An improved multiple model particle filtering approach for manoeuvring target tracking using Airborne GMTI with geographic information

This paper proposes a novel ground vehicle tracking method using an airborne ground moving target indicator radar where the surrounding geographic information is considered to determine vehicle’s movement type as well as constrain its positions. Multiple state models corresponding to different movement modes are applied to represent the vehicle’s behaviour within different terrain conditions. Based on geographic conditions and multiple state models, a constrained variable structure multiple model particle filter algorithm aided by particle swarm optimisation is proposed. Compared with the traditional multiple model particle filtering schemes, the proposed algorithm utilises a particle swarm optimisation technique for the particle filter which generates more effective particles and generated particles are constrained into the feasible geographic region. Numerical simulation results in a realistic environment show that the proposed method achieves better tracking performance compared with current state-of-the-art ones for manoeuvring vehicle tracking

An improved multiple model particle filtering approach for manoeuvring target tracking using airborne GMTI with geographic information

This paper proposes a ground vehicle tracking method using an airborne ground moving target indicator radar where the surrounding geographic information is considered to determine vehicle's movement type as well as constrain its positions. Multiple state models corresponding to different movement modes are applied to represent the vehicle's behaviour in different terrain conditions. Based on geographic conditions and multiple state models, a constrained variable structure multiple model particle filter algorithm is proposed. Compared with the traditional multiple model particle filtering schemes, the proposed algorithm utilises a particle swarm optimisation technique which generates more effective particles and generated particles are constrained into the feasible geographic region. Numerical simulation results in a realistic environment show that the proposed method achieves better tracking performance compared with current state-of-the-art ones for manoeuvring vehicle tracking

Deteksi Sebaran Gas Sulfur Dioksida (SO2) Hasil Erupsi Gunung Sinabung Menggunakan Citra Satelit

ABSTRACT Periode letusan Gunung Sinabung dimulai pada tahun 2010 dan terus berlanjut hingga tahun 2019. Material piroklastik yang dikeluarkan selama letusan gunung berapi mengandung belerang yang melekat pada garam sulfat di permukaan partikel atau gas. Kemudian gas SO2 dari letusan tersebut akan berdampak langsung pada daerah sekitarnya. Spektral dari sensor optik satelit dapat digunakan untuk memantau dan mengukur gas SO2 saat dan setelah terjadi letusan. Distribusi Spasial Emisi vulkanik gas SO2 dideteksi dengan menggunakan satelit Sentinel 5P. Peninjauan secara langsung dilapangan dilakukan untuk melihat dampak sebaran gas SO2 terhadap kimia tanah. Regresion Kriging digunakan untuk menginterpretasikan dampat tersebut pada wilayah penelitian. Daerah yang diteliti terletak pada radius 3-7 Km Timur Laut hingga Selatan Gunung Sinabung dengan luasan 4.517 ha. Sebanyak 51 titik sampel abu vulkanis dan tanah diambil pada permukaan tanah kedalaman 0-20 cm berdasarkan interval grid 1x1 Km. Semua sampel dikeringanginkan, di ayak dan dilakukan analisis pH, Elemental Oksida, dan Sulfat tersedia. Aplikasi Google Earth Engine (GEE) juga digunakan untuk memproses citra satelit sentinel 5P untuk menentukan jumlah dan distribusi kepadatan kolom SO2 di atmosfer selama tahun 2019. Didapatkan Bahwa nilai pH abu sangat masam hingga netral (3,56 -6,55), sedangkan tanah dikategorikan masam hingga netral (4,67 - 6,52). Kandungan sulfat yang tersedia dalam tanah berkisar antara 0-303,39 ppm dan 0-142,47 ppm pada sampel abu vulkanis. Kandungan SO3 total dalam abu vulkanis berkisar antara 0%-16,53% dan 0-3,71% dalam tanah. Data spektral citra satelit Sentinel 5P menunjukkan bahwa SO2 terkonsentrasi terutama di wilayah Timur Laut dan Timur, dengan tingkat emisi tertinggi terjadi pada bulan Agustus 2019. Kata kunci : Gunung Sinabung, SO2, Sentinel 5P, GEE

Sensor Data Fusion for Improving Traffic Mobility in Smart Cities

The ever-increasing urban population and vehicular traffic without a corresponding expansion of infrastructure have been a challenge to transportation facilities managers and commuters. While some parts of transportation infrastructure have big data available, so many other locations have sparse data. This has posed a challenge in traffic state estimation and prediction for efficient and effective infrastructure management and route guidance. This research focused on traffic prediction problems and aims to develop novel spatial-temporal and robust algorithms, that can provide high accuracy in the presence of both big data and sparse data in a large urban road network. Intelligent transportation systems require the knowledge of current traffic state and forecast for effective implementation. The actual traffic state has to be estimated as the existing sensors do not capture the needed state. Sensor measurements often contain missing or incomplete data as a result of communication issues, faulty sensors or cost leading to incomplete monitoring of the entire road network. This missing data pose challenges to traffic estimation approaches. In this work, a robust spatio-temporal traffic imputation approach capable of withstanding high missing data rate is presented. A particle-based approach with Kriging interpolation is proposed. The performance of the particle-based Kriging interpolation for different missing data ratios was investigated for a large road network. A particle-based framework for dealing with missing data is also proposed. An expression of the likelihood function is derived for the case when the missing value is calculated based on Kriging interpolation. With the Kriging interpolation, the missing values of the measurements are predicted, which are subsequently used in the computation of likelihood terms in the particle filter algorithm. In the commonly used Kriging approaches, the covariance function depends only on the separation distance irrespective of the traffic at the considered locations. A key limitation of such an approach is its inability to capture well the traffic dynamics and transitions between different states. This thesis proposes a Bayesian Kriging approach for the prediction of urban traffic. The approach can capture these dynamics and model changes via the covariance matrix. The main novelty consists in representing both stationary and non-stationary changes in traffic flows by a discriminative covariance function conditioned on the observation at each location. An advantage is that by considering the surrounding traffic information distinctively, the proposed method is very likely to represent congested regions and interactions in both upstream and downstream areas

Penerapan Algoritma Dijkstra Untuk Menentukan Rute Terbaik Pada Mobile E-Parking Berbasis Sistem Informasi Geografis

Lack of information about the posision of a place and find the best route to get to a destination is complicated, especially looking for a route in finding a parking space. Unavailability of applications that provide information and navigate parking users to find the best route in realtime is a problem that must be answered. Dijkstra's algorithm is one method of solving problems in finding the best route. The Dijkstra algorithm has compared the possible routes to be traversed and calculated every possible distance. The route with the shortest distance would be the best choice This application was built using an Android-based Geographic Information System that utilizes the Google Maps feature as a parking user interface service. This study uses the Java programming language with Android Studio as an IDE (Integrated Development Environment) and the prototyping model is used in developing problem solving and using UML (Unified Modeling Language) as a system design tool. The results that have been achieved from this research is that Dijkstra's algorithm can answer the problem faced by providing the best route navigation from the user's location to the parking lot at the destinatio