Multiple-Model Cardinality Balanced Multitarget Multi-Bernoulli Filter for Tracking Maneuvering Targets

Tracking targets in clutter, with the inherent data association problem, naturally leads to a Gaussian mixture representation of the probability density function (pdf) of the target state vector, conditioned on the measurements observed. Online trackers require reduction of the number of components in the mixture on each processing cycle, and the integral square error (ISE) based mixture reduction algorithm (MRA) significantly outperforms known alternative algorithms. Moreover, to handle target maneuver onset and changing trajectory characteristics, one can use multiple model adaptive estimation in the form of either multiple model adaptive estimation (MMAE) or interacting multiple model (IMM) algorithms. For maneuvering targets in clutter, one can replace each Kalman filter within a conventional MMAE or IMM with an ISE-based MRA, or better yet, replace each Kalman filter within an ISE-based algorithm with an MMAE or IMM, to yield superior tracking of aggressive maneuvers in deep clutter. Such an ISE-based algorithm of MMAEs is seen to have performance attributes significantly superior to that of a current state-of-the-art tracker.

This paper extends the smoothing algorithm based on integrated probabilistic data association to track multiple maneuvering targets by applying smoothing to joint integrated probabilistic data association (JIPDA). The proposed algorithm utilizes smoothing data association to obtain smoothing prediction which is needed to calculate the smoothing data association probabilities, the smoothing target trajectory state estimates and the smoothing target existence probability. The smoothing data association probabilities are used to update and propagate the forward tracks for tracking multiple maneuvering targets in clutter. This algorithm is called fixed-interval smoothing JIPDA (JIPDAS). Simulation is carried out to show improved false track discrimination performance over the existing algorithms for tracking multiple maneuvering targets in a heavy cluttered environment.

Adaptively modeling the target birth intensity while maintaining the filtering efficiency is a challenging issue in multi-target tracking (MTT). Generally, the target birth probability is predefined as a constant and only the target birth density is considered in existing adaptive birth models, resulting in deteriorated target tracking accuracy, especially in the target appearing cases. In addition, the existing adaptive birth models also give rise to a decline in operation efficiency on account of the extra birth modeling calculations. To properly adapt the real variation of the number of newborn targets and improve the multi-target tracking performance, a novel fast sequential Monte Carlo (SMC) adaptive target birth intensity cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter is proposed in this paper. Through adaptively conducting the target birth probability in a pre-processing step, which incorporates the information of current measurements to correct the pre-setting of the target birth probability, the proposed filter can truly adapt target birth cases and achieve better tracking accuracy. Moreover, the implementation efficiency can be improved significantly by employing a measurement noise-based threshold in the likelihood calculations of the multi-target updating. Simulation results verify the effectiveness of the proposed filter.

Multi-Bernoulli smoother for multi-target tracking

The interacting multiple model (IMM) algorithm is a widely accepted state estimation scheme for solving maneuvering target tracking problems, which are generally nonlinear. During the IMM filtering process, serious errors can arise when a Gaussian mixture of posterior probability density functions is approximated by a single Gaussian. Particle filters (PFs) are effective in dealing with nonlinearity and non-Gaussianity. This work considers an IMM algorithm that includes a constant velocity model, a constant acceleration model and a 3D turning rate (3DTR) model for tracking three-dimensional (3D) target motion, using various combinations of nonlinear filters. In existing literature on combining IMM and particle filtering techniques to tackle difficult target maneuvers, a PF is usually used in every model In comparison, simulation results show that by using a computationally economical PF in the 3DTR model and Kalman filters in the remaining models, superior performance can be achieved with significant reduction in computational costs.

In this paper, we consider the problem of radar detector threshold optimization for maneuvering targets in clutter. In the earlier works, the problem was studied in the context of the probabilistic data association filter (PDAF) for non-maneuvering targets. In this study, we have extended the ideas, which were applied to the PDAF, to the interacting multiple model PDAF (IMM-PDAF) for maneuvering targets. The proposed optimization problem and its solution show better results over the traditional approaches in terms of track loss percentage and RMS position error criteria.

Data assimilation is the process of estimating the state of dynamic systems (linear or nonlinear, Gaussian or non-Gaussian) as accurately as possible from noisy observational data. Although the Three Dimensional Variational (3D-VAR) methods, Four Dimensional Variational (4D-VAR) methods and Ensemble Kalman filter (EnKF) methods are widely used and effective for linear and Gaussian dynamics, new methods of data assimilation are required for the general situation, that is, nonlinear non-Gaussian dynamics. General Bayesian recursive estimation theory is reviewed in this thesis. The Bayesian estimation approach provides a rather general and powerful framework for handling nonlinear, non-Gaussian, as well as linear, Gaussian estimation problems. Despite a general solution to the nonlinear estimation problem, there is no closed-form solution in the general case. Therefore, approximate techniques have to be employed. In this thesis, the sequential Monte Carlo (SMC) methods, commonly referred to as the particle filter, is presented to tackle non-linear, non-Gaussian estimation problems. In this thesis, we use the SMC methods only for the nonlinear state estimation problem, however, it can also be used for the nonlinear parameter estimation problem. In order to demonstrate the new methods in the general nonlinear non-Gaussian case, we compare Sequential Monte Carlo (SMC) methods with the Ensemble Kalman Filter (EnKF) by performing data assimilation in nonlinear and non-Gaussian dynamic systems. The models used in this study are referred to as state-space models. The Lorenz 1963 and 1966 models serve as test beds for examining the properties of these assimilation methods when used in highly nonlinear dynamics. The application of Sequential Monte Carlo methods to different fixed parameters in dynamic models is considered. Four different scenarios in the Lorenz 1063 [sic] model and three different scenarios in the Lorenz 1996 model are designed in this study for both the SMC methods and EnKF method with different filter siz

Long time hybrid integration of radar rotating target

Appropriately modelling target-birth intensity is a significant but challenging issue in multi-target tracking systems. In existing cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filters, a priori knowledge about the locations where targets appear is required to model the target-birth intensity. Since the newborn targets can appear anywhere within the observation region, it is impractical to obtain such prior information. In this study, a novel CBMeMBer filter with adaptive target-birth intensity is presented, considering the newborn and surviving targets separately. The target-birth function of the target-birth intensity is modelled using current measurements rather than the known birth locations, and the target-birth magnitude is assigned by an allocation function rather than equally assigned. The new CBMeMBer filter can remove the restriction on the requirement of prior birth location information and can adapt well after continuous missing detection occurs. Simulations of the sequential Monte Carlo and Gaussian mixture implementations demonstrate the effectiveness of the proposed filter.

The regular and repetitive nature of the sequential Monte Carlo (SMC) method makes it very attractive for implementation using parallel and pipelined architectures. This paper develops a VLSI architecture for the hardware implementation of the SMC algorithm using bootstrap filter. A flat fading wireless channel is considered as our framework on which the channel estimator is designed and implemented using the SMC method. The design and verification activities at the algorithm level, architecture level, and the circuit level are reviewed. The proposed architecture is verified with an FPGA implementation.

Variable Structure Multiple Model (VSMM) estimation generalizes Multiple Model (MM) estimation by assuming that the set of models used for MM estimation is time varying. By using VSMM estimation, a large model set which may cover all possible target maneuvers can be used without significant increase of computational load, while maintaining a reasonable estimation accuracy. Various implementable VSMM algorithms, like Model Group Switching (MGS), Likely Model Set (LMS) and Minimal Sub-Model-Set Switching (MSMSS) using the Interacting Multiple Model (IMM) algorithm with a sub-model-set adaptation logic have appeared in the recent literature. However, the use of these algorithms for tracking maneuvering target in clutter has not been explored. In presence of clutter, one need to use data association technique to differentiate target originated measurement from clutter. The probabilistic data association (PDA) has been popularly adopted to many algorithms for tracking in clutter. In this paper, we integrate PDA technique with MSMSS and propose a VSIMM-PDA algorithm for maneuvering target tracking in clutter. A new grating technique to account for potential model errors is used. A numeric example via multiple Monte Carlo runs, which compares the performance of the new algorithm to a standard IMM-PDA in terms of Root Mean Squared error (RMS) and percentage of track loss, is presented.© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In statistical signal processing, the sequential Monte Carlo (SMC) method is powerful and can approach the theoretical optima. However, its computational complexity is usually very high, especially in multiple-input multiple-output (MIMO) systems. This paper presents a new low-complexity SMC (LC-SMC) algorithm for blind detection in MIMO systems, the main idea of which is to shrink the sampling space via channel estimation which is initialized using the first differentially modulated symbol and then updated using the Monte Carlo samples. Since the a posteriori probability of the transmitted symbols can be calculated separately by each transmit antenna, the proposed LC-SMC algorithm is not only computationally efficient, as compared to the original SMC whose complexity grows exponentially with the number of transmit antennas, but also makes blind turbo receiver more feasible for multilevel/phase modulations. Simulation results are presented to demonstrate the effectiveness of the LC-SMC algorithm

Recently, targets tracking plays an important role in the rapidly expanding field of autonomous driving. With the presentation of vehicle-to-vehicle (V2V) communication standards and the improvement of Global Navigation Satellite System (GNSS) technology, the accuracy of cooperative vehicle tracking should be greatly improved. However, high-precision positioning suffers from a number of vulnerabilities and is unstable in clutter environment. In this paper, a modified approach using particle filter (PF) integrated with Interacting Multiple Models (IMM) algorithm is proposed to improve the accuracy of cooperative vehicle tracking in changing V2V environment. This paper focuses on a novel framework for cooperative vehicle tracking and implementation of data fusion originating from positioning information receiving from radars and connected vehicles. The simulations on scenarios of single vehicle and multi-vehicle tracking were implemented by utilizing Matlab. The performance of the proposed modified IMM-PF algorithm was evaluated. The results demonstrated that the proposed approach can adapt to a changing accuracy of positioning information and significantly reduce the tracking errors by approximately 50% compared with traditional methods. Consequently, the proposed method can effectively enhance the security and reliability of autonomous driver assistance systems, and its application has potential to support urban planning for smart cities.

We propose a sequential Monte Carlo (SMC) method to efficiently and accurately compute cut-Bayesian posterior quantities of interest, variations of standard Bayesian approaches constructed primarily to account for model misspecification. We prove finite sample concentration bounds for estimators derived from the proposed method along with a linear tempering extension and apply these results to a realistic setting where a computer model is misspecified. We then illustrate the SMC method for inference in a modular chemical reactor example that includes submodels for reaction kinetics, turbulence, mass transfer, and diffusion. The samples obtained are commensurate with a direct-sampling approach that consists of running multiple Markov chains, with computational efficiency gains using the SMC method. Overall, the SMC method presented yields a novel, rigorous approach to computing with cut-Bayesian posterior distributions.

This paper addresses the problem of tracking multiple pedestrians whose motion is dependent on one another. The behavior of a pedestrian may be often affected by the motion of other pedestrians, obstacles in the surrounding, and his/her intended destination. Hence, a motion modeling technique, which integrates the various factors that affect the motion of pedestrians, is needed. In this paper, a social force based motion model integrated into the probability hypothesis density (PHD) framework is proposed. The social force concept has previously been used to model pedestrian motion when there are interactions among pedestrians. In this paper, the sequential Monte Carlo (SMC) technique and the Gaussian mixture (GM) technique are used to implement the proposed Social Force PHD (SF-PHD) filter and its multiple model variant in pedestrian tracking scenarios. A particle labeling approach is used in the SMC technique while a Gaussian component labeling approach is used in the GM technique for this purpose. Also, a modified performance measure independent of the proposed approaches but based on the posterior Cramer–Rao lower bound for targets whose motion is dependent on one another is derived. Simulation and real data-based results show that both the SMC implementation and the GM implementation of the proposed SF-PHD filter outperform existing filters that assume independent motion among ground targets.