Augmented Kalman Filter and Map Matching for 3D RISS/GPS Integration for Land Vehicles

Cossaboom, Matthew; Georgy, Jacques; Karamat, Tashfeen B.; Noureldin, Aboelmagd

doi:10.1155/2012/576807

Cited by 34 publications

(27 citation statements)

References 25 publications

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“…In this paper, the point-to-point map-matching method is used because of the simple format requirements of the digital map. The algorithm projects the estimated location,

P (X_{P}, Y_{P})

, to the closest link in the network using the distance form Equation (13) [31]:

X_{P} = \frac{[A X_{e} + B Y_{e}] + B (X_{1} Y_{2} - X_{2} Y_{1})}{[A^{2} + B^{2}] A^{- 1}}

Y_{P} = \frac{[A X_{e} + B Y_{e}] + A (X_{1} Y_{2} - X_{2} Y_{1})}{[A^{2} + B^{2}] B^{- 1}}

in which

(X_{e}, Y_{e})

is the position estimated from the MA-based APF algorithm,

(X_{1}, Y_{2})

is the start-point, and

(X_{2}, Y_{2})

is the end-point of the closest line segment in the digital map. Additionally,

A = (X_{2} - X_{1})

, and

B = (Y_{2} - Y_{1})

.…”

Section: Methodsmentioning

confidence: 99%

A Map/INS/Wi-Fi Integrated System for Indoor Location-Based Service Applications

Lan

et al. 2017

Sensors

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In this research, a new Map/INS/Wi-Fi integrated system for indoor location-based service (LBS) applications based on a cascaded Particle/Kalman filter framework structure is proposed. Two-dimension indoor map information, together with measurements from an inertial measurement unit (IMU) and Received Signal Strength Indicator (RSSI) value, are integrated for estimating positioning information. The main challenge of this research is how to make effective use of various measurements that complement each other in order to obtain an accurate, continuous, and low-cost position solution without increasing the computational burden of the system. Therefore, to eliminate the cumulative drift caused by low-cost IMU sensor errors, the ubiquitous Wi-Fi signal and non-holonomic constraints are rationally used to correct the IMU-derived navigation solution through the extended Kalman Filter (EKF). Moreover, the map-aiding method and map-matching method are innovatively combined to constrain the primary Wi-Fi/IMU-derived position through an Auxiliary Value Particle Filter (AVPF). Different sources of information are incorporated through a cascaded structure EKF/AVPF filter algorithm. Indoor tests show that the proposed method can effectively reduce the accumulation of positioning errors of a stand-alone Inertial Navigation System (INS), and provide a stable, continuous and reliable indoor location service.

show abstract

“…In this paper, the point-to-point map-matching method is used because of the simple format requirements of the digital map. The algorithm projects the estimated location,

P (X_{P}, Y_{P})

, to the closest link in the network using the distance form Equation (13) [31]:

X_{P} = \frac{[A X_{e} + B Y_{e}] + B (X_{1} Y_{2} - X_{2} Y_{1})}{[A^{2} + B^{2}] A^{- 1}}

Y_{P} = \frac{[A X_{e} + B Y_{e}] + A (X_{1} Y_{2} - X_{2} Y_{1})}{[A^{2} + B^{2}] B^{- 1}}

in which

(X_{e}, Y_{e})

is the position estimated from the MA-based APF algorithm,

(X_{1}, Y_{2})

is the start-point, and

(X_{2}, Y_{2})

is the end-point of the closest line segment in the digital map. Additionally,

A = (X_{2} - X_{1})

, and

B = (Y_{2} - Y_{1})

.…”

Section: Methodsmentioning

confidence: 99%

A Map/INS/Wi-Fi Integrated System for Indoor Location-Based Service Applications

Lan

et al. 2017

Sensors

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show abstract

“…The advanced map-matching algorithms, such as Kalmam filter [15][16][17][18][19] , Dempster-Shafer's mathematical (D-S) theory of evidence [20][21] , fuzzy logic model [22][23][24] , cost function model or computational geometry model, use more refined concepts. Krakiwsky was the first one who proposed the Kalmam filter map-matching algorithm [25] .…”

Section: Related Workmentioning

confidence: 99%

A Vehicle Map-matching Algorithm based on Measure Fuzzy Sorting

Wu¹,

Gu²,

Luo³

et al. 2014

JCP

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The vehicle position obtained from GPS and dead reckoning is wildly applied to car navigation systems. However, the estimated position has an undesirable error due to the unknown GPS noise. To solve this problem, previous papers presented a method called "map-matching" to correct the position error. In this paper, we proposes a fuzzy ranking map matching algorithm based on measure factor. Comparing with other four algorithms, our algorithm improves in strategies of the error region determination, the road grid index and auto-adapted fuzzy sorting. To be specific, the error rectangle is firstly replaced by the error ellipse to reduce geometrical operation. Secondly, the grid index is adopted to accelerate the speed of filtering candidate road. At last, the relativity function and fuzzy sorting method help to sort the membership degree and to decide the matching road section. For the experiments, we implement a vehicle navigation system of five kinds of vehicle running status to testify the robustness and efficiency of this algorithm. The result shows that 96.7% of the GPS points are matched. In comparison with other algorithms, this algorithm had highest accuracy, which is of importance for vehicle navigation.Index Terms-fuzzy set, measure fuzzy sorting, map matching, vehicle navigation system I.

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“…Moreover, sensor fusion based on Internet of Things technology also enables the simultaneous measurement of position and velocity (e.g., sensor data fusion based on the communication between radars/lasers/sonars and speedometers embedded in targets). Consequently, Kalman filters for such systems have become an important area of research [19][20][21][22][23][24]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Kalman Filter for Moving Object Tracking: Performance Analysis and Filter Design

Saho¹

2018

Kalman Filters - Theory for Advanced Applications

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This chapter presents Kalman filters for tracking moving objects and their efficient design strategy based on steady-state performance analysis. First, a dynamic/measurement model is defined for the tracking systems, assuming both position-only and position-velocity measurements. Then, problems with the Kalman filter design in tracking systems are summarized, and an efficient steady-state performance index proposed by the author [termed the root-mean-squared error index (the RMS index)] is introduced to resolve these concerns. The analytical relationship between the proposed RMS index and the covariance matrix of the process noise is shown, leading to a proposed design strategy that is based on this relationship. Theoretical performance analysis is conducted using the performance indices to show the optimality of the design strategy. Numerical simulations show the validity of the theoretical analyses and effectiveness of the proposed strategy in realistic situations. In addition, the optimal performance of the position-only-measured and position-velocity-measured systems is analyzed and compared. This comparison shows that the position-velocity-measured Kalman filter tracking is accurate when compared with the position-only-measured filter.

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Augmented Kalman Filter and Map Matching for 3D RISS/GPS Integration for Land Vehicles

Cited by 34 publications

References 25 publications

A Map/INS/Wi-Fi Integrated System for Indoor Location-Based Service Applications

A Map/INS/Wi-Fi Integrated System for Indoor Location-Based Service Applications

A Vehicle Map-matching Algorithm based on Measure Fuzzy Sorting

Kalman Filter for Moving Object Tracking: Performance Analysis and Filter Design

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