Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering

In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed to address bearings-only measurements in multi-target tracking. The proposed method, called the Gaussian mixture measurements-probability hypothesis density (GMM-PHD) filter, not only approximates the posterior intensity using a Gaussian mixture, but also models the likelihood function with a Gaussian mixture instead of a single Gaussian distribution. Besides, the target birth model of the GMM-PHD filter is assumed to be partially uniform instead of a Gaussian mixture. Simulation results show that the proposed filter outperforms the GM-PHD filter embedded with the extended Kalman filter (EKF) and the unscented Kalman filter (UKF)

Target Tracking in Clutter with Extremum Seeking Control for Adaptive Detection Thresholding

If the signal strength obtained from sonar is higher than the predefined detection threshold, it is considered as a candidate for target tracking. This detection threshold is a parameter that affects the detection probability of targets and the distribution of clutter measurements, so it is important to determine a proper threshold to improve target tracking performance. There are various techniques for adjusting the detection threshold. To apply these techniques, it is assumed that the probability density functions of the signal strength for clutter are known in advance. However, in a real environment, the probability density function of the signal strength is unknown in general. In this paper, we propose a detection threshold control method using extremum seeking control in realistic environments. The extremum seeking control is a method used in complex nonlinear systems. We propose a new structure for extremum seeking control that is applicable to detection processes with nonlinear characteristics. This structure is used to adjust the detection threshold of the received signal amplitude to make the estimated clutter measurement density converge to a designed clutter measurement density to achieve the best target tracking performance in the current environment. Simulation studies for the proposed extremum seeking control applied to target tracking in an unknown clutter signal distribution demonstrate the effectiveness and improved target tracking performance

An Efficient Multi-Path Multitarget Tracking Algorithm for Over-The-Horizon Radar

In target tracking environments using over-the-horizon radar (OTHR), one target may generate multiple detections through different signal propagation paths. Trackers need to jointly handle the uncertainties stemming from both measurement origin and measurement path. Traditional multitarget tracking algorithms suffer from high computational loads in such environments since they need to enumerate all possible joint measurement-to-track assignments considering the measurements paths unless they employ some approximations regarding the measurements and their corresponding paths. In this paper, we propose a novel algorithm, named multi-path linear multitarget integrated probabilistic data association (MP-LM-IPDA), to efficiently track multitarget in multiple detection environments. Instead of generating all possible joint assignments, MP-LM-IPDA calculates the modulated clutter measurement density for each measurement cell of each track. The modulated clutter measurement density considers the possibility that the measurement cells originate from the clutter as well as from other potential targets. By incorporating the modulated clutter measurement density, the single target tracking structure can be applied for multitarget tracking, which significantly reduces the computational load. The simulation results demonstrate the effectiveness and efficiency of the proposed algorithm

Markov Chain Realization of Multiple Detection Joint Integrated Probabilistic Data Association

In multiple detection target tracking environments, PDA-based algorithms such as multiple detection joint integrated probabilistic data association (MD-JIPDA) utilize the measurement partition method to generate measurement cells. Thus, one-to-many track-to-measurements associations can be realized. However, in this structure, the number of joint data association events grows exponentially with the number of measurement cells and the number of tracks. MD-JIPDA is plagued by large increases in computational complexity when targets are closely spaced or move cross each other, especially in multiple detection scenarios. Here, the multiple detection Markov chain joint integrated probabilistic data association (MD-MC-JIPDA) is proposed, in which a Markov chain is used to generate random data association sequences. These sequences are substitutes for the association events. The Markov chain process significantly reduces the computational cost since only a few association sequences are generated while keeping preferable tracking performance. Finally, MD-MC-JIPDA is experimentally validated to demonstrate its effectiveness compared with some of the existing multiple detection data association algorithms

Markov Chain Realization of Joint Integrated Probabilistic Data Association

A practical probabilistic data association filter is proposed for tracking multiple targets in clutter. The number of joint data association events increases combinatorially with the number of measurements and the number of targets, which may become computationally impractical for even small numbers of closely located targets in real target-tracking applications in heavily cluttered environments. In this paper, a Markov chain model is proposed to generate a set of feasible joint events (FJEs) for multiple target tracking that is used to approximate the multi-target data association probabilities and the probabilities of target existence of joint integrated probabilistic data association (JIPDA). A Markov chain with the transition probabilities obtained from the integrated probabilistic data association (IPDA) for single-target tracking is designed to generate a random sequence composed of the predetermined number of FJEs without incurring additional computational cost. The FJEs generated are adjusted for the multi-target tracking environment. A computationally tractable set of these random sequences is utilized to evaluate the track-to-measurement association probabilities such that the computational burden is substantially reduced compared to the JIPDA algorithm. By a series of simulations, the track confirmation rates and target retention statistics of the proposed algorithm are compared with the other existing algorithms including JIPDA to show the effectiveness of the proposed algorithm

Korea

Abstract- The measurement with the strongest signal amplitude in the validation gate is known as the strongest neighbor (SN) measurement. A standard Kalman filter that utilizes the SN at any time as if it is originated from the true target is called the strongest neighbor filter(SNF). Inconsistency of handling the SN as if it is true target is corrected in the existing probabilistic strongest neighbor filter(PSNF) which accounts the probability that the SN is from the true target. It is known that performance of the PSNF is superior to the SNF at a cost of increased computational load. In this paper, we propose a new probabilistic strongest neighbor filter that takes into account the current number of validated measurements in the derivation of probability density functions for the SN which are needed to establish probability weightings and estimation error covariance. The proposed algorithm does not involve infinite summation while the existing PSNF algorithm contains infinite summation that requires approximation for practical usage. Performance of the proposed filter is compared with the existing filters such as the SNF and the PSNF through a series of Monte Carlo simulation runs for aerial target tracking in clutter. The advantages of the new filter in practical applications are studied via analysis and simulation. Keywords: SNF, PSNF, PSNF-m, data association, target tracking, clutter, performance analysis

Comparison of Ballistic-Coefficient-Based Estimation Algorithms for Precise Tracking of a Re-Entry Vehicle and its Impact Point Prediction

This paper studies the problem of tracking a re-entry vehicle (RV) in order to predict its impact point on the ground. Re-entry target dynamics combined with super-high speed has a complex non-linearity due to ballistic coefficient variations. However, it is difficult to construct a database for the ballistic coefficient of a unknown vehicle for a wide range of variations, thus the reliability of target tracking performance cannot be guaranteed if accurate ballistic coefficient estimation is not achieved. Various techniques for ballistic coefficient estimation have been previously proposed, but limitations exist for the estimation of non-linear parts accurately without obtaining prior information. In this paper we propose the ballistic coefficient β model-based interacting multiple model-extended Kalman filter (β-IMM-EKF) for precise tracking of an RV. To evaluate the performance, other ballistic coefficient model based filters, which are gamma augmented filter, gamma bootstrapped filter were compared and assessed with the proposed β-IMM-EKF for precise tracking of an RV

Smoothing Linear Multi-Target Tracking Using Integrated Track Splitting Filter

Multi-target tracking (MTT) is a challenging issue due to an unknown number of real targets, motion uncertainties, and coalescence behavior of sensor (such as radar) measurements. The conventional MTT systems deal with intractable computational complexities because they enumerate all feasible joint measurement-to-track association hypotheses and recursively calculate the a posteriori probabilities of each of these joint hypotheses. Therefore, the state-of-art MTT system demands bypassing the entire joint data association procedure. This research work utilizes linear multi-target (LM) tracking to treat feasible target detections followed by neighbored tracks as clutters. The LM integrated track splitting (LMITS) algorithm was developed without a smoothing application that produces substantial estimation errors. Smoothing refines the state estimation in order to reduce estimation errors for an efficient MTT. Therefore, we propose a novel Fixed Interval Smoothing LMITS (FIsLMITS) algorithm in the existing LMITS algorithm framework to improve MTT performance. This algorithm initializes forward and backward tracks employing LMITS separately using measurements collected from the sensor in each scan. The forward track recursion starts after the smoothing. Therefore, each forward track acquires backward multi-tracks that arrived from upcoming scans (future scans) while simultaneously associating them in a forward track for fusion and smoothing. Thus, forward tracks become more reliable for multi-target state estimation in difficult cluttered environments. Monte Carlo simulations are carried out to demonstrate FIsLMITS with improved state estimation accuracy and false track discrimination (FTD) in comparison to the existing MTT algorithms