Nonlinear Kalman Filter Methods for Predicting Ship Encounter Situations in Terms of Near-Miss Collision Risk

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Yuting Deng ◽  
Xinyu Feng ◽  
Dan Liu ◽  
Weibin Zhang
Keyword(s):  
Kalman Filter ◽  
Near Miss ◽  
Collision Risk ◽  
Filter Methods ◽  
Nonlinear Kalman Filter

scholarly journals Improving Near Miss Detection in Maritime Traffic in the Northern Baltic Sea from AIS Data

2021 ◽  
Vol 9 (2) ◽  
pp. 180
Author(s):  
Lei Du ◽  
Osiris A. Valdez Banda ◽  
Floris Goerlandt ◽  
Pentti Kujala ◽  
Weibin Zhang
Keyword(s):  
Baltic Sea ◽  
Perceived Risk ◽  
Maritime Transportation ◽  
Near Miss ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Ship Collision ◽  
The Impact ◽  
Ship Behavior

Ship collision is the most common type of accident in the Northern Baltic Sea, posing a risk to the safety of maritime transportation. Near miss detection from automatic identification system (AIS) data provides insight into maritime transportation safety. Collision risk always triggers a ship to maneuver for safe passing. Some frenetic rudder actions occur at the last moment before ship collision. However, the relationship between ship behavior and collision risk is not fully clarified. Therefore, this work proposes a novel method to improve near miss detection by analyzing ship behavior characteristic during the encounter process. The impact from the ship attributes (including ship size, type, and maneuverability), perceived risk of a navigator, traffic complexity, and traffic rule are considered to obtain insights into the ship behavior. The risk severity of the detected near miss is further quantified into four levels. This proposed method is then applied to traffic data from the Northern Baltic Sea. The promising results of near miss detection and the model validity test suggest that this work contributes to the development of preventive measures in maritime management to enhance to navigational safety, such as setting a precautionary area in the hotspot areas. Several advantages and limitations of the presented method for near miss detection are discussed.

scholarly journals Thermal anomalies detection before strong earthquakes (<i>M</i> > 6.0) using interquartile, wavelet and Kalman filter methods

2011 ◽  
Vol 11 (4) ◽  
pp. 1099-1108 ◽  
Author(s):  
M. R. Saradjian ◽  
M. Akhoondzadeh
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Wavelet Transform ◽  
Anomaly Detection ◽  
Land Surface ◽  
Seasonal Effects ◽  
Filter Method ◽  
Strong Earthquakes ◽  
Thermal Anomalies ◽  
Filter Methods

Abstract. Thermal anomaly is known as a significant precursor of strong earthquakes, therefore Land Surface Temperature (LST) time series have been analyzed in this study to locate relevant anomalous variations prior to the Bam (26 December 2003), Zarand (22 February 2005) and Borujerd (31 March 2006) earthquakes. The duration of the three datasets which are comprised of MODIS LST images is 44, 28 and 46 days for the Bam, Zarand and Borujerd earthquakes, respectively. In order to exclude variations of LST from temperature seasonal effects, Air Temperature (AT) data derived from the meteorological stations close to the earthquakes epicenters have been taken into account. The detection of thermal anomalies has been assessed using interquartile, wavelet transform and Kalman filter methods, each presenting its own independent property in anomaly detection. The interquartile method has been used to construct the higher and lower bounds in LST data to detect disturbed states outside the bounds which might be associated with impending earthquakes. The wavelet transform method has been used to locate local maxima within each time series of LST data for identifying earthquake anomalies by a predefined threshold. Also, the prediction property of the Kalman filter has been used in the detection process of prominent LST anomalies. The results concerning the methodology indicate that the interquartile method is capable of detecting the highest intensity anomaly values, the wavelet transform is sensitive to sudden changes, and the Kalman filter method significantly detects the highest unpredictable variations of LST. The three methods detected anomalous occurrences during 1 to 20 days prior to the earthquakes showing close agreement in results found between the different applied methods on LST data in the detection of pre-seismic anomalies. The proposed method for anomaly detection was also applied on regions irrelevant to earthquakes for which no anomaly was detected, indicating that the anomalous behaviors can be related to impending earthquakes. The proposed method receives its credibility from the overall capabilities of the three integrated methods.

Towards a Model of Regional Vessel Near-miss Collision Risk Assessment for Open Waters based on AIS Data

2019 ◽  
Vol 72 (06) ◽  
pp. 1449-1468 ◽  
Author(s):  
Weibin Zhang ◽  
Xinyu Feng ◽  
Yong Qi ◽  
Feng Shu ◽  
Yijin Zhang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Safety Management ◽  
Open Water ◽  
Water Area ◽  
Assessment System ◽  
Near Miss ◽  
Collision Risk ◽  
Accurate Analysis ◽  
Open Water Area ◽  
Temporal Features

The absence of a regional, open water vessel collision risk assessment system endangers maritime traffic and hampers safety management. Most recent studies have analysed the risk of collision for a pair of vessels and propose micro-level risk models. This study proposes a new method that combines density complexity and a multi-vessel collision risk operator for assessing regional vessel collision risk. This regional model considers spatial and temporal features of vessel trajectory in an open water area and assesses multi-vessel near-miss collision risk through danger probabilities and possible consequences of collision risks via four types of possible relative striking positions. Finally, the clustering method of multi-vessel encountering risk, based on the proposed model, is used to identify high-risk collision areas, which allow reliable and accurate analysis to aid implementation of safety measures.

scholarly journals People counter on CCTV video using histogram of oriented gradient and Kalman filter methods

2020 ◽  
Vol 8 (3) ◽  
pp. 222-227
Author(s):  
Faisal Dharma Adhinata ◽  
Muhammad Ikhsan ◽  
Wahyono Wahyono
Keyword(s):  
Kalman Filter ◽  
Mutual Information ◽  
Public Service ◽  
Information Entropy ◽  
Support Service ◽  
Entropy Method ◽  
Histogram Of Oriented Gradient ◽  
Filter Methods ◽  
Human Object ◽  
Long Time

CCTV cameras have an important function in the field of public service, especially for convenience. The objects recorded through CCTV cameras are processed into information to support service satisfaction in the community. This study uses the function of CCTV for people counting from objects recorded by a camera. Currently, the process of detecting and tracking people takes a long time to detect all frames. In this study, the frame selection into keyframes uses the mutual information entropy method. The keyframes processing uses the Histogram of Oriented Gradient (HOG) and Kalman filter methods. The proposed method results F1 value of 0.85, recall of 76 %, and precision of 97 % with winStride parameter (12,12), scale 1.05, and the distance of the human object to CCTV 4 meters.

scholarly journals Simultaneous and sequential state and parameter estimation using receding-horizon nonlinear Kalman filter

2022 ◽  
Vol 109 ◽  
pp. 13-31
Author(s):  
Pavanraj H. Rangegowda ◽  
Jayaram Valluru ◽  
Sachin C. Patwardhan ◽  
Siddhartha Mukhopadhyay
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Receding Horizon ◽  
Nonlinear Kalman Filter

scholarly journals Improving conflicts detection in maritime traffic: Case studies on the effect of traffic complexity on ship collisions

2019 ◽  
Vol 234 (1) ◽  
pp. 209-222 ◽  
Author(s):  
Fulko van Westrenen ◽  
Michael Baldauf
Keyword(s):  
Near Miss ◽  
Traffic Density ◽  
Decision Making Process ◽  
High Complexity ◽  
Collision Risk ◽  
Maritime Traffic ◽  
Open Sea ◽  
Reliable Detection ◽  
Human Operators ◽  
Actual Geometry

In shipping, collision risk is a serious safety threat. Risk probability estimations used for policymaking are derived from traffic density statistics, largely ignoring the decision-making process on board. Conflict detection and resolution on board is done using rather rudimentary but effective mental-model-based techniques. In this article, the authors analyse traffic using the concept of complexity. The actual geometry of the ships involved in the conflict defines how well the crews on board can resolve the conflict. This geometry is transformed into a complexity value. A reliable detection and resolution of conflicts by human operators decreases in certain situations. A previous study has shown that when complexity reaches a threshold, the risk of a near miss increases significantly. In this study, three actual collisions at open sea are analysed. It will be shown that situations of high complexity, which decreases human reliability, can be predicted well in advance, allowing for a safe resolution. The technique also allows for alerting and a decision support for the crew.

Sign in / Sign up

Export Citation Format

Share Document