mutual information in chromatography is derived. FUMI covers two subjects in one formula: (i) the degree of peak overlap and the noise level; (ii) the mathematical formalism of peak deconvolution and quantitation based on the one-dimensional Kalman filter for peak resolution. Computer simulation demonstrates that a sufficient amount of mutual information can be retrieved from overlapped peaks (Gaussian) through (i) observation and (ii) data processing. A practical advantage of FUMI is logical evaluation and optimization of chromatographic experiments without resort to experience: the optimal can be defined as the chromatogram which can transmit the maximal amount of mutual information in a unit time.

Comments on "Tracking a maneuvering target using input estimation

A best linear unbiased estimate (BLUE) of the input process which characterizes a pilot-induced maneuver is developed for the case of the one-dimensional Kalman filter. This approach is utilized to yield a recursive implementation of the adaptive Kalman filter that is identical to that obtained by P.L. Bogler in the above mentioned paper (see ibid., vol.AES-23, no.3, p.298-310, May 1987). >

A one-dimensional kalman filter for peak resolution

A one-dimensional Kalman filter algorithm is presented that resolves several overlapped liquid chromatographic peaks without algebraic operations of matrices. The resolving powers or filtering reliability of the algorithm is independent of the number of peaks, but depends on both the peak overlap (resolution value R s) and the signal-to-noise ratio more significantly than the usual multi-dimensional Kalman filter. The reliability is shown to be similar to that of the multi-dimensional filter for the resolution of overlapped Gaussian peaks with limited R s and S/N values.

One-dimensional Kalman filter in a robot system for total analysis of solid dosage formulations

A one-dimensional Kalman filter for peak resolution is applied to a totally automated robot system for liquid chromatographic analysis of solid dosage formulations. Sample solutions were prepared from four tablets of clemastine fumarate (1 mg/tablet) and injected automatically at regular, short intervals onto the column by the robot system. The overlapped peaks in the resulting complicated chromatogram were resolved and evaluated quantitatively by the one-dimensional filter. The peak-resolving powers are shown to be reliable by comparison with a multidimensional Kalman filter. The applicability of the whole analytical system with the linear Kalman filter is discussed.

Tracking a Maneuvering Target Using Input Estimation

The conventional Kalman tracking filter incurs mean tracking errors in the presence of a pilot-induced target maneuver. Chan,Hu, and Plant proposed a solution to this problem which used themean deviations of the residual innovation sequence to make corrections to the Kalman filter. This algorithm is further developedhere for the case of a one-dimensional Kalman filter, for which an Implementable closed-form recursive relation exists. Simulation results show that the Chan, Hu, and Plant method can accurately detect and correct an acceleration discontinuity under a variety of maneuver models and radar parameters. Also, the inclusion of thislogic into a multiple hypothesis tracking system is briefly outlined.

Recursive filtering algorithm for a two-dimensional system

An approximate two-dimensional recursive filtering algorithm that parallels the one-dimensional Kalman filter is presented for a causal system considered by Habibi [1]. A numerical result is also shown.

An Edge-Preserving Recursive Noise-Smoothing Algorithm for Image Data

Recursive Kalman filters are often used for noise reduction in image data. These linear filters are based on the second-order statistics of image and noise. The noise is effectively reduced by the filtering operation, but the edges in the image are blurred and image contrast is reduced as well. These effects decrease the subjective quality of the image. A simple and computationally fast scan-ordered one-dimensional Kalman filter is derived, which is then provided with additional structural information about the edges in the noisy image. This filter behaves like the original noise-smoothing Kalman filter if no edges are present but has a greatly improved step response. In this way the edge-blurring phenomenon is effectively reduced. Results of several experiments are presented to demonstrate the feasibility of our approach.

Recursive least squares smoothing of noise in images

In the recent past considerable attention has been devoted to the application of Kalman filtering to smoothing out observation noise in image data. A generalization of the one-dimensional Kalman filter to two dimensions was earlier suggested by Habibi, but it has since been shown that this generalization is invalid since it does not preserve the optimality of the Kalman filter. A new method is proposed here that enables well-established Kalman-filter theory to yield a simple two-dimensional filter for images that can be modeled by two-dimensional wide-sense Markov (WSM) random fields.