Frequency modulated continuous wave (FMCW) radar have become common place in many roadside trac and
on board vehicle safety systems. The accuracy in these systems is based on the underlying calibration of these
sensors, which can be a time consuming and costly process. In our approach, using an uncalibrated commercial-
o-the-shelf (COTS) radar sensor, vehicles were monitored along a roadside. A moving target indication (MTI)
technique is used to reduce background clutter with thresholding and CFAR techniques used for signal detection.
These detections are fed into an extended Kalman lter, and using dierent association approaches, the results
are compared to GPS ground truth.
KEYWORDS: Motion estimation, Motion measurement, 3D acquisition, 3D metrology, Detection and tracking algorithms, Sensors, Kinematics, Monte Carlo methods, 3D modeling, Modeling
A framework of simultaneously estimating the motion and structure parameters of a 3D object by using high
range resolution (HRR) and ground moving target indicator (GMTI) measurements with template information
is given. By decoupling the motion and structure information and employing rigid-body constraints, we have
developed the kinematic and measurement equations of the problem. Since the kinematic system is unobservable
by using only one scan HRR and GMTI measurements, we designed an architecture to run the motion and
structure filters in parallel by using multi-scan measurements. Moreover, to improve the estimation accuracy
in large noise and/or false alarm environments, an interacting multi-template joint tracking (IMTJT) algorithm
is proposed. Simulation results have shown that the averaged root mean square errors for both motion and
structure state vectors have been significantly reduced by using the template information.
KEYWORDS: Signal to noise ratio, Performance modeling, Radar, Statistical modeling, Automatic target recognition, Data modeling, Principal component analysis, Feature extraction, Curium, Signal processing
Joint tracking and ATR with HRR radar is an important field of research in recent years. This paper addresses
the issue of end-to-end performance modeling for HRR radar based joint tracking and ATR system under various
operating conditions. To this end, an ATR system with peak location and amplitude as features is considered. A
complete set of models are developed to capture the statistics in all stages of processing, including HRR signal,
extracted features, Baysian classifier and tracker. In particular, we demonstrate that the effect of operating
conditions on feature can be represented through a random variable with Log-normal distribution. Then, the
result is extended to predicting the system performance under specified operating conditions.
Although this paper is developed based on a type of ATR and tracking system, the result indicates the trend of
the performance for general joint ATR and tracking system over operating conditions. It also provides guidance
to how the empirical performance model of a general joint tracking and ATR system shall be constructed.
This paper presents a novel methodology for target recognition and reconstruction of rigid body moving targets. Traditional methods such as Synthetic Aperture Radar (SAR) rely on information gathered from multiple sensor locations and complex processing algorithms. Additional processing is often required to mitigate the effects of motion and improve the image resolution. Many of these techniques rely on information external to the target such as target radar signatures and neglect information available from the structure of the target, structural invariance, and kinematics. This revolutionary target reconstruction method incorporates information not traditionally used. As a result, the absolute position of target scattering centers can theoretically be determined with external, high resolution radar range information from three observations of four target scattering centers. Relative motion between the target and the sensor and structural invariance provide additional information for determining position of the target's scattering center, actual scaling, and angular orientation with respect to the sensor for reconstruction and imaging.
This methodology is based on the kinematics of rotational motion resulting from relative movement between the sensor and the target. External range data provides one-dimensional information for determining position in a two-dimensional projection of the scattering centers. The range location of the scattering center, relative to a defined center, is analyzed using rotational motion. Range and target kinematics support the development of a conceptual model. Actual scaling and the target's orientation with respect to the sensor are developed through a series of trigonometric relationships. The resulting three-dimensional coordinates for the scattering centers are then used for target reconstruction and image enhancement.
In many cases, tracking ground targets can be formulated as a nonlinear filtering problem when terrain and road constraints are incorporated into system modeling and polar coordinate is used. Furthermore, when tracking ground maneuvering targets with an interacting multiple model (IMM) approach, a non-Gaussian problem exists due to an inherent mixing operation. A multirate interacting multiple model particle filter (MRIMM-PF) is presented in this paper to effectively solve the problem of nonlinear and non-Gaussian tracking, with an emphasis on computational savings.
Spatial reconstruction of a rigid, moving target's scattering centers using one dimensional, high range resolution (HRR) radar remains of high interest to synthetic aperture radar (SAR) processing of moving targets. Innovative range and Doppler equations for a rotating target with constant angular velocity were developed by Fazio, Hong, and Wood and presented at the April 2002 SPIE AeroSense Conference in Orlando, Florida. Further research has produced a method of reconstructing a three-dimensional scattering center model of a moving target with variable angular velocity. The reconstruction algorithm uses the relative ranges from a minimum of five observations of three scattering centers. In-plane rotational motion provides necessary information for positioning the projection of the scattering centers onto the observation (reconstruction) plane; while out-of-rotational-plane target motion is necessary to locate the center above or below the reconstruction plane.
Three-dimensional image reconstruction of moving targets from one-dimensional radar information traditionally has been challenging. Range and Doppler (range rate) measurements of prominent, radar scattering centers are rich with information for image reconstruction. Target kinematics and structural rigidity produce invariant parameters that support mathematical reconstruction solutions. Relating two frames of reference, one based on the radar source and the other on target motion, generate innovative range and Doppler equations. Solutions of these equations provide the basis for determining the three-dimensional location of scattering centers and target image reconstruction.
The paper develops a multirate filtering/smoothing approach for out-of-sequence (OOS) measurements. There are two major steps in OOS filtering/smoothing: retrospection from current time to OOS time (smoothing) and updating the current estimate with the OOS measurement (filtering), which imposes a high computation and memory burden on implementing OOS filtering. The multirate approach provides an excellent framework for efficient information retrospection and forward update. A multirate interacting multiple model (MRIMM) filter is developed to track a target with or without maneuvering behavior in an environment of our-of- sequence measurement reporting.
The goal of this paper is to demonstrate the benefits of a tracking and identification algorithm that uses a belief data association filter for target recognition. By associating track and ID information, the belief filter accumulates evidence for classifying High-Range Resolution (HRR) radar signatures from a moving target. A track history can be utilized to reduce the search space of targets for a given pose range. The technique follows the work of Mitchell and Westerkamp by processing HRR amplitude and location feature sets. The new aspect of the work is the identification of multiple moving targets of the same type. The conclusions from the work is that moving ATR from HRR signatures necessitates a track history for robust target ID.
In this paper, six trackers are reviewed and their performance is compared. Real radar target data are used for this study, where the data were collected from commercial and military aircrafts in various conditions. Since the true target trajectors are unavailable, the prediction RMS error is used as the performance criterion. The six trackers are compared in terms of their performance and computation complexity as well. Evaluated results shows that non-model trackers generally outperform model based trackers. A brief discussion is given.
Humans exhibit remarkable abilities to estimate, filter, predict, and fuse information in target tracking tasks, To improve track quality, we extend previous tracking approaches by investigating human cognitive-level fusion for constraining the set of plausible targets where the number of targets is not known a priori. The target track algorithm predicts a belief in the position and pose for a set of targets and an automatic target recognition algorithm uses the pose estimate to calculate an accumulated target-belief classification confidence measure. The human integrates the target track information and classification confidence measures to determine the number and identification of targets. This paper implements the cognitive belief filtering approach for sensor fusion and resolves target identity through a set-theory approach by determining a plausible set of targets being tracked.
A seeker is one of the most significant components in a missile guidance system. Because an advanced missile is required to operate in highly dynamic scenarios, the ability to handle uncertainties is important to the filter of a missile seeker. Sources for uncertainties are various. For instance, when a nonlinear system is linearized, the unmodeled high frequency dynamics could corrupt the filtering process when a large persistent estimation error accumulates. Facing the uncertainties, we propose to develop a robust filter for a seeker so that the uncertainties are precompensated in design. First, the uncertainty in a linear observation equation, which is the approximation of a nonlinear equation, is replaced by an equivalent noise process. The equivalent noise process is added to the nominal system and an auxiliary system is formed. A Kalman filter is designed for the auxiliary system so that an unbiased estimate of the system can be reached. Because the uncertainty in the original system is compensated, the filter for the auxiliary system is robust for the original system in the sensor that it provides an error bound for the nominal system with admissible uncertainties.
KEYWORDS: Detection and tracking algorithms, Personal digital assistants, Algorithm development, Sensors, Monte Carlo methods, Optical engineering, Data communications, Data modeling, Data fusion, Statistical analysis
This paper proposes a new algorithm which is able to decompose the measurements gathered within the same frame into multiple resolutions. The decomposition is based on the geometric relation between the measurements. The statistics of the decomposed measurements are derived with which multiresolutional models are constructed. The computation of the statistics is approximated so that it is efficient in computation. Simulations are designed to analyze the performance of the algorithm and to illustrate the significance of this multiresolutional approach. The results of the simulations prove that multiresolutional data and model structures can significantly reduce the computational burden of the tracking algorithms while the performance of a multiresolutional tracker is still comparable to that of a conventional one.
KEYWORDS: Detection and tracking algorithms, Sensors, Algorithm development, Data communications, Data modeling, Data fusion, Motion models, Electronic filtering, Systems modeling, Kinematics
KEYWORDS: Detection and tracking algorithms, Monte Carlo methods, Data modeling, Algorithm development, Systems modeling, Palladium, Target detection, Logic, Statistical analysis, Filtering (signal processing)
Bias phenomenon in multiple target tracking has been observed for some time. Beginning with a new view of a tracking algorithm structure, this paper is devoted to a study of the bias resulting from the miscorrelation in data association. The main result of this paper is a necessary condition for miscorrelation to cause bias. Relying on the main result, one new step is added to the tracking algorithm structure to compensate the bias generated by miscorrelation. A case study on the bias phenomenon in global nearest neighbor tracking is launched as a practice of the ideas and results presented in this paper. Tracking examples are given as an illustration. A discussion of several problems related to our results is given in the end of this paper.
A Multirate Interacting Multiple Model tracking algorithm is developed in this paper. The algorithm is based on a reformulation of the interacting multiple model (IMM) filter under the assumption that each model operates at an update rate proportional to the model's assumed dynamics. A wavelet transform is used to generate equivalent multirate measurements, which exhibit the additional property of lower equivalent measurement noise for low-rate data. Using this filtering approach performance virtually equivalent to a full IMM filter can be realized but with only a moderated increase in computational complexity over a single Kalman filter.
KEYWORDS: Detection and tracking algorithms, Data modeling, Wavelet transforms, Data processing, Optical filters, Data acquisition, Electronic filtering, Distance measurement, Signal processing, Discrete wavelet transforms
A framework of multiresolutional target tracking is established in this paper. The wavelet transform is employed in constructing multiresolutional data and model structures. Multiresolutional tracking is performed over the multiresolutional data and model structures in a top-down fashion. The main advantages of multiresolutional target tracking include: computational efficiency, performance robustness and algorithm flexibility.
A real-time multiresolutional approach for target tracking is developed in this paper. The wavelet transform is utilized to provide the multiresolutional measurements and bridge information at different resolutional levels. The approach is applied to tracking maneuvering targets and novel results are obtained.
KEYWORDS: 3D modeling, Optical spheres, Image fusion, Cameras, Information fusion, 3D image processing, 3D metrology, Sensor fusion, 3D vision, Systems modeling
The vision system described in this paper reconstructs 3-D scenes from sequences of noisy binocular images. First, the system establishes all possible matches between the feature pixels in the first binocular image pair and assigns a confidence value to a possible match. Because of finite resolution of cameras, each possible match is associated with a 3-D volume, instead of a 3-D point. The possible matches are used to predict projections of associated 3-D volumes onto the remaining binocular image pairs. These projections are utilized to limit searches for possible matches. The new matches are used to update confidence values using an optimal fusion algorithm. After fusion, matched pixels with high confidence values are considered as correct matches. The spatial uncertainty due to finite camera resolution is reduced by fusing the 3-D information provided by binocular image sequences.
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