Multitarget Tracking with Doppler Ambiguity

For operations in the Arctic, drift ice can be a major hazard. To be able to mitigate this, it is essential to know the position of viable threats. Many sensors can be employed, such as satellite Synthetic Aperture Radar (SAR), marine radar, air surveillance et cetera. At the core of the fusion of this sensor data is a Multi-Target Tracking (MTT) problem. This problem is studied in this paper through the implementation and application of the Multiple Hypothesis Tracking (MHT) algorithm. A major limiting factor in the application of multi-target tracking is scalability. A common method of handling the scaling is clustering, which separates the MHT filter into smaller independent parts. However, with growing scale, the association of sensor data to the “right” cluster can become resource intensive in itself. A method is explored, based on rectangular lower probability bounds, to efficiently index the clusters and compartmentalize the measurement update of the MHT. The method uses the bounding box of the lower probability bound of tracks and reports respectively, to perform an intersection lookup against the sensor field-of-view, efficiently selecting clusters of relevance. The method, as well as the MHT algorithm, has been implemented and published online under an open-source license. In this report, the implementation is described and tested on simulated data for statistics. Further, it is tested against data extracted from the polarimetric classification of ice using satellite imagery of the Arctic. Results show that computational speed improvements can be achieved, in comparison to the linear complexity of a naive search, but that comparable performance can be obtained using standard database lookups.

In this paper we describe a novel data association algorithm, termed m-best S-D, that determines in O(mSkn/sup 3/) time (m assignments, S/spl ges/3 lists of size n, k relaxations) the (approximately) m-best solutions to an S-D assignment problem. The m-best S-D algorithm is applicable to tracking problems where either the sensors are synchronized or the sensors and/or the targets are very slow moving. The significance of this work is that the m-best S-D assignment algorithm (in a sliding window mode) can provide for an efficient implementation of a suboptimal multiple hypothesis tracking (MHT) algorithm by obviating the need for a brute force enumeration of an exponential number of joint hypotheses. We first describe the general problem for which the m-best S-D applies. Specifically, given line of sight (LOS) (i.e., incomplete position) measurements from S sensors, sets of complete position measurements are extracted, namely, the 1st, 2nd, ..., mth best (in terms of likelihood) sets of composite measurements are determined by solving a static S-D assignment problem. Utilizing the joint likelihood functions used to determine the m best S-D assignment solutions, the composite measurements are then quantified with a probability of being correct using a JPDA-like (joint probabilistic data association) technique. Lists of composite measurements from successive scans, along with their corresponding probabilities, are used in turn with a state estimator in a dynamic 2-D assignment algorithm to estimate the states of moving targets over time. The dynamic assignment cost coefficients are based on a likelihood function that incorporates the "true" composite measurement probabilities obtained from the (static) m-best S-D assignment solutions. We demonstrate the merits of the m-best S-D algorithm by applying it to a simulated multitarget passive sensor track formation and maintenance problem, consisting of multiple time samples of LOS measurements originating from multiple (S=7) synchronized high frequency direction finding sensors.

The data fusion can come down to a process that combines the state vectors from different sources to obtain a more accurate result. Compare to the achieved results that depend on single source, the method has gained an improved performance and reduced the computational complexity and bandwidth of transmission as well. This paper makes use of Probabilistic Data Association (PDA) algorithm and Joint Probabilistic Data Association (JPDA) algorithm to track the Multi-target for each local sensor in a clutter environment. Furthermore, a method based on statistical double-threshold association algorithm and covariance-weighted fusion algorithm is proposed in this paper. Meanwhile, the simulation result shows that the performance has been improved significantly in multi-sensor and multi-target tracking progress with the proposed method in the paper.

Tracking body parts of multiple people in a video sequence is very useful for face/gesture recognition systems as well as human computer interaction (HCI) interfaces. This paper describes a framework for tracking multiple objects (e.g., hands and faces of multiple people) in a video stream. We use a probabilistic model to fuse the color and motion information to localize the body parts and employ a multiple hypothesis tracking (MHT) algorithm to track these features simultaneously. The MHT algorithm is capable of tracking multiple objects with limited occlusions and is suitable for resolving any data association uncertainty. We incorporated a path coherence function along with MHT to reduce the negative effects of spurious measurements that produce unconvincing tracks and needless computations. The performance of the framework has been validated using experiments on synthetic and real sequence of images.

In multi-target tracking, Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However, traditional MHT can not make full use of motion information. In this work, we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.

For the purpose of improving the performance of unscented Kalman filter (UKF) under the condition without accurate system noise statistics, this paper presents a new adaptive UKF based on the maximum likelihood principle. According to the maximum likelihood principle, the estimation of system noise statistics is determined by minimizing the negative of log likelihood function of innovation sequences. Subsequently, the estimated system noise statistics are fed back to the standard UKF to overcome its limitation. The proposed adaptive UKF can enhance the adaptive capability of standard UKF through the online estimation of system noise statistics. The effectiveness and advantage of the proposed algorithm are verified by the numerical simulations and comparison analysis.

A battery chemistry-adaptive fuel gauge using probabilistic data association

The Iterated Closest Contour Point (ICCP) algorithm is widely used in geomagnetic navigation. In order to enhance the anti-interference performance of the ICCP, an improved algorithm is proposed. First, the principle of delta modulation is introduced to generate a geomagnetic matching sequence according to the magnetic fluctuations, this assists finding the optimal quantitative step and matching length; thus, the algorithm's accuracy and real-time performance are improved. Second, in order to solve the problem of geomagnetic matching under an interference environment, a Probability Data Association (PDA) algorithm based on regenerated measurements is adopted. The ideal magnetic value is regarded as a target, and the measured values within the confidence region are taken as the effective measurements of the target. Each of them will give an estimation of the vehicle's position. Considering the constraints of a vehicle's kinematic performance, its final position can be obtained by fusing all effective estimations with the PDA algorithm. Simulation and semi-physical experiments have verified the feasibility and effectiveness of the proposed algorithm. The Regenerated Measurements (RM)-PDA algorithm shows better performance and can be used in practical applications.

In this paper, an algorithm for tracking multiple maneuvering targets by Multiple Hypothesis Tracking (MHT) with nonlinear non-Gaussian Kalman filter is investigated. The main challenges in multiple maneuvering targets tracking are the nonlinearity and non -Gaussianity problems. The Multiple Hypothesis Tracking (MHT) is used to detect the multiple targets in maneuverable and non-maneuverable modes. The computational requirements increase exponentially with number of tracks, the backscan depth and this can be reduced by careful design and tuning of MHT. The 1-backscan MHT algorithm is a good compromise between the two conflicting requirements of good tracking performance and limitation of computation time. The nonlinear non-Gaussian Kalman filter is used to track the target with high maneuver rate. The nonlinear non-Gaussian Kalman filter is implemented in MHT to give less probability of missing the target. The 1-backscan MHT with nonlinear non-Gaussian Kalman filter is free from computational burden by using simple probability concepts. This method of tracking also shows the reduction in the overshoot of root mean square error (RMSE).

This paper presents a method to track multiple moving humans using Ultra-Wideband (UWB) radar. UWB radar can complement other human tracking technologies, as it works well in poor visibility conditions. Our tracking approach is based on a point process interpretation of the multi-path UWB radar scattering model for moving humans. Based on this model, we present a multiple hypothesis tracking (MHT) framework for tracking the ranges and velocities of a variable number of moving human targets. The multi-target tracking (MTT) problem for UWB radar differs from traditional applications because of the complex multipath scattering observations per target. We develop an MHT framework for UWB radar-based multiple human target tracking, which can simultaneously solve the complex observation clustering and data association problems using Bayesian inference. We present experimental results in which a monostatic UWB radar tracks both individual and multiple human targets to estimate target ranges and velocities, even with changing numbers of targets across radar scans.

To date, Wald sequential probability ratio test (WSPRT) has been widely applied to track management of multiple hypothesis tracking (MHT). But in a real situation, if the false alarm spatial density is much larger than the new target spatial density, the original track score will be very close to the deletion threshold of the WSPRT. Consequently, all tracks, including target tracks, may easily be deleted, which means that the tracking performance is sensitive to the tracking environment. Meanwhile, if a target exists for a long time, its track will have a high score, which will make the track survive for a long time even after the target has disappeared. In this paper, to consider the relationship between the hypotheses of the test, we adopt the Shiryayev SPRT (SSPRT) for track management in MHT. By introducing a hypothesis transition probability, the original track score can increase faster, which solves the first problem. In addition, by setting an independent SSPRT for track deletion, the track score can decrease faster, which solves the second problem. The simulation results show that the proposed SSPRT-based MHT can achieve better tracking performance than MHT based on the WSPRT under a high false alarm spatial density.

Multi-target tracking is a difficult task in radar data processing field. To track multiple targets with the video data collected by a X-band surface surveillance radar, a two-level cascade detection algorithm is designed at first. We combine the motion model and appearance model of the targets together for multi-target tracking and propose a multi-target tracking algorithm based on scale adaptive kernelized correlation filter (SAKCF) and Kalman filter (KF) in this paper. Firstly, the extended targets are detected by the designed tow-level cascade detection algorithm and the centroids of the extended targets are extracted by centroid algorithm too. Then, a SAKCF tracker is assigned to each target. KF is utilized to predict the position of each target and each SAKCF tracker searches the region near the predicted position to determine the real position and scale of each target. The association between tracks and trace points is performed based on Hungary algorithm. Finally, the abnormal tracks are deleted. Experimental results based on the radar video data show that compared with the classical simple online real-time target tracking (SORT) algorithm and joint probabilistic data association (JPDA) algorithm, the proposed algorithm improves multi-object tracking precision (MOTP) and multi-object tracking accuracy (MOTA).

In this paper, we present a novel 2-D laser-based global localization approach for mobile robots, which is composed of geometrical relationship construction, a new structural unit encoding scheme (SUES), and an extended multiple hypothesis tracking (MHT) algorithm. Different from existing methods, we construct a 3-D "directional endpoint" feature encapsulating both the endpoint and the direction of a line segment; on this basis, a novel and efficient online structural unit encoding scheme (SUES) is proposed to describe the geometric relationship between the two directional endpoint features with some robustness to dynamic disturbances. Note that SUES presented in this paper is different from the bag-of-words scheme in two aspects: 1) SUES quantizes the geometrical relationship without offline training for vocabulary and 2) SUES is independent of the quality of the vocabulary. By factoring the global localization problem into a discrete pose estimation problem, the MHT is extended on the basis of SUES and odometry information to gradually restore the global robot pose. Different from the classical MHT framework, the extended MHT takes the independent observation results as the a priori, while the likelihood term is composed of consecutive candidate poses and the odometry information. Evaluations are carried out by using both publicly available data sets and self-recorded data sets. Comparative experimental results with respect to the adaptive Monte Carlo localization are presented to show the superior performance of the proposed approach in terms of success ratio and efficiency.

An important step in multitarget tracking (MTT) is the assignment of measurement/target pairing probabilities, i.e., the probability that a certain measurement is associated with a particular target. This is one of the more computationally intensive steps of the Joint Probabilistic Data Association (JPDA) and other MTT algorithms. Determining such assignment probabilities in the conventional JPDA algorithm involves the generation and analysis of all possible binary feasibility matrices, where each feasibility matrix represents the feasible event that each measurement is due to only one target (or clutter) and that each target causes at most one measurement. We discuss a new and most efficient technique for obtaining the measurement/target pairing probabilities directly (without first producing the feasibility matrices) and its implementation on a new analog optical computer.

This study aims to reduce the sensitivity of the unscented Kalman filter (UKF) estimates with respect to uncertain model parameters, leading to a more robust UKF. The standard minimum‐variance cost index is augmented to include a penalty on the sensitivities of the state estimates about parameter uncertainties in the form of a weighted norm. A new filter gain is thus obtained, and the desensitised UKF (DUKF) provides better estimation of the true states than the standard UKF with an imperfect plant model. Numerical examples are shown to demonstrate the efficacy of the DUKF.