Maneuvering Target In Clutter Research Articles

A novel algorithm for tracking a maneuvering target in clutter

An algorithm for tracking a maneuvering target under measurement origin uncertainties is derived based on the approximation and propagation of the target state posterior distribution by combining Bayesian decision theory and suitable hypothesis merging procedures. Simulation results show that the main feature of this algorithm is its ability to significantly reduce the estimation error during non-maneuvering periods, making it quite suitable for tracking low maneuvering aircrafts. At the same time, the proposal is able to keep very low levels of track loss rate even for scenarios with high false alarm probability and trajectories with high degree of maneuverability. This proposal presents overall superiority when compared to the Interacting Multiple-Model with Probabilistic Data Filtering (IMMPDAF) solution, with an affordable increase in computational cost.

Tracking a Maneuvering Target in Clutter by Asynchronous and Heterogeneous Sensors on Single Airborne Platform

A new algorithm is proposed for tracking a maneuvering target in clutter using heterogeneous sensors, such as active and passive sensors, which are carried on single moving airborne platform and report their observations asynchronously. The algorithm combining interacting multiple model (IMM) with probabilistic data association filter (PDAF) is sequentially implemented to cope with asynchronous reports. In addition, in order to close to practice, the algorithm is based on Earth-centered Earth-fixed (ECEF) coordinate system while it considers the effect of the platform’s attitude. So the algorithm extends previous algorithms from synchronous case to asynchronous case, from fixed station to moving airborne platform, and from local Cartesian coordinates to general ECEF coordinates. The simulation results show that the proposed algorithm has a broader and more practical scope while being slightly worse than existing algorithm which only be applied under synchronous case.

Multisensor Tracking of a Maneuvering Target in Clutter with Proposed Parallel Updating Approach

To solve the problem of measurement original uncertainty, we present a proposed parallel updating approach for tracking a maneuvering target in cluttered environment using multiple sensors. A parallel updating method is followed where the raw sensor measurements are passed to a central processor and fed directly to the target tracker. A past approach using parallel sensor processing has ignored certain data association probabilities. Simulation results show that compared with an existing IMMPDAF algorithm with parallel sensor approach, the IMMPDAF algorithm with proposed parallel updating approach solves the problem of measurements' origins and achieves significant improvement in the accuracy of track estimation.

Tracking a maneuvering target in clutter with out-of-sequence measurements for airborne radar

Tracking a maneuvering target in clutter with out-of-sequence measurements for airborne radar

CuTE: curb tracking and estimation

The number of road accident related fatalities and damages are reduced substantially by improving road infrastructure and enacting and imposing laws. Further reduction is possible through embedding intelligence onto the vehicles for safe decision making. Road boundary information plays a major role in developing such intelligent vehicles. A prominent feature of roads in urban, semi-urban, and similar environments, is curbs on either side defining the road's boundary. In this brief, a novel methodology of tracking curbs is proposed. The problem of tracking a curb from a moving vehicle is formulated as tracking of a maneuvering target in clutter from a mobile platform using onboard sensors. A curb segment is presumed to be the maneuvering target, and is modeled as a nonlinear Markov switching process. The target's (curb's) orientation and location measurements are simultaneously obtained using a two-dimensional (2-D) scanning laser radar (LADAR) and a charge-coupled device (CCD) monocular camera, and are modeled as traditional base state observations. Camera images are also used to estimate the target's mode, which is modeled as a discrete-time point process. An effective curb tracking algorithm, known as Curb Tracking and Estimation (CuTE) using multiple modal sensor information is, thus, synthesized in an image enhanced interactive multiple model filtering framework. The use and fusion of camera vision and LADAR within this frame provide for efficient, effective, and robust tracking of curbs. Extensive experiments conducted in a campus road network demonstrate the viability, effectiveness, and robustness of the proposed method

A Bayesian EM algorithm for optimal tracking of a maneuvering target in clutter

The difficulty in tracking a maneuvering target in the presence of false measurements arises from the uncertain origin of the measurements (as a result of the observation/detection process) and the uncertainty in the maneuvering command driving the state of the target. Conditional mean estimates of the target state require a computational cost which is exponential with the number of observations and the levels of the maneuver command. In this paper, we propose an alternative optimal state estimation algorithm. Unlike the conditional mean estimator, which require computational cost exponential in the data length, the proposed iterative algorithm is linear in the data length (per iteration). The proposed iterative off-line algorithm optimally combines a hidden Markov model and a Kalman smoother—the optimality is demonstrated via the expectation maximization algor ithm—to yield the maximum a posteriori trajectory estimate of the target state. The algorithm proposed in this paper, uses probabilistic multi-hypothesis (PMHT) techniques for tracking a single maneuvering target in clutter. The extension of our algorithm to multiple maneuvering target tracking is straightforward and details are omitted. Previous applications of the PMHT technique (IEEE Trans. Automat. Control, submitted) have addressed the problem of tracking multiple non-maneuvering targets. These techniques are extended to address the problem of optimal tracking of a maneuvering target in a cluttered environment.

Application of a Monte Carlo method for tracking maneuvering target in clutter

The Monte Carlo methods provide a possibility for improved sub-optimal Bayesian estimation. In preceding studies the authors have suggested a new implementation of the general bootstrap simulation approach — the bootstrap multiple model (BMM) filter for tracking a maneuvering target. In the present paper this algorithm is further extended for operating in a cluttered environment. Probabilistic data association (PDA), taking into account the possible measurement-to-target association hypotheses, is incorporated into the BMM algorithm to overcome the measurement–origin uncertainty. By simulation the proposed BMM PDA algorithm is evaluated and compared with the well-known interacting multiple model (IMM) PDA filter. The obtained results demonstrate a superior tracking performance of the BMM PDA algorithm at the cost of an increase in computation.

A general characterization and simulation method for deposition and etching technology : Satoshi Tazawaet al. IEEE Trans. Semicond. mfg5, 27 (1992)

The difficulty in tracking a maneuvering target in the presence of false measurements arises from the uncertain origin of the measurements (as a result of the observation/detection process) and the uncertainty in the maneuvering command driving the state of the target. Conditional mean estimates of the target state require a computational cost which is exponential with the number of observations and the levels of the maneuver command.In this paper, we propose an alternative optimal state estimation algorithm. Unlike the conditional mean estimator, which require computational cost exponential in the data length, the proposed iterative algorithm is linear in the data length (per iteration). The proposed iterative off-line algorithm optimally combines a hidden Markov model and a Kalman smoother—the optimality is demonstrated via the expectation maximization algor ithm—to yield the maximum a posteriori trajectory estimate of the target state.The algorithm proposed in this paper, uses probabilistic multi-hypothesis (PMHT) techniques for tracking a single maneuvering target in clutter. The extension of our algorithm to multiple maneuvering target tracking is straightforward and details are omitted. Previous applications of the PMHT technique (IEEE Trans. Automat. Control, submitted) have addressed the problem of tracking multiple non-maneuvering targets. These techniques are extended to address the problem of optimal tracking of a maneuvering target in a cluttered environment.

An Algorithm for Tracking a Maneuvering Target in Clutter

We consider the problem of tracking a maneuvering target in clutter. In such an environment, missed detections and false alarms make it impossible to decide, with certainty, the origin of received echoes. Processing radar returns in cluttered environments consists of three functions: 1) target detection and plot formation, 2) plot-to-track association, and 3) track updating. Two inadequacies of the present approaches are 1) Optimization of detection characteristics have not been considered and 2) features that can be used in the plot-to-track correlation process are restricted to a specific class. This paper presents a new approach to overcome these limitations. This approach facilitates tracking of a maneuvering target in clutter and improves tracking performance for weak targets.

On Tracking a Maneuvering Target in Clutter

A new technique for tracking a maneuvering target in a cluttered environment is developed. This approach does not rely on a statistical description of the maneuver as a random process. Instead, the state model for the target is changed when a maneuver is detected. Undesired measurements due to clutter or false alarms are assumed to occur uniformly and independently distributed. The Probabilistic Data Association Filter is used for both state models of the target. Measurement sequences over a window are tested for occurrence of possible maneuver. In the process, tracks are formed, deleted and merged. Simulation has shown the effectiveness of the scheme.

Emendations to "An Algorithm for Tracking a Maneuvering Target in Clutter"

Emendations to "An Algorithm for Tracking a Maneuvering Target in Clutter"