scholarly journals MCC-CKF: A Distance Constrained Kalman Filter Method for Indoor TOA Localization Applications

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 478 ◽  
Author(s):  
Cheng Xu ◽  
Mengmeng Ji ◽  
Yue Qi ◽  
Xinghang Zhou
Keyword(s):  
Kalman Filter ◽  
Negative Impact ◽  
Extreme Environment ◽  
State Constraint ◽  
Line Of Sight ◽  
Filter Method ◽  
Inequality State ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian ◽  
Non Line Of Sight

Non-Gaussian noise may have a negative impact on the performance of the Kalman filter (KF), due to its adoption of only second-order statistical information. Thus, KF is not first priority in applications with non-Gaussian noises. The indoor positioning based on arrival of time (TOA) has large errors caused by multipath and non-line of sight (NLOS). This paper introduces the inequality state constraint to enhance the ranging performance. Based on these considerations, we propose a constrained Kalman filter based on the maximum correntropy criterion (MCC-CKF) to enhance the TOA performance in the extreme environment of multipath and non-line of sight. Pratical experimental results indicate that MCC-CKF outperforms other estimators, such as Kalman filter and Kalman filter based on maximum entropy.

Maximum Correntropy Criterion Constrained Kalman Filter

2017 ◽  
Author(s):  
Seyed Fakoorian ◽  
Mahmoud Moosavi ◽  
Reza Izanloo ◽  
Vahid Azimi ◽  
Dan Simon
Keyword(s):  
Kalman Filter ◽  
Gaussian Noise ◽  
State Constraints ◽  
Statistical Information ◽  
Second Order ◽  
System Model ◽  
Error Covariance ◽  
Inequality State ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian

Non-Gaussian noise may degrade the performance of the Kalman filter because the Kalman filter uses only second-order statistical information, so it is not optimal in non-Gaussian noise environments. Also, many systems include equality or inequality state constraints that are not directly included in the system model, and thus are not incorporated in the Kalman filter. To address these combined issues, we propose a robust Kalman-type filter in the presence of non-Gaussian noise that uses information from state constraints. The proposed filter, called the maximum correntropy criterion constrained Kalman filter (MCC-CKF), uses a correntropy metric to quantify not only second-order information but also higher-order moments of the non-Gaussian process and measurement noise, and also enforces constraints on the state estimates. We analytically prove that our newly derived MCC-CKF is an unbiased estimator and has a smaller error covariance than the standard Kalman filter under certain conditions. Simulation results show the superiority of the MCC-CKF compared with other estimators when the system measurement is disturbed by non-Gaussian noise and when the states are constrained.

scholarly journals Non-Line of Sight LoRa –Based Localization using RSSI-Kalman-Filter and Trilateration

2020 ◽  
pp. 1
Author(s):  
Thirafi Wian Anugrah ◽  
Andrian Rakhmatsyah ◽  
Aulia Arif Wardana
Keyword(s):  
Kalman Filter ◽  
Signal Strength ◽  
Line Of Sight ◽  
Filter Method ◽  
Mountain Area ◽  
Localization System ◽  
Indoor Location ◽  
Kalman Filter Method ◽  
Close Result ◽  
Non Line Of Sight

<span>The method that analyzes in this research is the combination of the Received Signal Strength Indicator (RSSI) with the Trilateration Method. This research also filtered the RSSI value using the Kalman filter method for smoothing data. The localization system traditionally based on Global Positioning System (GPS) device. However, GPS technology not working well in Non-line-of-sight (NLOS) like an indoor location or mountain area. The other way to implement the localization system is by using LoRa technology. This technology used radio frequency to communicate with each other node. The radiofrequency has a measurement value in the form of signal strength. These parameters, when combined with the trilateration method, can be used as a localization system. After implementation and testing, the system can work well compared with the GPS system for localization. RMSE is used to calculate error distance on these methods, the result from three methods used, the value from RSSI with Kalman filter have a close result to actual position, then value GPS follows with close result from Kalman filter, and the last one is RSSI without Kalman filter.</span>

Dynamic Target Tracking Based on Particle Filter in Actual Environment

2013 ◽  
Vol 683 ◽  
pp. 824-827
Author(s):  
Tian Ding Chen ◽  
Chao Lu ◽  
Jian Hu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Monitoring System ◽  
Filter Method ◽  
Complex Scene ◽  
Actual Environment ◽  
Dynamic Target ◽  
Gaussian System ◽  
Non Gaussian

With the development of science and technology, target tracking was applied to many aspects of people's life, such as missile navigation, tanks localization, the plot monitoring system, robot field operation. Particle filter method dealing with the nonlinear and non-Gaussian system was widely used due to the complexity of the actual environment. This paper uses the resampling technology to reduce the particle degradation appeared in our test. Meanwhile, it compared particle filter with Kalman filter to observe their accuracy .The experiment results show that particle filter is more suitable for complex scene, so particle filter is more practical and feasible on target tracking.

scholarly journals Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise

Entropy ◽  
2017 ◽  
Vol 19 (12) ◽  
pp. 648 ◽  
Author(s):  
Bowen Hou ◽  
Zhangming He ◽  
Xuanying Zhou ◽  
Haiyin Zhou ◽  
Dong Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Gaussian Noise ◽  
Tracking Model ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian

scholarly journals A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of things

2020 ◽  
Vol 16 (9) ◽  
pp. 155014772096123
Author(s):  
Nan Hu ◽  
Chuan Lin ◽  
Fangjun Luan ◽  
Chengdong Wu ◽  
Qi Song ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Kalman Filter ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Nearest Neighbor ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Mobile Localization ◽  
M Estimation ◽  
Non Line Of Sight

As the key technology for Internet of things, wireless sensor networks have received more attentions in recent years. Mobile localization is one of the significant topics in wireless sensor networks. In wireless sensor network, non-line-of-sight propagation is a common phenomenon leading to the growing non-line-of-sight error. It is a fatal impact for the localization accuracy of the mobile target. In this article, a novel method based on the nearest neighbor variable estimation is proposed to mitigate the non-line-of-sight error. First, the linear regression model of the extended Kalman filter is used to obtain the residual of the distance measurement value. After that, the residual analysis is used to complete the identification of the measurement value state. Then, by analyzing the statistical characteristics of the non-line-of-sight residual, the nearest neighbor variable estimation is proposed to estimate the probability density function of residual. Finally, the improved M-estimation is proposed to locate the mobile robot. Experiment results prove that the accuracy and robustness of the proposed algorithm are better than other methods in the mixed line-of-sight/non-line-of-sight environment. The proposed algorithm effectively inhibits the non-line-of-sight error.

scholarly journals Removal of Non-Gaussian Jitter Noise for Shape From Focus Through Improved Maximum Correntropy Criterion Kalman Filter

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 36244-36255 ◽  
Author(s):  
Hoon-Seok Jang ◽  
Mannan Saeed Muhammad ◽  
Min-Koo Kang
Keyword(s):  
Kalman Filter ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian ◽  
Shape From Focus

Ultra Wideband Indoor Positioning Using Kalman Filters

2012 ◽  
Vol 433-440 ◽  
pp. 4207-4213 ◽  
Author(s):  
Li Zhang ◽  
Hao Zhang ◽  
Xue Rong Cui ◽  
T Aaron Gulliver
Keyword(s):  
Kalman Filter ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Time Difference Of Arrival ◽  
Positioning Systems ◽  
Positioning Technique ◽  
Performance Results ◽  
Source Of Error ◽  
Mitigation Technique ◽  
Non Line Of Sight

A time-difference-of-arrival (TDOA) positioning technique for indoor ultra wideband (UWB) systems is presented. Non-line-of-sight (NLOS) propagation error is a major source of error in positioning systems. Therefore an NLOS mitigation technique employing a Kalman filter is utilized to reduce the NLOS errors in indoor UWB environments. An extended Kalman filter (EKF) is used to process the TDOA data for mobile positioning and tracking. Performance results are presented which show that the proposed scheme can significantly improve the positioning accuracy in a UWB environment.

Research on Line-of-Sight Rate Estimation of Strapdown Seeker

2014 ◽  
Vol 556-562 ◽  
pp. 3739-3744 ◽  
Author(s):  
Pei Wang ◽  
Ke Zhang ◽  
Cong Nie
Keyword(s):  
Kalman Filter ◽  
Gaussian Noise ◽  
Line Of Sight ◽  
Proportional Navigation ◽  
Unscented Particle Filter ◽  
Cubature Kalman Filter ◽  
On Line ◽  
New Type ◽  
Attitude Measurements ◽  
Non Gaussian

As a new type of guidance technology, the strapdown imaging guidance technology, which helps improve the system reliability and reduce costs efficiently, has been paid great attention and developed quickly. However, the detecting information of strapdown seeker can’t be used to proportional navigation directly because it is coupled with missile attitude. A method to estimate the inertial Line-of-Sight (LOS) rate of strapdown imaging seeker based on Cubature Kalman filter (CKF) and Tracking-Differentiator (TD) was presented. As there were high nonlinearity in both state and measurement equations and more serious non-Gaussian noise in the measurements, the Extended Kalman filter (EKF) could not completely meet the requirements of filtering. Compared with EKF and Unscented Particle filter (UKF), CKF was a congruent method for states estimating in the conditions of nonlinearity and non-Gaussian noise. CKF was applied to estimate the LOS rate of strapdown imaging seeker. Because measurement noise of missile attitude will be reflected in estimation result, TD was used to decrease noise of the missile attitude measurements for improving the estimation precision. Monte Carlo simulation results show that the proposed method can improve the precision of guidance.

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