Target Position Estimation Error Research Articles

Parameter Estimation for Uniformly Accelerating Moving Target in the Forward Scatter Radar Network

Passive radar based on the global navigation satellite positioning system (GNSS) has become the focus of attention in the field of radar. A parameter estimation method is proposed in the forward scatter radar (FSR) network based on GNSS to extend the application scenarios. For uniformly accelerating moving targets, only the instant times when the target crosses the individual baselines are used to retrieve the target motion parameters. The target position, velocity, and acceleration information can be obtained. Firstly, the minimum network configuration is derived theoretically. Then, the effects of crossing time error, station location error, transmitting/receiving station deployment, and target height on the accuracy are analyzed through Monte Carlo simulations. Finally, the simulation results indicate that the target position estimation error is in the order of 100 m. This paper provides the fundamental theory of aerial target positioning with a GNSS-based FSR network.

Validation of a CT-based motion model with in-situ fluoroscopy for lung surface deformation estimation

Many surrogate-based motion models (SMMs), proposed to guide motion management in radiotherapy, are constructed by correlating motion of an external surrogate and internal anatomy during CT-simulation. Changes in this correlation define model break down. We validate a methodology that incorporates fluoroscopic (FL) images acquired during treatment for SMM construction and update. Under a prospective IRB, 4DCT scans, VisionRT (VRT) surfaces, and orthogonal FLs were collected from five lung cancer patients. VRT surfaces and two FL time-series were acquired pre- and post-treatment. A simulated annealing optimization scheme was used to estimate optimal lung deformations by maximizing the mutual information (MI) between digitally reconstructed radiographs (DRRs) of the SMM-estimated 3D images and FLs. Our SMM used partial-least-regression and was trained using the optimal deformations and VRT surfaces from the first breathing-cycle. SMM performance was evaluated using the MI score between reference FLs and the corresponding SMM or phase-assigned 4DCT DRRs. The Hausdorff distance for contoured landmarks was used to evaluate target position estimation error. For four out of five patients, two principal components approximated lung surface deformations with submillimeter accuracy. Analysis of the MI score between more than 4000 pairs of FL and DRR demonstrated that our model led to more similarity between the FL and DRR images compared to 4DCT and DRR images from a model based on an a priori correlation model. Our SMM consistently displayed lower mean and 95th percentile Hausdorff distances. For one patient, 95th percentile Hausdorff distance was reduced by 11 mm. Patient-averaged reductions in mean and 95th percentile Hausdorff distances were 3.6 mm and 7 mm for right-lung, and 3.1 mm and 4 mm for left-lung targets. FL data were used to evaluate model performance and investigate the feasibility of model update. Despite variability in breathing, use of post-treatment FL preserved model fidelity and consistently outperformed 4DCT for position estimation.

Mixed Mode Radar Coincidence Imaging With Hybrid Excitation Radar Array

A novel mixed mode radar coincidence imaging (MMRCI) system, which combines the coherent radar detection and incoherent radar coincidence imaging function during the radar coincidence imaging (RCI) process, is presented in this paper. The target location function of the MMRCI system gives the target position information synchronously with imaging tests, and this makes the MMRCI work independently with low target position estimation error. In the MMRCI system, the resolution of target distance estimation is proportional to the whole bandwidth assisted by the double-frequency linear frequency modulation (DFLFM) signal when the occupied bandwidth of the DFLFM signal is less than one-tenth of the whole bandwidth. The target position estimation error caused by the target motion can be reduced effectively so that the image quality of the moving target can be guaranteed in a longer detection time. The RCI is reconstructed using the incoherent part of the transmitting signal. A general relationship between the spatial correlation of the random radiation field in the imaging plane and the deployments of linear random source is analyzed in the paper. The imaging efficiency is ensured through the grading imaging method based on the imaging requirement. The effectiveness of the MMRCI method is validated via a set of simulations and experiments, including superresolution RCI of both stationary and moving targets in various scenes using the convex optimization algorithms.

Respiratory motion compensation for CT-guided interventions in the liver

Computed tomography (CT) guided minimally invasive procedures in the liver, such as tumor biopsy and thermal ablation therapy, require precise targeting of hepatic structures that are subject to breathing motion. To facilitate needle placement, we introduced a navigation system which uses needle-shaped optically tracked navigation aids and a real-time deformation model to continuously estimate the position of a moving target. In this study, we assessed the target position estimation accuracy of our system in vitro with a custom-designed respiratory liver motion simulator. Several real-time compatible transformations were compared as a basis for the deformation model and were evaluated in a set of experiments using different arrangements of three navigation aids in two porcine and two human livers. Furthermore, we investigated different placement strategies for the case where only two needles are used for motion compensation. Depending on the transformation and the placement of the navigation aids, our system yielded a root mean square (RMS) target position estimation error in the range of 0.7 mm to 2.9 mm throughout the breathing cycle generated by the motion simulator. Affine transformations and spline transformations performed comparably well (overall RMS < 2 mm) and were considerably better than rigid transformations. When two navigation aids were used for motion compensation instead of three, a diagonal arrangement of the needles yielded the best results. This study suggests that our navigation system could significantly improve the clinical treatment standard for CT-guided interventions in the liver.

Respiratory motion compensation for CT-guided interventions in the liver

Computed tomography (CT) guided minimally invasive procedures in the liver, such as tumor biopsy and thermal ablation therapy, require precise targeting of hepatic structures that are subject to breathing motion. To facilitate needle placement, we introduced a navigation system which uses needle-shaped optically tracked navigation aids and a real-time deformation model to continuously estimate the position of a moving target.In this study, we assessed the target position estimation accuracy of our system in vitro with a custom-designed respiratory liver motion simulator. Several real-time compatible transformations were compared as a basis for the deformation model and were evaluated in a set of experiments using different arrangements of three navigation aids in two porcine and two human livers. Furthermore, we investigated different placement strategies for the case where only two needles are used for motion compensation.Depending on the transformation and the placement of the navigation aids, our system yielded a root mean square (RMS) target position estimation error in the range of 0.7 mm to 2.9 mm throughout the breathing cycle generated by the motion simulator. Affine transformations and spline transformations performed comparably well (overall RMS < 2 mm) and were considerably better than rigid transformations. When two navigation aids were used for motion compensation instead of three, a diagonal arrangement of the needles yielded the best results.This study suggests that our navigation system could significantly improve the clinical treatment standard for CT-guided interventions in the liver.