#
INS/Partial DVL Measurements Fusion with Correlated Process and Measurement Noise^{ †}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

## 2. Extended Kalman Filter with Correlated Process and Measurement Noise

_{z}is the measurement residual vector, is the a posteriori estimate of the error state vector, is the covariance matrix of the prior estimation error, K

_{k}is the Kalman gain, H

_{k}is the measurement matrix and Φ

_{k}is the state transition matrix.

_{k}[10],

_{k}affects the state at time k + 1 just as v

_{k}

_{−1}affects the measurement at time k + 1 (see (1) for the definition of the nonlinear system).

## 3. Extended Loosely Coupled Approach

## 4. Analysis and Results

## 5. Conclusions

## References

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**Figure 1.**The extended loosely coupled approach with virtual beam implementation and with the process-measurement noise cross-covariance matrix.

**Figure 2.**The velocity RMS error with (M = 1) and without (M = 0) the cross-covariance matrix of the correlated process and measurement noises.

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**MDPI and ACS Style**

Eliav, R.; Klein, I.
INS/Partial DVL Measurements Fusion with Correlated Process and Measurement Noise. *Proceedings* **2019**, *4*, 34.
https://doi.org/10.3390/ecsa-5-05727

**AMA Style**

Eliav R, Klein I.
INS/Partial DVL Measurements Fusion with Correlated Process and Measurement Noise. *Proceedings*. 2019; 4(1):34.
https://doi.org/10.3390/ecsa-5-05727

**Chicago/Turabian Style**

Eliav, Rei, and Itzik Klein.
2019. "INS/Partial DVL Measurements Fusion with Correlated Process and Measurement Noise" *Proceedings* 4, no. 1: 34.
https://doi.org/10.3390/ecsa-5-05727