Converted state equation Kalman filter for nonlinear maneuvering target tracking

Wen, Zhang; Zhao, Xiaofeng; Liu, Zengli; Liu, Kang; Chen, Bo

doi:10.1016/j.sigpro.2022.108741

Cited by 13 publications

(7 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These outputs are spherical, but only the polar components (distance and azimuth) are considered in the 2-D model. Since the intruder dynamics are best described in rectangular coordinates, the converted measurement Kalman filter (CMKF) was used to convert the measurement data in polar coordinates into a Cartesian coordinate system, so that the tracking could be realized through a linear Kalman filter [31]. The 2-D model used in the simulations was then represented by…”

Section: Radarmentioning

confidence: 99%

Optimal Multi-Sensor Obstacle Detection System for Small Fixed-Wing UAVs

Portugal,

Marta

2023

Modelling

View full text Add to dashboard Cite

The safety enhancement of small fixed-wing UAVs regarding obstacle detection is addressed using optimization techniques to find the best sensor orientations of different multi-sensor configurations. Four types of sensors for obstacle detection are modeled, namely an ultrasonic sensor, laser rangefinder, LIDAR, and RADAR, using specifications from commercially available models. The simulation environment developed includes collision avoidance with the Potential Fields method. An optimization study is conducted using a genetic algorithm that identifies the best sensor sets and respective orientations relative to the UAV longitudinal axis for the highest obstacle avoidance success rate. The UAV performance is found to be critical for the solutions found, and its speed is considered in the range of 5–15 m/s with a turning rate limited to 45°/s. Forty collision scenarios with both stationary and moving obstacles are randomly generated. Among the combinations of the sensors studied, 12 sensor sets are presented. The ultrasonic sensors prove to be inadequate due to their very limited range, while the laser rangefinders benefit from extended range but have a narrow field of view. In contrast, LIDAR and RADAR emerge as promising options with significant ranges and wide field of views. The best configurations involve a front-facing LIDAR complemented with two laser rangefinders oriented at ±10° or two RADARs oriented at ±28°.

show abstract

Section: Radarmentioning

confidence: 99%

Optimal Multi-Sensor Obstacle Detection System for Small Fixed-Wing UAVs

Portugal,

Marta

2023

Modelling

View full text Add to dashboard Cite

show abstract

“…For filters, consistency is equally as crucial as accuracy because it can help us gauge the algorithm’s robustness. The average normalized estimation error square (ANEES), the consistency analysis index, can be calculated as follows [ 7 ]:

where

and

are the state estimation error and covariance matrix at time k , respectively. If

, the filter is considered consistent, where

and

are the lower and upper bounds of the acceptance interval, respectively.…”

Section: Numerical Examples and Analysismentioning

confidence: 99%

“…where For filters, consistency is equally as crucial as accuracy because it can help us gauge the algorithm's robustness. The average normalized estimation error square (ANEES), the consistency analysis index, can be calculated as follows [7]:…”

Section: Numerical Examples and Analysismentioning

confidence: 99%

See 1 more Smart Citation

A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises

Wang

et al. 2023

Sensors

View full text Add to dashboard Cite

The features of measurement and process noise are directly related to the optimal performance of the cubature Kalman filter. The maneuvering target model’s high level of uncertainty and non-Gaussian mean noise are typical issues that the radar tracking system must deal with, making it impossible to obtain the appropriate estimation. How to strike a compromise between high robustness and estimation accuracy while designing filters has always been challenging. The H-infinity filter is a widely used robust algorithm. Based on the H-infinity cubature Kalman filter (HCKF), a novel adaptive robust cubature Kalman filter (ARCKF) is suggested in this paper. There are two adaptable components in the algorithm. First, an adaptive fading factor addresses the model uncertainty issue brought on by the target’s maneuvering turn. Second, an improved Sage–Husa estimation based on the Mahalanobis distance (MD) is suggested to estimate the measurement noise covariance matrix adaptively. The new approach significantly increases the robustness and estimation precision of the HCKF. According to the simulation results, the suggested algorithm is more effective than the conventional HCKF at handling system model errors and abnormal observations.

show abstract

“…Although these methods have improved the target tracking performance of the Doppler radar, improving tracking accuracy is still necessary. One study [26] proposed the Converted State Kalman Filter (CSKF) algorithm to address the nonlinear problems in the motion and measurement equations in target tracking. This algorithm converts the equations of motion to the polar coordinate using the Cartesian coordinate, making the state and observation linearly related.…”

Section: Introductionmentioning

confidence: 99%

A Method to Track Moving Targets Using a Doppler Radar Based on Converted State Kalman Filtering

Zhao,

Liu

et al. 2024

Electronics

Self Cite

View full text Add to dashboard Cite

Strong nonlinearity between Doppler measurement and target motion in Doppler radar target tracking leads to the inadequate utilization of measurement information and limited tracking accuracy. We solved this problem by combining converted state Kalman filtering and the Interacting Multiple Model. This maneuvering target tracking method is suitable for Doppler measurement. First, we converted the target motion in the Cartesian coordinate to the polar coordinate. Then, we expanded the measurement equation to include Doppler measurement, making target motion linearly related to the Doppler radar observation vectors and allowing efficient utilization of measurement information. Next, we used unscented transformation to calculate the statistical characteristics of the process noise in the polar coordinate. This process helps to reduce the noise error caused by the coordinate system transformation in the original converted state Kalman filter. Finally, the system effectively tracks targets that may perform maneuvers with unknown motion during actual tracking. Using the converted state Kalman filter with Doppler measurement as a sub-filter, an Interacting Multiple Model tracking method can be constructed to adjust the model probabilities without going through nonlinear transformation. Simulation results show that the technique can achieve effective target tracking in Doppler measurement application scenarios and has higher tracking accuracy in non-maneuvering and maneuvering scenarios.

show abstract

Converted state equation Kalman filter for nonlinear maneuvering target tracking

Cited by 13 publications

References 36 publications

Optimal Multi-Sensor Obstacle Detection System for Small Fixed-Wing UAVs

Optimal Multi-Sensor Obstacle Detection System for Small Fixed-Wing UAVs

A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises

A Method to Track Moving Targets Using a Doppler Radar Based on Converted State Kalman Filtering

Contact Info

Product

Resources

About