Radar Plot Extraction and Tracking Systems Research Papers (original) (raw)

Visual monitoring activities using cameras automatically without human intervention is a big and challenging problem so we need automatic object tracker system. This paper presents a new object tracking system in Real time that systematically combines both motion detection and sound detection. In this system detect motion as well as sound in a real time and if lack of security it is also give alert message through alarm. The proposed method is excellent in real-time performance because it detect the moving objects efficiently and accurately form the video recorded by a shaking camera with changing background and noises.

Radar is an acronym for Radio Detection And Ranging.

Radar have become indispensable in several major fields of research and in commerce.

This project is about the Radar System controlled via Arduino. This RADAR system consists of an ultrasonic sensor and servo motor, these are the major components of the system. The basic working of the system is that it has to detect objects in its defined range. The ultrasonic sensor is attached to the servo motor it rotates about 180 degrees and gives visual representation on the software called processing IDE. Processing IDE gives graphical representation and it also gives the angle or position of the object and distance of the object. This system is controlled through Arduino. Arduino UNO board is sufficient to control ultrasonic sensors and also to interface the sensor and display device. While researching, we learned about existing navigation and obstacle detection innovations and different systems where ultrasonic sensors are used efficiently. The main application of this RADAR system comes into a different field of navigation, positioning, object identification, mapping, spying or tracking, and different applications. These fewer investment systems are also suitable for indoor applications.

This project aimed to compare the use of and resultant errors when Measurement Fusion (Plot Fusion) and Track Fusion were used to combine data from various sensors in a simulated environment analogous to the Singaporean environment. The environment and analysis was done wholly using a program executed by MATLAB 6.1, and results showed that Measurement Fusion was more accurate when tracking objects following a path with many turns. However, the major source of error was not the fusion algorithm, but the inclusion algorithm.

This chapter introduces the basics of designing a tracking system. It presents the problems and flaws existent in different designs. It will be seen that there is no one ideal solution to the tracking problem, and it remains a challenge... more

This chapter introduces the basics of designing a tracking system. It presents the problems and flaws existent in different designs. It will be seen that there is no one ideal solution to the
tracking problem, and it remains a challenge to combine various solutions together to obtain an optimal tracking design.

Kalman filter has been proven to be a very effective method to identify targets in an efficient and accurate manner. It provides efficient estimations when the precise nature of the modeled system is unknown in the presence of measurement and process noise. However, Kalman filter is computationally extensive especially in Multi Target Tracking (MTT) radar system. Therefore, it is desirable to apply it on advanced parallel architecture such as FPGA, GPU, and multi-cores to increase performance and achieve real time requirements. In this paper, we present an efficient parallel architecture of Kalman filter on different platforms such as FPGA, GPU, and multi-core. Kalman filter operations are carried out on a single core CPU before they are decomposed, parallelized, scheduled, and mapped into FPGA and GPU platforms. Different optimization techniques for both the computation and memory utilization are adopted and applied to achieve high performance. The experimental results show the viability of using FPGA and GPU platforms to perform signal processing in real time. Parallel architectures can significantly outperform an equivalent sequential implementation due to their pipelined architecture, custom functionality of VLSI ASIC devices, flexibility, and adaptability. Our simulation results indicate that the achieved speed-up of FPGA and GPU over the sequential one is improved by up to 37.76 and 31.93, respectively.

This paper describes the practical use of ASTERIX CAT-240 messages for the network distribution of radar video. The standard, which has emerged from the European Air Traffic Control community, offers a method of harmonising the exchange of radar video from a sensor or server into multiple display clients. This paper describes the background to the standard and explains some of the practical challenges for deployed systems in terms of IP fragmentation, compression and standards.

- by A Wild
- •
- Naval Engineering, Sensors and Sensing, GPS Applications, Air traffic control

Some theoretical background to tracking and data fusion was provided in the first two chapters. An analysis of the tracking program was then carried out. Subsequently, modifications were made to the program with the aim of improving... more

Fusion in radar tracking can be classified broadly into two types – plot fusion and track fusion. Plot fusion, otherwise known as measurement fusion, is based on a centralized architecture, where all estimation and fusion is carried out in the central node. Track fusion, on the other hand, is based on a hierarchical architecture, whereby sensors do some form of processing to obtain an estimate, which is transmitted to the central node for subsequent fusion.