Integrated Probabilistic Data Association Research Articles

An IPDA based target existence assisted Bayesian detector for target tracking in clutter

In this paper, an integrated probabilistic data association (IPDA) based target existence assisted Bayesian detector (TE-BD) is derived for target tracking in the clutter. The key idea of the proposed method is to feed back the predicted target position information and the existence probability from the tracker to the detector to facilitate target detection and tracking, while ensuring radar can terminate track quickly when target disappears. We first derive the prior information assisted target detection model, and then present the general steps of the TE-BD scheme based on the IPDA framework. Simulation results verify the effectiveness of the proposed IPDA-TE-BD w.r.t. the existing works.

Modified smoothing data association for target tracking in clutter

Tracking an unknown target in noisy environment is difficult especially when the target is maneuvering and has unknown trajectory. Smoother uses measurements from future scans to estimate the target state in past scan. This requires the fusion of forward and backward prediction. However, due to uncertain target motions and low detection probabilities, backward prediction could not associate with forward prediction which results in inequitable fusion pair and thus, smoothing performance could not be improved. To cope with these difficulties, the proposed algorithm modifies the fixed-lag smoothing data association based on the integrated probabilistic data association (IPDA) algorithm and a new algorithm called modified smoothing IPDA (MSIPDA) is developed. MSIPDA utilizes two IPDA filters to obtain forward IPDA (fIPDA) track and backward (bIPDA) track estimation in each scan. Each fIPDA prediction generates multiple fusion pairs in association with bIPDA multi-track prediction. These fusion pairs are created in the form of components. As a result, multiple smoothing components are formed with their smoothing component data association probabilities for computing MSIPDA track components state estimate. In addition, the smoothing data association probabilities upgrade the forward track which makes the forward track more powerful for target tracking in clutter. The numerical assessment of MSIPDA is verified using simulations. The result shows significant false track discrimination performance in comparison to existing algorithms.

Consensus-based distributed adaptive target tracking in camera networks using Integrated Probabilistic Data Association

In this paper, a novel consensus-based adaptive algorithm for distributed target tracking in large scale camera networks is presented, aimed at situations characterized by limited sensing range, high-level clutter, and possibly occulted targets. The concept of Integrated Probabilistic Data Association (IPDA) is introduced in the distributed adaptive tracker design so that the proposed algorithm, named IPDA Adaptive Consensus Filter (IPDA-ACF), incorporates probabilities of acquiring target-originated measurements, conditioned on either target perceivability or target existence. A distributed adaptation scheme represents the core element of the algorithm, allowing fast convergence under a large variety of operating conditions, emphasizing the influence of the nodes with the highest probability of obtaining target-originated measurements. A theoretical analysis of stability and reduction of noise influence allows getting an insight into the relationship between the local trackers and the global consensus scheme. A comparison with analogous existing methods done by extensive simulations shows that the proposed method achieves the best performance, in spite of lower communication and computation requirements.

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.

IPDA filters in the sense of Gaussian mixture PHD algorithm

The Integrated Probabilistic Data Association (IPDA) type filters provide estimates of the underlying target probability of existence as well as they track state maintenance. For each scan, IPDA recursive calculates the probability of target existence in order to resolve the uncertainty. Likewise, Random Finite Set (RFS) is a method for single target and multi-target tracking. It provides a Bayesian recursion of multi-target distribution through the Finite Set calculus. Practical implementation of multi-target posterior recursion is too difficult. It was analytically proved that IPDA algorithm can be derived from the RFS based filter recursion under the linear Gaussian assumptions. Probability hypothesis density (PHD) filter is an alternative to this problem where only the first order moment of the complete multi-target posterior is propagated in time. In this article, IPDA and Gausian Mixtures PHD (GM PHD) filters in a single target tracking scenario are derived and compared. Simulations have demonstrated the superiority of IPDA filters in heavy clutters.

Launch point prediction employing the smoothing IPDA algorithm in 3-D cluttered environments

The counter-battery radar system tracks the artillery projectile trajectory and finds the launch point (LP) to take swift protective actions against enemies’ succeeding shelling. The LP estimation highly depends on the accuracy of target state estimate. However, measurements from the surveillance radar systems include clutter measurements as well as target measurements, so the data association is needed for target tracking in the cluttered environments. In this paper, the smoothing integrated probabilistic data association (sIPDA) algorithm is applied for estimation of artillery projectile trajectory in the cluttered environments and backward prediction with the constant axial force (CAF) model is carried out for launch point prediction (LPP). The proposed method significantly improves the accuracy of LPP compared with the integrated probabilistic data association (IPDA) algorithm.

Iterative joint integrated probabilistic data association for multitarget tracking

In situations with a significant number of targets in mutual proximity (close to each other), optimal multitarget data association approach suffers from the numerical explosion. This severely limits the applicability, i.e., the number of close targets that may be reliably tracked.We propose an iterative implementation of the joint integrated probabilistic data association (JIPDA) which allows a performance/computation resources tradeoff. This approach can also be incorporated into joint integrated track splitting (JITS). The iterations start with the single target integrated probabilistic data association (IPDA) and each subsequent iteration improves the approximation towards JIPDA, reaching the optimal multitarget solution within a finite number of iterations.

Passive target tracking with intermittent measurement based on random finite set

In the tracking problem for the maritime radiation source by a passive sensor, there are three main difficulties, i.e., the poor observability of the radiation source, the detection uncertainty (false and missed detections) and the uncertainty of the target appearing/disappearing in the field of view. These difficulties can make the establishment or maintenance of the radiation source target track invalid. By incorporating the elevation information of the passive sensor into the automatic bearings-only tracking (BOT) and consolidating these uncertainties under the framework of random finite set (RFS), a novel approach for tracking maritime radiation source target with intermittent measurement was proposed. Under the RFS framework, the target state was represented as a set that can take on either an empty set or a singleton; meanwhile, the measurement uncertainty was modeled as a Bernoulli random finite set. Moreover, the elevation information of the sensor platform was introduced to ensure observability of passive measurements and obtain the unique target localization. Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source and demonstrate the superiority of the proposed approach in comparison with the traditional integrated probabilistic data association (IPDA) method. The tracking performance under different conditions, particularly involving different existence probabilities and different appearance durations of the target, indicates that the method to solve our problem is robust and effective.

Bernoulli particle filter with observer altitude for maritime radiation source tracking in the presence of measurement uncertainty

For maritime radiation source target tracking in particular electronic counter measures (ECM) environment, there exists two main problems which can deteriorate the tracking performance of traditional approaches. The first problem is the poor observability of the radiation source. The second one is the measurement uncertainty which includes the uncertainty of the target appearing/disappearing and the detection uncertainty (false and missed detections). A novel approach is proposed in this paper for tracking maritime radiation source in the presence of measurement uncertainty. To solve the poor observability of maritime radiation source target, using the radiation source motion restriction, the observer altitude information is incorporated into the bearings-only tracking (BOT) method to obtain the unique target localization. Then the two uncertainties in the ECM environment are modeled by the random finite set (RFS) theory and the Bernoulli filtering method with the observer altitude is adopted to solve the tracking problem of maritime radiation source in such context. Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source, and also demonstrate the superiority of the method compared with the traditional integrated probabilistic data association (IPDA) method. The tracking performance under different conditions, particularly those involving different duration of radiation source opening and switching-off, indicates that the method to solve our problem is robust and effective.

Smoothing innovations and data association with IPDA

Surveillance sensors return detections from targets as well as the clutter measurements. Data association algorithms often use innovations to discriminate between the target and the clutter measurements. Reducing the covariance of innovations reduces the surveillance area from which measurements are used, reducing the number of clutter measurements. This paper introduces smoothing innovations which reduce innovation covariance, and improve the data association performance. This concept is applied to the Integrated Probabilistic Data Association (IPDA) to produce a Smoothing IPDA (sIPDA). sIPDA trajectory estimation errors are reduced with a smoothing delay. A surprising outcome is that sIPDA improves the false track discrimination in real time (without the smoothing delay).

Tracking in clutter using IMM-IPDA-based algorithms

We describe three single-scan probabilistic data association (PDA) based algorithms for tracking manoeuvering targets in clutter. These algorithms are derived by integrating the interacting multiple model (IMM) estimation algorithm with the PDA approximation. Each IMM model a posteriori state estimate probability density function (pdf) is approximated by a single Gaussian pdf. Each algorithm recursively updates the probability of target existence, in the manner of integrated PDA (IPDA). The probability of target existence is a track quality measure, which can be used for false track discrimination. The first algorithm presented, IMM-IPDA, is a single target tracking algorithm. Two multitarget tracking algorithms are also presented. The IMM-JIPDA algorithm calculates a posteriori probabilities of all measurement to track allocations, in the manner of the joint IPDA (JIPDA). The number of measurement to track allocations grows exponentially with the number of shared measurements and the number of tracks which share the measurements. Therefore, IMM-JIPDA can only be used in situations with a small number of crossing targets and low clutter measurement density. The linear multitarget IMM-IPDA (IMM-LMIPDA) is also a multitarget tracking algorithm, which achieves the multitarget capabilities by integrating linear multitarget (LM) method with IMM-IPDA. When updating one track using the LM method, the other tracks modulate the clutter measurement density and are subsequently ignored. In this fashion, LM achieves multitarget capabilities using the number of operations which are linear in the: number of measurements and the number of tracks, and can be used in complex scenarios, with dense clutter and a large number of targets.

Low elevation sea-surface target tracking using IPDA type filters

When detecting a target using radar in the presence of multipath fading, the signal to noise ratio (SNR) at receiver can be dramatically reduced at certain ranges due to multipath signal cancellation, which leads to a significant drop of the detection probability at those ranges. The detection of a low elevation sea-surface target (LESST) is one example. For target tracking, this special case contrasts to the usual situation where we may often assume a constant probability of detection. Automatic target tracking requires a measure of track quality to discriminate between true and false tracks. This track quality measure should take both the existence of target and the SNR of the sensor into account, and be consistent with tracking performance. This paper investigates automatic target tracking using the integrated probabilistic data association (IPDA) type filters in the presence of multipath fading. Several existing single-scan probabilistic data association (PDA)-based filters, which calculate the probabilistic measure of track quality while estimating the track state, are evaluated in the scenario of tracking a nonmaneuvering LESST in the presence of both multipath fading and clutter. In addition, a variable Markov chain (VMC) IPDA tracker (IPDA-VMC), which has joint merits of both IPDA and the IPDA with a three state Markov chain (IPDA-M2), has been designed and evaluated here. In the derivation of track quality measure of both IPDA-M2 and 1PDA-VMC, the possibility that a target is present but is temporarily unable to be detected has been explicitly considered. A tracker using this track quality measure for false track discrimination has significantly improved LESST tracking performance. A simulation study verifies that both IPDA-M2 and IPDA-VMC have better capability to handle the LESST tracks and have an improved tracking performance over other PDA-based tracking algorithms under consideration.