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A generic Bayesian framework is presented to track lost-in-space noncooperative maneuvering satellites. The developed framework predicts the reachability set for a lost-in-space satellite given bounds on maneuver parameters such as maneuver time and maneuver magnitude. Reachability sets are represented as a desired order polynomial series as a function of maneuver parameters. Recent advances in non-product quadrature methods are utilized to compute coefficients of this polynomial series in a computationally efficient manner. A major contribution of this work is to develop quadrature methods to generate samples for spherically uniform distribution for bounded magnitude maneuvers. Samples generated from this polynomial series are used for direct particle propagation in a traditional Bayesian filter rather than solving governing equations of motion for each sample point. An important component of the developed framework is a search strategy which exploits the reachability set calculations to task the sensor to increase the detection probability of the satellite. The samples generated from initial reachability sets are updated to systematically reduce the target search region based on actual detection of the target in a Bayesian framework. Numerical simulations are performed to show the efficacy of the developed ideas for tracking a lost-in-space satellite with the help of space based sensor. Performance of the proposed method varies widely based on factors such as the reachability set polynomial order, maneuver uncertainty bounds, sensor parameters (Field of view, measurement frequency, and detection probability), and initial conditions. For numerical experiments performed, the observer gained the custody of the maneuvering target in 100 % and 96 % of Monte Carlo (MC) simulations for the single maneuver and two maneuver cases, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a filtering algorithm for tracking strong maneuvering targets is proposed to improve the attack ability of air-to-air missiles in air combat. Firstly, by analyzing the filtering algorithm of strong maneuvering targets, a hybrid grid multiple model(HGMM)framework based on interacting multiple model algorithm is constructed. Then, the hybrid grid multiple model algorithm is designed in detail. Finally, numerical simulation shows that the proposed method is feasible and has good tracking effect, the tracking accuracy is improved abaut 20%.

Multiple radar systems represent an attractive option for target tracking because they can significantly enlarge the area coverage and improve both the probability of trajectory detection and the localization accuracy. The presence of multiple extended targets or weak targets is a challenge for multiple radar systems. Moreover, their performance may be severely deteriorated by regions characterized by a high clutter density. In this article, an algorithm for detection and tracking of multiple targets, extended or weak, based on measurements provided by multiple radars in an environment with heavily cluttered regions, is proposed. The proposed method features three stages. In the first stage, past measurements are exploited to build a spatiotemporal clutter map in each radar; a weight is then assigned to each measurement to assess its significance. In the second stage, a track-before-detect algorithm, based on a weighted 3-D Hough transform, is applied to obtain target tracklets. In the third stage, a low-complexity tracklet association method, exploiting a lion reproduction model, is applied to associate tracklets of the same target. Three experiments are presented to illustrate the effectiveness of the proposed approach. The first experiment is based on synthetic data, the second one is based on actual data from a radar network with two homogeneous air surveillance radars, and the third one is based on actual data from a radar network with four different marine surveillance radars. The results reveal that the proposed method can outperform competing approaches. [ABSTRACT FROM AUTHOR]

In this study, a coherent detection method is proposed to solve the problems during the coherent integration (CI) process, including the range cell migration (RCM) and Doppler spread (DS) effects which are caused by the complex motions of the maneuvering target. Particularly, this method applies the third-order keystone transform firstly, which could remove the third-order RCM induced by the jerk motion. Then target's defined velocity can be estimated by performing the frequency-shifting cross-correlation function and scaled Fourier transform. Thereafter, utilising the estimated defined velocity to construct a velocity compensation function, which could correct the first-order RCM (FRCM). Subsequently, the matched filtering process is performed to eliminate the second-order RCM and DS effects. Finally, applying the Fourier transform with respect to the slow time to realise the CI.

In this study, a coherent detection method is proposed to solve the problems during the coherent integration (CI) process, including the range cell migration (RCM) and Doppler spread (DS) effects which are caused by the complex motions of the maneuvering target. Particularly, this method applies the third-order keystone transform firstly, which could remove the third-order RCM induced by the jerk motion. Then target's defined velocity can be estimated by performing the frequency-shifting cross-correlation function and scaled Fourier transform. Thereafter, utilising the estimated defined velocity to construct a velocity compensation function, which could correct the first-order RCM (FRCM). Subsequently, the matched filtering process is performed to eliminate the second-order RCM and DS effects. Finally, applying the Fourier transform with respect to the slow time to realise the CI. [ABSTRACT FROM AUTHOR]

When a target is in complex three-dimensional (3-D) motions, inverse synthetic aperture radar (ISAR) images cannot be obtained with any motion compensation algorithms. In this case, selecting optimal coherent processing interval (CPI) (or frame) of the fixed rotational axis is the only way to obtain focused ISAR images. In this article, we focus on selecting optimal CPI for ISAR imaging of maneuvering targets, and a novel method using tracking information is proposed. Without complex computational procedures, the proposed method provides robust performance. The method includes three steps. First, target’s orientation (roll, pitch, and yaw) is estimated by using an extended Kalman filter. Second, the least-square method is adopted to find the best fitted lines of roll, pitch, and yaw angles in different time intervals, and the linearity degrees of the three rotation angles are measured over the imaging intervals. Third, the thresholds of the linearity degrees are set, according to which, optimal CPIs can be selected. The performance of proposed method is demonstrated using both simulated data and measured data. In both cases, optimal CPIs can be selected and well-focused ISAR images can be achieved. [ABSTRACT FROM AUTHOR]

Tracking the target that maneuvers at a variable turn rate is a challenging problem. The traditional solution for this problem is the use of the switching multiple models technique, which includes several dynamic models with different turn rates for matching the motion mode of the target at each point in time. However, the actual motion mode of a target at any time may be different from all of the dynamic models, because these models are usually limited. To address this problem, we establish a formula for estimating the turn rate of a maneuvering target. By applying the estimation method of the turn rate to the multi-target Bayes (MB) filter, we develop a MB filter with an adaptive estimation of the turn rate, in order to track multiple maneuvering targets. Simulation results indicate that the MB filter with an adaptive estimation of the turn rate, is better than the existing filter at tracking the target that maneuvers at a variable turn rate. [ABSTRACT FROM AUTHOR]

Tracking multiple maneuvering (MM) targets is a well-known and challenging problem because of clutter and several uncertainties existing in target motion mode, target detection, and data association. An efficient solution to this problem is the Gaussian mixture probability hypothesis density (GM-PHD) filter for jump Markov system (JMS) models. However, this solution is inapplicable to circumstances where detection probability is low because the GM-PHD filter for JMS models requires a high detection probability. To address this problem, we propose a sequential multiple target (MT) Bayes filter for JMS models. To track MM targets that are switching among a set of linear Gaussian models, an implementation process of this filter for linear Gaussian jump Markov MT models is also developed. The conclusion that the novel filter is more efficient for tracking MM targets than the existing filter for JMS models in circumstances of low detection probability is validated by simulation results. [ABSTRACT FROM AUTHOR]

This paper investigates the use of interval analysis to solve the problem of maneuvering target tracking, using range-only measures collected by a multistatic radar. The problem consists in one transmitter, and some receivers working together as a multistatic radar. The radar process is plagued by several uncertainty sources that affect directly the receivers' measures. As a result, target tracking can be both imprecise and unreliable. This study presents the tracking using an interval-based approach that computes the set of all feasible configurations for the target which are consistent with the measures. The algorithm is compared to a conventional tracking method: particle filtering. [ABSTRACT FROM AUTHOR]

In order to improve the performance of interacting multiple model (IMM) algorithm, a multi rate constant-velocity (MRCV) model and a current statistical (CS) model were introduced in IMM algorithm and an adaptive multi-rate interacting multiple model (AMRIMM) algorithm was developed. Monte Carlo simulations show that the AMRIMM algorithm with less computational amount significantly outperforms the standard IMM algorithm using constant-velocity (CV) and constant-acceleration (CA) models. [ABSTRACT FROM AUTHOR]

Networks of small, densely distributed wireless sensor nodes are being envisioned and developed for a variety of applications involving monitoring and the physical world in a tetherless fashion. Typically, each individual node can sense in multiple modalities but has limited communication and computation capabilities. Many challenges must be overcome before the concept of sensor networks In particular, there are two critical problems underlying successful operation of sensor networks: (1) efficient methods for exchanging information between the nodes and (2) collaborative signal processing (CSP) between the nodes to gather useful information about the physical world. This article describes the key ideas behind the CSP algorithms for distributed sensor networks being developed at the University of Wisconsin (UW). We also describe the basic ideas on how the CSP algorithms interface with the networking/routing algorithms being developed at Wisconsin (UW-API). We motivate the framework via the problem of detecting and tracking a single maneuvering target. This example illustrates the essential ideas behind the integration between UW-API and UW-CSP algorithms and also highlights the key aspects of detection and localization algorithms. We then build on these ideas to present our approach to tracking multiple targets that necessarily requires classification techniques becomes a reality [ABSTRACT FROM PUBLISHER]