Probabilistic Data Association Algorithm Research Articles

Vote selection mechanisms and probabilistic data association-based mobile node localization algorithm in mixed LOS/NLOS environments

As one of the key technologies of wireless sensor networks (WSNs), the localization of mobile nodes (MN) is one of the most significant research topics in WSNs. When a line-of-sight (LOS) channel is available, accuracy localization result can be obtained. Motivated by the fact that the non-line-of-sight (NLOS) propagation of signal is ubiquitous and decreases the accuracy of localization, we propose a MN localization algorithm in mixed LOS/NLOS environments. Considering the characteristics of NLOS error, we propose a localization algorithm based on vote selection mechanisms to filter the distance measurements and reserve the reliable measurements. Then a modified probabilistic data association algorithm is proposed to fuse the multiple measurements reserved from the vote selection. The position of the MN is finally determined by a linear least squares algorithm based on reference nodes selection. This algorithm effectively mitigates various kinds of NLOS errors and largely improves the localization accuracy of the MN in mixed LOS/NLOS environments. The simulation and experiments results show that the proposed algorithm has better robustness and higher localization accuracy than other methods.

Multi‐target tracking in clutter using a high pulse repetition frequency radar

In this study, two algorithms of single-target tracking in clutter using a high pulse repetition frequency radar are extended: the Gaussian mixture measurement likelihood-integrated track splitting (GMM-ITS) algorithm and the enhanced multiple models (MM) to multi-target tracking algorithm, that is, the GMM-joint ITS algorithm and the enhanced MM-joint probabilistic data association algorithm, respectively. Both algorithms are extended on the basis of the optimal Bayes approach that creates track clusters for determining the nearby tracks that share measurements by enumerating and evaluating all the feasible joint measurement allocations. In all cases, the track trajectory probability density function is a Gaussian mixture, and both algorithms enable false track discrimination using the probability of target existence.

Heterogeneous Multiple Sensors Joint Tracking of Maneuvering Target in Clutter.

To solve the problem of tracking maneuvering airborne targets in the presence of clutter, an improved interacting multiple model probability data association algorithm (IMMPDA-MDCM) using radar/IR sensors fusion is proposed. Under the architecture of the proposed algorithm, the radar/IR centralized fusion tracking scheme of IMMPDA-MDCM is designed to guarantee the observability of the target state. The interacting multiple model (IMM) deals with the model switching. The modified debiased converted measurement (MDCM) filter accounts for non-linearity in the dynamic system models, and reduces the effect of measurement noise on the covariance effectively. The probability data association (PDA) handles data association and measurement uncertainties in clutter. The simulation results show that the proposed algorithm can improve the tracking precision for maneuvering target in clutters, and has higher tracking precision than the traditional IMMPDA based on EKF and IMMPDA based on DCM algorithm.

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.

Multitarget range-azimuth tracker

We focus on the design of a Kalman filter-based algorithm to track multiple targets in range and azimuth. The detection stage implements a multitarget strategy aimed at collecting and eventually processing all target measurements. Existing tracks are updated using a joint probabilistic data association (JPDA) algorithm to handle the data association problem.We model target echoes as coherent pulse trains in white Gaussian noise with unknown power. The performance analysis assumes a maritime scenario and shows that the proposed tracker outperforms more conventional approaches.

Coalescence-avoiding joint probabilistic data association based on bias removal

AbstractIn order to deal with the track coalescence problem of the joint probabilistic data association (JPDA) algorithm, a novel approach from a state bias removal point of view is developed in this paper. The factors that JPDA causes the state bias are analyzed, and the direct computation equation of the bias in the ideal case is given. Then based on the definitions of target detection hypothesis and target-to-target association hypothesis, the bias estimation is extended to the general and practical case. Finally, the estimated bias is removed from the state updated by JPDA to generate the unbiased state. The results of Monte Carlo simulations show that the proposed method can handle track coalescence and presents better performance when compared with the traditional methods.

Smoothing joint integrated probabilistic data association

The authors extend the smoothing integrated probabilistic data association algorithm to multi-target tracking in clutter, or alternatively, use smoothing to improve the joint integrated probabilistic data association (JIPDA) algorithm. The predictions of forward and backward JIPDA are fused to form the smoothing prediction, which is used for smoothing multi-target data association.

Online clutter estimation using a Gaussian kernel density estimator for multitarget tracking

In this study, the spatial distribution of false alarms is assumed to be a non-homogeneous Poisson point (NHPP) process. Then, a new method is developed under the kernel density estimation (KDE) framework to estimate the spatial intensity of false alarms for the multitarget tracking problem. In the proposed method, the false alarm spatial intensity estimation problem is decomposed into two subproblems: (i) estimating the number of false alarms in one scan and (ii) estimating the variation of the intensity function value in the measurement space. Under the NHPP assumption, the only parameter that needs to be estimated for the first subproblem is the mean of false alarm number, and the empirical mean is used here as the maximum likelihood estimate of that parameter. Then, for the second subproblem, an online multivariate local adaptive Gaussian kernel density estimator is proposed. Furthermore, the proposed estimation method is seamlessly integrated with widely used multitarget trackers, like the joint integrated probabilistic data association algorithm and the multiple hypotheses tracking algorithm. Simulation results show that the proposed KDE-based method can provide a better estimate of the false alarm spatial intensity and help the multitarget trackers yield superior performance in scenarios with spatially non-homogeneous false alarms.

A battery chemistry-adaptive fuel gauge using probabilistic data association

This paper considers the problem of state of charge (SOC) tracking in Li-ion batteries when the battery chemistry is unknown. It is desirable for a battery fuel gauge (BFG) to be able to perform without any offline characterization or calibration on sample batteries. All the existing approaches for battery fuel gauging require at least one set of parameters, a set of open circuit voltage (OCV) parameters, that need to be estimated offline. Further, a BFG with parameters from offline characterization will be accurate only for a “known” battery chemistry. A more desirable BFG is one that is accurate for “any” battery chemistry. In this paper, we show that by storing finite sets of OCV parameters of possible batteries, we can derive a generalized BFG using the probabilistic data association (PDA) algorithm. The PDA algorithm starts by assigning prior model probabilities (typically equal) for all the possible models in the library and recursively updates those probabilities based on the voltage and current measurements. In the event of an unknown battery to be gauged, the PDA algorithm selects the most similar OCV model to the battery from the library. We also demonstrate a strategy to select the minimum sets of OCV parameters representing a large number of Li-ion batteries. The proposed approaches are demonstrated using data from portable Li-ion batteries.

OTHR multipath tracking using the bernoulli filter

A multipath target tracking and recognition algorithm is proposed for an over-the-horizon radar (OTHR) tracking system in the presence of detection uncertainty and clutter. This algorithm, multipath Bernoulli filter (MPBF), uses a generalization of the Bernoulli filter for joint detection and tracking of a single target. Due to multiple ionospheric propagation paths between the radar sites and the target, finite set statistics (FISST) is used to derive the likelihood function of MPBF. While the measurement models in OTHR are nonlinear, a closed-form solution in the form of Gaussian mixture is implemented by extended Kalman filter (EKF). Since multipath tracks occur in the multipath probabilistic data association (MPDA) algorithm and the original MPBF, a method is added in MPBF to identify which are the real or multipath tracks. Simulation results are presented to demonstrate that the proposed algorithm is capable of recognizing multipath tracks and has better performance over the MPDA.

A Novel Mobile Localization Method for Distributed Sensor Network with Non-Line-of-Sight Error Mitigation

Mobile localization using distributed sensor network has attracted significant research interest in recent years. One of the main challenges for mobile location estimation is the presence of the non-line-of-sight (NLOS) propagation. It results in erroneous measurements containing NLOS error due to the reflection and diffraction. The NLOS error could seriously reduce the localization accuracy. This paper presents a novel mobile localization method for distributed sensor network in NLOS propagation scenarios. The aim of the method is to mitigate the NLOS error and enhance the localization accuracy. The transition between the LOS and NLOS is described by two-state Markov model. The interacting multiple model frame is employed to estimate the position of unknown node. The probability data association algorithm is used to filter the estimated location. The simulation results show that the proposed method outperforms the traditional filter method in NLOS scenarios.

Centralized Fuzzy Data Association Algorithm of Three-sensor Multi-target Tracking System

For improving the effect of multi-target tracking in dense target and clutter scenario, a centralized fuzzy optimal assignment algorithm (CMS-FOA) of three-sensor multi-target system is proposed. And on the base of this, a generalized probabilistic data association algorithm (CMS-FOAGPDA) based on CMS-FOA algorithm is presented. The fusion algorithm gets effective 3-tuple of measurement set by using components of several satisfactory solutions of the fuzzy optimal assignment problem and then uses generalized probabilistic data association algorithm to calculate the update states of targets. Simulation results show that, in the aspect of multi-target tracking accuracy, CMS-FOA algorithm is superior to the optimal assignment (CMS-OA) algorithm based on state estimate and CMS-FOAGPDA algorithm is better than CMS-FOA algorithm. But considering the time spent, CMS-FOA algorithm spends a minimum of time and CMS-FOAGPDA algorithm is exactly on the contrary. Therefore, compared with CMS-OA algorithm, the two algorithms presented in the study each has its advantages and should be chosen according to the needs of the actual application when in use.

Adaptive Tracking Algorithm of Multi-Target Based on Fuzzy Clustering

For multi-target tracking system, aiming at solving the problem of low precision of state estimation caused by the data correlation ambiguity, the paper presents a novel multi-sensor multi-target adaptive tracking algorithm based on fuzzy clustering theory. Based on the joint probability data association algorithm, the new approach takes account of the case that whether the measure is validated and its possibility of belong to false alarm, and improves the correlation criterion of effective measurement with existing track on the basis of fuzzy clustering theory, which all perfect the update equation of target state estimation and the covariance. Meanwhile, with the adaptive distributed fusion processing structure, it enhance the robustness of the system and without prejudice to the real-time tracking. With the simulation case studies of radar/infrared sensor fusion multi-target tracking system, it verifies the effectiveness of the proposed approach.

Rough Sets Probabilistic Data Association Algorithm and its Application in Multi-target Tracking

Abstract A rough set probabilistic data association (RS-PDA) algorithm is proposed for reducing the complexity and time consumption of data association and enhancing the accuracy of tracking results in multi-target tracking application. In this new algorithm, the measurements lying in the intersection of two or more validation regions are allocated to the corresponding targets through rough set theory, and the multi-target tracking problem is transformed into a single target tracking after the classification of measurements lying in the intersection region. Several typical multi-target tracking applications are given. The simulation results show that the algorithm can not only reduce the complexity and time consumption but also enhance the accuracy and stability of the tracking results.

Multitarget tracking system based on an infrared fish-eye lens

A multitarget tracking system based on an infrared (IR) fish-eye lens is built to satisfy urgent requirements of large-field IR, multitarget, real-time reconnaissance and tracking. Utilizing an IR fish-eye lens and a 512×512 pixel PtSi detector, the system can detect threatening targets at the whole space domain and the whole time domain. The hardware of the system based on a dual-digital signal processor is designed to implement data processing for multitarget tracking algorithms, which include a track initiation algorithm and a modified generalized probability data association algorithm. We also carried out a tracking experiment for two aerial maneuvering targets. Comparing the theoretical and experimental tracks, the availability of the system and the real-time capability of multitarget tracking are validated.

Multitarget Tracking with Doppler Ambiguity

A new approach for multitarget detection and tracking with Doppler ambiguity is presented. Ambiguous Doppler measurements, in addition to the position measurements, are directly used in data association and tracking. Based on the unscented Kalman filter (UKF), the multiple hypothesis tracking (MHT) algorithm and the probabilistic data association (PDA) algorithm, three different methods for solving the ambiguity, independent of the choice of particular pulse repetition frequency (PRF) values, in the tracking level are proposed. First, the UKF is modified to handle explicitly the ambiguous Doppler measurement. It is shown that the modified UKF can achieve better tracking performance than the standard UKF. On the other hand, the MHT and PDA algorithms, both of which are usually used to solve the measurement-to-track association problem, are modified here to handle the Doppler ambiguity problem. Simulations are performed to demonstrate the effectiveness of the new algorithms.

Target Tracking with Two Passive Infrared Sensors Augmented with Irradiance Ratio

In target tracking using the passive infrared sensors, the principle of triangulation distance measurement is normally used as the basic method. However, when the target directions are nearly collinear relative to the baseline, this method merely based on EKF and angle measurements produces poor results. To solve this problem, we propose a target tracking solution based on dual infrared sensors in the cluttered environment. This method is a joint estimation algorithm of target motion state and atmospheric parameter such as the extinction coefficient. The method combines the probability data association algorithm with the augmented extended Kalman filter algorithm, into which we introduce the rate of infrared energy absorbed by the sensors at the ends of the baseline as additional measurement vector. Simulation results show that the proposed method performs better than the standard extended Kalman filter method, even in the case that the targets position is near the baseline in the cluttered environment.

Extended Probabilistic Data Association Algorithm

[Purpos In order to improve the accuracy of target tracking and reduce losing rate of target in the multiple target tracking, a new algorithm called Extended Probabilistic Data Association (EPDA) is presented in this paper. [Metho This paper defines joint association event based on the number of target and puts forward the EPDA for target tracking. [Result Experimental results show that this algorithm has higher accuracy of target tracking than the Probabilistic Data Association algorithm and costs much less time relative to the Joint Probabilistic Data Association algorithm. [Conclusion Consequently, EPDA is an effective algorithm to balance the accuracy and the losing rate in target tracking.

Integrated track initialization and maintenance in heavy clutter using probabilistic data association

Target tracking in high clutter or low signal-to-noise ratio (SNR) environments is an important topic and still a challenging task. Joint Maximum Likelihood Probabilistic Data Association (JML-PDA) is a well-known batch method for initializing the tracks of very low observable (VLO) targets in heavy clutter environments. On the other hand, the Joint Probabilistic Data Association (JPDA) algorithm, which is commonly used for recursive track maintenance, lacks track initialization capability. In this paper, we propose a Combined JML-PDA and JPDA (CJML-PDA) algorithm to automatically initialize and maintain the tracks. This combined approach seamlessly shares information between the initialization and maintenance stages of the tracker. In contrast, in other batch-recursive approaches the initialization and maintenance algorithms operate rather independent of each other. The effectiveness of the proposed algorithm is demonstrated on a heavy clutter scenario and its performance is tested on closely-spaced (but resolved) targets with association ambiguity using angle-only measurements.

From Nominal to True A Posteriori Probabilities: An Exact Bayesian Theorem Based Probabilistic Data Association Approach for Iterative MIMO Detection and Decoding

It was conventionally regarded that the approximate Bayesian theorem based existing probabilistic data association (PDA) algorithms output the estimated symbol-wise a posteriori probabilities (APPs) as soft information. In our recent work, however, we demonstrated that these probabilities are not the true APPs in the rigorous mathematical sense, but a type of nominal APPs, which are unsuitable for the classic architecture of iterative detection and decoding (IDD) aided receivers. To circumvent this predicament, in this paper we propose an exact Bayesian theorem based logarithmic domain PDA (EB-Log-PDA) method, whose output has similar characteristics to the true APPs, and hence it is readily applicable to the classic IDD architecture of multiple-input-multiple-output (MIMO) systems using the general M-ary modulation. Furthermore, we investigate the impact of the EB-Log-PDA algorithm's inner iteration on the design of EB-Log-PDA aided IDD receiver. We demonstrate that introducing inner iterations into EB-Log-PDA, which is common practice in conventional-PDA aided uncoded MIMO systems, would actually degrade the IDD receiver's performance, despite significantly increasing the overall computational complexity of the IDD receiver. Finally, we investigate the relationship between the extrinsic log-likelihood ratios (LLRs) of the proposed EB-Log-PDA and of the approximate Bayesian theorem based logarithmic domain PDA (AB-Log-PDA) reported in our previous work. Despite their difference in extrinsic LLRs, we also show that the IDD schemes employing the EB-Log-PDA and the AB-Log-PDA without incorporating any inner PDA iterations have a similar achievable performance close to that of the optimal maximum a posteriori (MAP) detector based IDD receiver, while imposing a significantly lower computational complexity in the scenarios considered.