An optimal radar detector threshold adaptation for maneuvering targets in clutter

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.

A single-sensor, single-target mixture reduction (MR) data association algorithm is extended for use in multisensor, multitarget tracking situations. MR is extended for tracking an arbitrary number of targets using an arbitrary number of sensors under the assumption that the sensor measurement errors are independent across sensors. Like the single-sensor, single-target MR algorithm, which gives better performance than the probabilistic data association (PDA) filter, the multisensor, multitarget MR extensions give similar improvements in performance compared to the joint PDA (JPDA) and multisensor JPDA (MSJPDA) algorithms. Further, in the formulations for the multisensor and multitarget MR algorithms, the equations for the calculation of the data association probabilities have been put in the same form as for the JPDA, thus allowing previously developed fast JPDA computational techniques to be applicable. N the problem of tracking targets in random clutter, the optimal Bayesian solution leads to Gaussian mixture distributions that consist of an exponentially increasing number of components.1 In practice, the number of components in each mixture is kept in check by approximating it with fewer components. The nearest neighbor (NN) approximation reduces each target's mixture to its largest component.13 The joint probabilistic data association (JPDA) approximation merges the components of each target's mixture into a single Gaussian component.1'4 However, when there are several significant, well-spaced components in the original mixture, important information may be lost by using the NN or JPDA methods. A more flexible approximation method for single-sensor, single-target tracking, known as mixture reduction (MR), has been proposed by Salmond.5 This MR algorithm preserves the mean and covariance of the original mixture of components, successively merging components until the number of components is reduced to some userspecified limit. In this paper, we first review Salmond's MR algorithm for single-sensor tracking of a single target in clutter. We then derive extensions to Salmond's MR algorithm for use in tracking multiple targets and when there are measurements available from multiple sensors. Just as the single-sensor, single-target MR algorithm gives better performance5 than the probabilistic data association (PDA) filter, the multisensor, multitarget MR extensions are shown to give similar improvements in performance as compared to the JPDA and multisensor JPDA (MSJPDA)6 algorithms. Extension of the MR algorithm for tracking of multiple targets is necessary for use in realistic tracking scenarios where there are usually many targets that need to be tracked. Moreover, many current tracking systems have been designed with multiple sensors, thus requiring the further extension to a multisensor algorithm. The paper is organized as follows. In Sec. II, the target dynamics and measurement equations are defined. We review the singlesensor, single-target MR algorithm5 in Sec. Ill, and we extend the algorithm to the multitarget and multisensor cases in Sec. IV and V, respectively. In Sec. VI, simulation results evaluating the performances of the MR extensions are presented. Finally, concluding remarks are given in Sec. VII.

The well-known probabilistic data association (PDA) filter handles the uncertainty in measurement origins inherent in tracking-in-clutter problems by using a probabilistically weighted sum of all measurements in the gate. In fact, the measurements in the gate may or may not include the one originated from the target. As such, two hypothetical models can be set up, corresponding to the events that the target measurements is and is not in the gate, respectively. This paper present an approach that integrates the PDA filter with the multiple-order method in a coherent manner based on the use of the above two hypothetical models. It is shown theoretically that the standard PDA filter is a special case of the first-order Generalized Pseudo Bayesian algorithm in the proposed formulation using a particular set of model transition probabilities. It is then proposed to adopt the superior interacting multiple-model architecture in this new formulation to improve the performance. The new algorithm is capable of achieving better performance by tuning the transition probabilities at a computational complexity comparable to that of the PDA filter. Simulation results are provided.

Object-Oriented Environment for Assessing Tracking Algorithms

This paper presents a measurement preprocessing based variable dimension probabilistic data association filter (PDAF) for tracking a single maneuvering target in clutter. The measurement preprocessing scheme is derived from a maximum likelihood method and utilizes validated measurements for yielding target's estimated accelerations and velocities which lead to preprocessed measurements. The proposed preprocessing method is integrated into a variable dimension PDAF which uses preprocessed measurements as its inputs. Simulation results show that the proposed tracking algorithm can achieve improved performance.

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.

In tracking a single target in clutter, many algorithms have been developed ranging in complexity from nearest neighbor (NN) and probabilistic data association (PDA) to the optimal Bayesian filter. In multiple-target tracking, a number of the techniques have been exercised such as the JPDA and the multiple hypothesis (MHT) schemes. Sub-optimal algorithms, such as the PDA filter, have been used widely since the optimal algorithms have an exponentially increasing computational complexity since all the possible sequences of target-to-measurement associations must be considered. In this paper, the Viterbi algorithm (VA) is used to develop a parallel search data association algorithm, called the Viterbi Data Association (VDA) technique. This algorithm includes the gating, automatic track initiation and termination modules. Simulations have been carried out to verify the performance and the robustness of the proposed algorithms Moreover, the VDA algorithm is compared with the fuzzy data association (FDA) algorithm when tracking a target in a cluttered, low signal-to-noise ratio (SNR) environment.

Square-root formulations have not been developed for state-of-the-art tracking algorithms. The only exception is the square-root implementation of the PDAF by R. Kenefic (1990). It is shown that there is a substantially better implementation of the square-root probabilistic data association filter (PDAF) than Kenefic's algorithm in terms of both numerical robustness and computational efficiency. On the average, the proposed algorithm requires half the number of computations required by Kenefic's algorithm for square-root PDAF. The same approach is extended to develop computationally efficient square-root algorithms for the interesting multiple model (IMM) and IMM probabilistic data association (IMMPDA) filters. >

Vision sensors are widely used not only for detection and recognition, but also measurement of the pose of the moving objects. But in the crowded environment, occlusion often disrupts the measurement, and wrong data association due to misrecognition deteriorates the tracking performance. Probabilistic data association filter (PDAF) is known as useful to address such issues, in which observed features are weighted by probability to deal with multiple observations as well as to cope with occlusion and false recognition. This paper presents a novel object tracking method in which PDAF is incorporated into moving horizon estimation (MHE) framework to deal with multiple frame tracking and physical constraints. The performance of the proposed method is evaluated by comparing with the PDAF.

This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms.

By integrating the cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filter with the interacting multiple models (IMM) algorithm, an MM-CBMeMBer filter is proposed in this paper for tracking multiple maneuvering targets in clutter. The sequential Monte Carlo (SMC) method is used to implement the filter for generic multi-target models and the Gaussian mixture (GM) method is used to implement the filter for linear-Gaussian multi-target models. Then, the extended Kalman (EK) and unscented Kalman filtering approximations for the GM-MM-CBMeMBer filter to accommodate mildly nonlinear models are described briefly. Simulation results are presented to show the effectiveness of the proposed filter.

Hybrid fuzzy probabilistic data association filter and joint probabilistic data association filter

Object tracking is widely utilized and becomes indispensable in automation technology. In environments containing many objects, however, occlusion and false recognition frequently occur. To alleviate these issues, in this paper, we propose a novel object tracking method based on moving horizon estimation incorporating probabilistic data association (MHE-PDA) through a probabilistic data association filter (PDAF). Since moving horizon estimation (MHE) is accomplished through numerical optimization, we can ensure that the estimation is consistent with physical constraints and robust to outliers. The robustness of the proposed method against occlusion and false recognition is verified by comparison with PDAF through simulations of a cluttered environment.

A methodology for the use of the multirate sampling technique in the measurement acquisition process of a probabilistic data association (PDA) filter is presented. The proposed filter, called the multirate sample probabilistic data association (MSPDA) filter, increases the computational efficiency of the tracking system without compromising the accuracy of the tracking algorithm. The use of multirate sampling enhances the utilization of the central processor, since the time spent in acquiring and processing certain measurements relevant to the components of a state vector is considerably saved. The performance of the MSPDA filter is compared with that of the traditional PDA tracker and other presently used filters. It is shown that the computational saving is about 25% while the degradation in accuracy is only to the extent of 5% of the MSE of the corresponding state vector component. >

Robust tracking of a target in a clutter environment is an important and challenging task. In recent years, the nearest neighbor methods and probabilistic data association filters were proposed. However, the performance of these methods diminishes as number of measurements increases. In this paper, we propose a robust generative approach to effectively model multiple sensor measurements for tracking a moving target in an environment with high clutter. We assume a time-dependent number of measurements that include sensor observations with unknown origin, some of which may only contain clutter with no additional information. We robustly and accurately estimate the trajectory of the moving target in high clutter environment with unknown number of clutters by employing Bayesian nonparametric modeling. In particular, we employ a class of joint Bayesian nonparametric models to construct the joint prior distribution of target and clutter measurements such that the conditional distributions follow a Dirichlet process. The marginalized Dirichlet process prior of the target measurements is then used in a Bayesian tracker to estimate the dynamically-varying target state. We show through experiments that the tracking performance and effectiveness of our proposed framework are increased by suppressing high clutter measurements. In addition, we show that our proposed method outperforms existing methods such as nearest neighbor and probability data association filters.