摘要 :
This study is about track initiation of targets using measurements from a radar. It is difficult to initiate a track for a target far from the radar, because of low detection probability. To maintain high detection probability, th...
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This study is about track initiation of targets using measurements from a radar. It is difficult to initiate a track for a target far from the radar, because of low detection probability. To maintain high detection probability, the threshold of the radar must be set low. However the density of false alarms is increased simultaneously, so tracking algorithm must solve a hard association problem. The conventional method is not feasible because many false tracks are initiated. On the other hand, the MHT (Multiple Hypothesis Tracking) shows a good performance of association and as an improved algorithm, the Track Oriented MHT is proposed. We evaluated the performance of the track initiation method using Track Oriented MHT. We measured the track initiation distance in different radar threshold levels.
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摘要 :
This study is about track initiation of targets using measurements from a radar. It is difficult to initiate a track for a target in clutter environment. The conventional method is not feasible because many false tracks are initia...
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This study is about track initiation of targets using measurements from a radar. It is difficult to initiate a track for a target in clutter environment. The conventional method is not feasible because many false tracks are initiated. On the other hand, the MHT(Multiple Hypothesis Tracking) shows a good performance of association and as an improved algorithm, the Track Oriented MHT is proposed. We tried the improvements of Track Oriented MHT by using Doppler Velocity for reduction of false tracks. In the improved algorithm, observed Doppler velocity is compared with velocity calculated by the tracking filter. We evaluated the performance of the improved track initiation methods using Track Oriented MHT.
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摘要 :
In both military and civilian surveillance systems such as Air Traffic Control (ATC) systems, tracking targets in clutter using radar involves dealing with a number of challenges, all related to real time decision and data fusion ...
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In both military and civilian surveillance systems such as Air Traffic Control (ATC) systems, tracking targets in clutter using radar involves dealing with a number of challenges, all related to real time decision and data fusion theories. A major challenge is to detect the real targets through the received measurements in real time and to activate the tracking process.
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A novel track initiation method using ants of different tasks, a kind of ant colony optimization (ACO) algorithm, is developed in this paper. For the proposed system of ants of different tasks, we assume that the number of tracks ...
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A novel track initiation method using ants of different tasks, a kind of ant colony optimization (ACO) algorithm, is developed in this paper. For the proposed system of ants of different tasks, we assume that the number of tracks to be initiated equals the one of tasks, and moreover, ants of the same task search for a given track by collaboration, while ants of different tasks will compete with each other during the search process. In order to fulfill such behaviors, the pheromone model is established, and the corresponding objective function to be optimized is also presented. Numerical simulation results indicate that, for the case of bearings-only multi-sensor-multi-target tracking, the track initiation performance for the proposed system of ants of different tasks performs well compared to other track initiation methods.
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The fundamental problem of target tracking in a dense multi-target environment with clutter, false alarm, missed detections, and so forth is the uncertain origin of measurements. The essential requirements under these environment ...
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The fundamental problem of target tracking in a dense multi-target environment with clutter, false alarm, missed detections, and so forth is the uncertain origin of measurements. The essential requirements under these environment are to extract the tracks of targets and reject missed tracks of targets. MHT (Multiple Hypotheses Tracking) has a lot of attention in recent years to put into practice these requirements. Conventional MHT, however, required enormous amounts of computer time because all conceivable hit-to-track association hypotheses including new targets To resolve these problem, we enhance the conventional MHT by the limitation of the number of hypotheses by establishing tracks at each scan. In this paper, we evaluate the performance of the track-initiation between the proposed method and the conventional method.
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This paper proposes a target positioning algorithm using asynchronous TDOA(Time Difference of Arrival) and FDOA(Frequency Difference of Arrival) measurements for a single target in a distributed sensor network. A conventional algo...
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This paper proposes a target positioning algorithm using asynchronous TDOA(Time Difference of Arrival) and FDOA(Frequency Difference of Arrival) measurements for a single target in a distributed sensor network. A conventional algorithm, target localization through TDOA measurements cannot estimate a target's position when the number of TDOA measurements is not enough for localization at the same time. Our algorithm uses TDOA and FDOA measurements at the different time to compute the target's position and velocity estimates. Through computer simulation trials, the validity of our algorithm is confirmed.
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摘要 :
This paper proposes a target positioning algorithm using asynchronous TDOA(Time Difference of Arrival) and FDOA(Frequency Difference of Arrival) measurements for a single target in a distributed sensor network. A conventional aigo...
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This paper proposes a target positioning algorithm using asynchronous TDOA(Time Difference of Arrival) and FDOA(Frequency Difference of Arrival) measurements for a single target in a distributed sensor network. A conventional aigorithm, target localization through TDOA measurements cannot estimate a target's position when the number of TDOA measurements is not enough for localization at the same time. Our algorithm uses TDOA and EDOA measurements at the different time to compute the target's position and velocity estimates. Through computer simulation trials, the validity of our algorithm is confirmed.
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摘要 :
Most radar systems employ a feed-forward processing chain in which they first perform some low-level processing of received sensor data to obtain target detections and then pass the processed data on to some higher-level processor...
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Most radar systems employ a feed-forward processing chain in which they first perform some low-level processing of received sensor data to obtain target detections and then pass the processed data on to some higher-level processor such as a tracker, which extracts information to achieve a system objective. System performance can be improved using adaptation between the information extracted from the sensor/processor and the design and transmission of subsequent illuminating waveforms. As such, cognitive radar systems offer much promise. In this paper, we develop a general cognitive radar framework for a radar system engaged in target tracking. The model includes the higher-level tracking processor and specifies the feedback mechanism and optimization criterion used to obtain the next set of sensor data. Both target detection (track initiation/termination) and tracking (state estimation) are addressed. By separating the general principles from the specific application and implementation details, our formulation provides a flexible framework applicable to the general tracking problem. We demonstrate how the general framework may be specialized for a particular problem using a distributed sensor model in which system resources (observation time on each sensor) are allocated to optimize tracking performance. The cognitive radar system is shown to offer significant performance gains over a standard feed-forward system.
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When particle filter is applied in radar target tracking, the accuracy of the initial particles greatly effects the results of filtering. For acquiring more accurate initial particles, a new method called “competition strategy al...
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When particle filter is applied in radar target tracking, the accuracy of the initial particles greatly effects the results of filtering. For acquiring more accurate initial particles, a new method called “competition strategy algorithm” is presented. In this method, initial measurements give birth to several particle groups around them, regularly. Each of the groups is tested several times, separately, in the beginning periods, and the group that has the most number of efficient particles is selected as the initial particles. For this method, sample initial particles selected are on the basis of several measurements instead of only one first measurement, which surely improves the accuracy of initial particles. The method sacrifices initialization time and computation cost for accuracy of initial particles. Results of simulation show that it greatly improves the accuracy of initial particles, which makes the effect of filtering much better.
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The detection and identification of Resident Space Objects (RSOs) from survey tracks requires robust and efficient orbit determination methods for the association of observations of the same RSO. Both Initial Orbit Determination (...
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The detection and identification of Resident Space Objects (RSOs) from survey tracks requires robust and efficient orbit determination methods for the association of observations of the same RSO. Both Initial Orbit Determination (IOD) and Orbit Determination (OD) methods perform the orbital estimation in which the association of tracks relies. The choice of proper IOD and OD methods is essential for the whole data association, since they are in charge of providing the estimation required to evaluate the figure of merit of the association. In this paper, we review the state of the art and propose a novel method that does not require initialisation, accounts for measurement noise and provides a full estimation (i.e., state vector and covariance) from an arbitrary number of optical observations. To do so, a boundary value problem is formulated to find a pair of ranges leading to a minimum residuals of the observations. The proposed methods are compared against classical alternatives simulated in scenarios representative of the current space debris environment.
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