Probabilistic Data Association Technique Research Articles

Multiple passive-sensor distributed target tracking approach with Machine Learning Feedback

In this paper, a novel recursive state estimation approach in three-dimensional space for a non-maneuvering target is developed. The proposed technique employs angle and amplitude information in a distributed fusion setup, tackling the issue of target state estimation in cluttered environments, as well as target detection with a machine learning feedback mechanism with sequence measurements from multiple passive sensors. A new Integrated Probabilistic Data Association technique with Machine Learning Feedback (IPDA-MLF) is proposed. Most of the clutter measurements are eliminated by fusing angles and amplitude information in the Local Sensor tracker with an Integrated Probabilistic Data Association algorithm (LS-IPDA) framework. To deal with low observability issues, as well as to fuse information from multiple sensors, a Machine Learning (ML) algorithm is deployed, which utilizes a convolutional neural networks-based algorithm. The proposed IPDA-MLF algorithm resolves possible track-to-track associations and provides feedback to the local tracker for increased track retention. The refined information from local trackers is again converted to heat maps and the ML-based solution is used to fuse the local tracks at a central level. Simulation results show that the performance of the proposed algorithm is improved by the incorporation of the machine learning feedback, reducing false tracks, increasing true track retention, and eliminating mismatched fusion at the central tracker.

Neural Network-Based Stereo Vision Outlier Removal

Stereo vision systems rely on accurate feature matching to provide valid stereo reconstruction and pose estimation. This accuracy is achieved through outlier removal techniques, such as RANSAC. However, images also contain semantic information, which can be extracted using neural networks. This paper proposes an additional outlier removal method, where the images are semantically segmented using a neural network, before the features identified are assigned semantic identifiers using a probabilistic data association technique, and matches are evaluated based on this added semantic information. This blending of feature-based techniques with dense semantic maps allows for more information to be tied to each feature, not just its position in the image. This opens paths to applications like class-based clustering. The approach proposed is compared to a traditional outlier removal system by comparing the produced disparity values to known ground truth measurements, and assessed for accuracy and execution speed. It is shown how the addition of semantic segmentation does improve the accuracy of disparity measurements in stereo images, with a loss in processing speed. However, this loss can be mitigated by utilising more specialised hardware.

Multiple extended target tracking by truncated JPDA in a clutter environment

Data association is a crucial part of target tracking systems with clutter measurements. In general, its complexity increases sharply with a number of targets and measurements. Recently, high-resolution sensors have given rise to extended target tracking problems and more than one measurements can emerge from each target, making the association problems more complex. In this study, a tractable algorithm based on the Gaussian process measurement model and truncated joint probabilistic data association technique is proposed for multiple extended target tracking in the presence of measurement origin uncertainty. Based on the marginal association probabilities, the calculation amount is effectively reduced by truncating the association events with low probabilities in the shortest path problem. The effectiveness of the proposed algorithm is verified by the test of multiple extended targets tracking and compared with the linear-time joint probabilistic data association as well as the algorithm on random finite sets. Simulation results show that the proposed algorithm can track multiple extended targets accurately, which is significant in high-resolution radar tracking systems.

Multi‐sensor track‐to‐track fusion with target existence in cluttered environments

Multi-sensor fusion for multiple target tracking in cluttered environments is needed for improving tracking accuracy and track maintenance over single sensor target tracking in real target tracking applications. A track quality measure, such as the probability of target existence, can be used when fusing local sensor tracks for false track discrimination at the fusion centre and to improve the performance of the track-to-track association. The track quality measure is a highly effective tool for track maintenance and lowers the computational load by sending tracks with a high probability of target existence to the track centre. In this study, the authors utilise the iterative joint-integrated probabilistic data association technique for multi-sensor distributed fusion systems. Track-to-track fusion performance also depends on distributed fusion architecture. The track-to-track fusion algorithm is evaluated in distributed fusion systems without memory, with memory, and with feedback. The track confirmation rates and position errors are simulated and compared with low-level centralised fusion, which is theoretically optimal.

Target Tracking Formulation of the SVSF With Data Association Techniques

An important area of study for aerospace and electronic systems involves target tracking applications. To successfully track a target, state and parameter estimation strategies are used in conjunction with data association techniques. Even after 50 years, the Kalman filter (KF) remains the most popular and well-studied estimation strategy in the field. However, the KF adheres to a number of strict assumptions that leads to instabilities in some cases. The smooth variable structure filter (SVSF) is a relatively new method, which is becoming increasingly popular due to its robustness to disturbances and uncertainties. This paper presents a new formulation of the SVSF. The probabilistic and joint probabilistic data association techniques are combined with the SVSF and applied on multitarget tracking scenarios. In addition, a new covariance formulation of the SVSF is presented based on improving the estimation results of nonmeasured states. The results are compared and discussed with the popular KF method.

An SVSF-Based Generalized Robust Strategy for Target Tracking in Clutter

Autonomous self-drive requires intelligence and cognition that relies on observations and tracking of the state of motion of surrounding vehicles. This information can be acquired by using sensors; however, these are often affected by clutter and noise that, in turn, introduce the issues of estimation and data origin uncertainty into the tracking system. The most popular methods for estimation and tracking are based on the well-studied Kalman filter (KF). KF is optimal when noise is white and remains so despite uncertainties in the filter model; the robustness and stability of the KF is affected if this condition is not met. The smooth variable structure filter (SVSF) is a relatively new method that is more robust to disturbances and uncertainties. The SVSF ensures stability by using a discontinuous corrective term that maintains estimates to within a subspace of the true state trajectory. The discontinuous corrective term results in chattering that is removed by using a smoothing boundary layer. In this paper, a generalized covariance formulation of the SVSF and a generalized optimal time-varying smoothing boundary layer are proposed. The generalized optimal SVSF is then combined with a joint probabilistic data association technique for target tracking. The robustness and accuracy of the new form of filtering and data association is validated and comparatively analyzed by its application to an experimental traffic monitoring system based on Light Detection and Ranging.

Global sensor selection for maneuvering target tracking in clutter

This paper studies the problem of sensor selection for maneuvering target tracking in the cluttered environment. By modeling the target dynamics as jump Markov linear systems, a decentralized tracking algorithm is developed by applying the extended Kalman filter and the probabilistic data association technique. A cost function that minimizes the expected filtered mean square position error is utilized and a sensor selection scheme is proposed. A numerical example is provided to illustrate the effectiveness of the proposed approach.

Continuous 2-D assignment for multitarget tracking with rotating radars

Mechanically steered scanning radars receive measurements continuously while sweeping the surveillance region. However, most target tracking algorithms, like the multiple hypothesis tracker (MHT) and the joint probabilistic data association (JPDA) techniques, wait for the end of a scan in order to process the measurements and to estimate targets states. This is due to the fundamental assumption of one-to-one association between tracks and measurements and the 360° physical limit of a scan. Associating measurements to initialized tracks and filtering at the end of a complete scan may cause significant delays in target state update. In addition, association may become imperfect due to longer intervals between updates. This issue becomes significant when tracking high-speed targets with low scan rate sensors as in the airborne early warning (AEW) system. In this paper, we present a new dynamic sector processing algorithm using two-dimensional (2-D) assignment for scanning radars that report measurements within the duration of a scan. The full scan is dynamically and adaptively divided into sectors, which could be as small as a single detection, depending on the scanning rate, sparsity of targets, and required target state update speed. Measurement-to-track association, filtering, and target state update are done dynamically while sweeping from one region to another, i.e., continuous track update, limited only by the intermeasurement interval, becomes possible. The proposed algorithm offers low latency while maintaining estimation accuracy in track updates as well as efficient utilization of computational resources compared with standard frame-based tracking algorithms. Experimental results based on rotating radars demonstrate the advantages of the proposed technique.

A joint possibilistic data association technique for tracking multiple targets in a cluttered environment

Multitarget tracking in a cluttered environment is a significant issue with a wide variety of applications. A typical approach to address this issue is the joint probabilistic data association (JPDA) technique. This technique determines joint probabilities over all targets and hits and updates the predicted target state estimate using a probability-weighted sum of innovations. This paper proposes a new joint possibilistic data association technique for tracking multiple targets. Unlike the JPDA technique, the proposed technique determines joint possibilities over all targets and hits and updates the predicted target state estimate using a possibility-weighted sum of innovations. The possibility weights are determined using the noise covariance matrices and the current received measurements such that the total sum of the distances between all measurements and targets is minimized. The proposed technique performs data association based on a possibility matrix of measurements to trajectories; thus, it highly reduces the computational complexity compared to conventional data association techniques. The proposed association technique is applied to examples of multitarget tracking in a cluttered environment, and the results demonstrate its efficiency.

Numerically Stable and Efficient Implementation of a Continuous-Discrete Multiple-Model Estimator

This paper deals with the problem of implementing adaptive radar tracking filters based on continuous-time models of target motion and on discrete-time models of measurement process. The particular difficulties addressed include: nonlinear and non-stationary target movement models with uncertain parameters, and low data rate due to a rotating radar antenna. The proposed tracking filter relies basically on the continuous-discrete variant of the extended Kalman filter (EKF), the probabilistic data association (PDA) technique and the interacting multiplemodel (IMM) state estimation scheme. Numerical properties of the algorithm are discussed and a software implementation is developed using the open-source BLAS library. Several design concepts are combined to assure numerical stability, convergence and efficiency of the estimator.

Probabilistic data association applications to data fusion

We describe a new approach to fusion techniques in a multiple target tracking system for an infrared search and track (IRST) system operating in the mid- and long-wave infrared (IR) bands. The use of the two IR bands allows better performances in terms of detection probability, lower number of false tracks, and shorter time for track declaration. To properly merge data from the two sensors, an enhancement of the probabilistic data association (PDA) technique is introduced in the process. A simplification of the approach used brings back the algorithm to the well-known PDA, allowing its use in conjunction with the interacting multiple model (IMM) with increased tracking ability of the system.

Inhabitants Tracking System in a Cluttered Home Environment Via Floor Load Sensors

Home automation systems should evolve to the next phase in which they are aware of contexts, because providing services based on contexts will upgrade the service quality, thus making people more comfortable and home living more convenient. In particular, location is a piece of important and useful context information for seeking appropriate services, as well as providing them to people living in a home. So far, there have been several studies focused on tracking inhabitants in smart home environments. However, these approaches are often intrusive or require inhabitants to wear some sort of devices, which may make the inhabitants uncomfortable or even inconvenient. This problem could devastate the ultimate goal, which is to provide convenient services, and hence cause such approaches somewhat controversial. In this study, we utilize a number of load sensors together to construct a sensory floor on which exerted pressure can be detected and cover its surface with wooden flooring as in a normal home environment. Although the wooden flooring provides a flat surface for inhabitants to walk on, it also causes clutter in the sensor readings, which lead to difficulty in clearly identifying the location(s) of pressure source(s). Thus, we apply probabilistic data association technique and LeZi-Update approach to analyze the cluttered pressure readings collected by the load sensors so as to determine the positions of the inhabitants, as well as to track their movements. With our nonintrusive approach, there is no need for inhabitants to wear any devices, and this also solves the cross-walking problem in the home environment. Note to Practitioners-This system aims for detecting the location of multiple inhabitants in the home environment. We adopt the sensory floor as our tracking sensor, which consists of many blocks, each with a load cell to collect the human body weight. These blocks are covered with conventional wooden flooring to provide a flat surface on which inhabitants can walk freely. After collecting data, we apply mathematical techniques to analyze them, thus determining inhabitants' locations and tracking their movements. The limitation is that the system cannot differentiate different persons if they have close weight readings, which is the natural limitation of the load cell.

Tracking of multiple maneuvering targets using multiscan JPDA and IMM filtering

The paper considers the problem of tracking multiple maneuvering targets in the presence of clutter using switching multiple target motion models. A novel suboptimal filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach and the joint probabilistic data association (JPDA) technique. Unlike the standard single-scan JPDA approach, the authors exploit a multiscan joint probabilistic data association (mscan-JPDA) approach to solve the data association problem. The algorithm is illustrated via a simulation example involving tracking of four maneuvering targets and a multiscan data window of length two

Probabilistic data association techniques for target tracking with applications to sonar, radar and EO sensors

We present an overview of the probabilistic data association (PDA) technique and its application for different tracking scenarios, in particular for low observable (LO) (low SNR) targets. A summary of the PDA technique is presented. The use of the PDA technique for tracking low observable targets with passive sonar measurements is presented. This target motion analysis is an application of the PDA technique, in conjunction with the maximum likelihood (ML) approach, for motion parameter estimation via a batch procedure. The use of the PDA technique for tracking highly maneuvering targets combined radar resource management is described. This illustrates the application of the PDA technique for recursive state estimation using the interacting multiple model (IMM) estimator with probabilistic data association filter (PDAF) (IMMPDAF). Then we present a flexible (expanding and contracting) sliding-window parameter estimator using the PDA approach for tracking the state of a maneuvering using measurements from an electro-optical (EO) sensor. This, while still a batch procedure, has the flexibility of varying the batches depending on the estimation results in order to make the estimation robust to maneuvers as well as appearance or disappearance.

Multisensor tracking of a maneuvering target in clutter using immpda filtering with simultaneous measurement update

We present a (suboptimal) filtering algorithm for tracking a highly maneuvering target in a cluttered environment using multiple sensors. The filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach and the probabilistic data association (PDA) technique to a two sensor (radar and infrared, for instance) problem for state estimation for the target. A simultaneous measurement update approach is followed where the raw sensor measurements are passed to a central processor and fed directly to the target tracker. A multisensor PDA filter is developed for parallel sensor processing for target tracking under clutter. A past approach using parallel sensor processing has ignored certain data association probabilities leading to an inaccurate implementation. Another existing approach applies only to nonmaneuvering targets. The algorithm is illustrated via a highly maneuvering target tracking simulation example where two sensors, a radar and an infrared sensor, are used. Compared with an existing IMM/PDA filtering algorithm with sequential sensor processing, the proposed algorithm achieves significant improvement in the accuracy of track estimation.

Tracking of Multiple Moving Speakers With Multiple Microphone Arrays

In this paper we use multiple microphone arrays to fuse the location estimate from each microphone array which yields an improved estimate of the positions and velocities of multiple, simultaneously active, moving speakers based on time delay of arrivals (TDOAs). Our approach: 1) incorporates kinematic information of moving speakers by using an interacting multiple model (IMM) estimator for each speaker in order to constrain the evolution of the location measurements; 2) fuses the location estimates of the same speaker from multiple microphone arrays for better acoustical coverage of the sensed environment, and 3) directly accounts for the measurement origin uncertainty, i.e., which measurement comes from which speaker by using the probabilistic data association (PDA) technique with the IMM estimator. We demonstrate that a network of arrays combined with the estimation technique widely used in multisensor multitarget tracking area provides a consistent and coherent way to reduce the uncertainty and ambiguity of measurements. The effectiveness of our approach is illustrated by extensive simulation study on tracking a single moving speaker and two closely-spaced speakers with a crossing segment in their trajectories.

Probabilistic Data Association Techniques for Target Tracking in Clutter

In tracking targets with less-than-unity probability of detection in the presence of false alarms (FAs), data association-deciding which of the received multiple measurements to use to update each track-is crucial. Most algorithms that make a hard decision on the origin of the true measurement begin to fail as the FA rate increases or with low observable (low probability of target detection) maneuvering targets. Instead of using only one measurement among the received ones and discarding the others, an alternative approach is to use all of the validated measurements with different weights (probabilities), known as probabilistic data association (PDA). This paper presents an overview of the PDA technique and its application for different target tracking scenarios. First, it describes the use of the PDA technique for tracking low observable targets with passive sonar measurements. This target motion analysis is an application of the PDA technique, in conjunction with the maximum-likelihood approach, for target motion parameter estimation via a batch procedure. Then, the PDA technique for tracking highly maneuvering targets and for radar resource management is illustrated with recursive state estimation using the interacting multiple model estimator combined with PDA. Finally, a sliding window (which can also expand and contract) parameter estimator using the PDA approach for tracking the state of a maneuvering target using measurements from an electrooptical sensor is presented.

Tracking of multiple maneuvering targets in clutter using multiple sensors, IMM, and JPDA coupled filtering

We consider the problem of tracking multiple maneuvering targets in clutter using switching multiple target motion models. A novel suboptimal filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach, the joint probabilistic data association (JPDA) technique and coupled target state estimation to a Markovian switching system. The algorithm is illustrated via a simulation example involving tracking of two highly maneuvering, at times closely spaced, targets.

Tracking of multiple maneuvering targets in clutter using IMM/JPDA filtering and fixed-lag smoothing

We consider the problem of tracking multiple maneuvering targets in clutter using switching multiple target motion models. A suboptimal filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach and the joint probabilistic data association (JPDA) technique to a Markovian switching system. A suboptimal fixed-lag smoothing algorithm is developed by applying the IMM and the JPDA approaches to a state-augmented system. The algorithms are illustrated via a simulation example involving tracking of two highly maneuvering targets.

Multisensor tracking of a maneuvering target in clutter using IMMPDA fixed-lag smoothing

We present a suboptimal fixed-lag smoothing algorithm for tracking a highly maneuvering target in a cluttered environment using multiple sensors. The fixed-lag smoothing algorithm is developed by applying the basic interacting multiple model (IMM) approach and the probabilistic data association (PDA) technique to a state-augmented system. In the past this approach had been restricted to Markovian switching systems with no uncertainty regarding the origin of the measurements (i.e., no clutter). The algorithm is illustrated via a highly maneuvering target tracking simulation example where two sensors, a radar and an infrared sensor, are used and are assumed to operate in a cluttered environment. Compared with an existing IMMPDA filtering algorithm, the proposed smoothing algorithm achieves significant improvement in the accuracy of track estimation by introducing a small time lag between the instants of estimation and latest measurements whereas the computational load increases linearly with lag. However, the tracking delay may lead to undesired effects on control loops in certain applications.