Maneuvering Target Tracking Research Articles

A variational marginalized particle filter for jump Markov nonlinear systems with unknown transition probabilities

This paper studies a new variational marginalized particle filter for jointly estimating the state and the system mode parameters of jump Markov nonlinear systems. Contrary to the Markovian assumption usually considered to model the evolution of the system modes, we introduce conjugate prior distributions for the system mode parameters. The joint posterior distribution of the state and system mode parameters is then marginalized with respect to the mode variables. The remaining state vector is sampled using a sequential Monte Carlo algorithm, and the mode parameters are sampled using variational Bayesian inference. In order to obtain analytical solutions for the different variational distributions, we use an extended factorized approximation simplifying the variational distributions. A comprehensive simulation study is conducted to compare the performance of the proposed approach with the state-of-the-art for a modified nonlinear benchmark model and maneuvering target tracking scenarios.

Maximum mixture correntropy based outlier-robust nonlinear filter and smoother

In this paper, we are dedicated to studying the robust filtering and smoothing problem for a nonlinear non-Gaussian system. Considering the advantage of mixture correntropy with two kernel bandwidths in dealing with non-Gaussian noise, we propose the novel robust recursive filter and smoother based on the cost functions induced by the maximum mixture correntropy criterion, where the nonlinear dynamic model function and measurement model function are linearized by using the statistical linear regression (SLR) method. In the proposed robust recursive filter and smoother, we apply a third-order spherical cubature rule to obtain the prior estimation of the state and covariance matrix, and approximate the multi-dimensional Gaussian integrals encountered in the SLR method. Furthermore, two additional weights are introduced into the proposed robust recursive filter and smoother to modify the filtering and smoothing gains, respectively. The simulation results of manoeuvring target tracking under different non-Gaussian noise scenarios illustrate the effectiveness of the proposed robust recursive filter and smoother.

UAV Maneuvering Target Tracking based on Deep Reinforcement Learning

Maneuvering target tracking means that UAV observes the target through sensors, follows the target and maintains the tracking range with the target. Aiming at the target tracking problem of UAV, we built UAV motion model and UAV tracking model based on reinforcement learning, and verified the feasibility and effectiveness of the method in the simulation environment.

Adaptive Transition Probability Matrix-Based Parallel IMM Algorithm

Conventionally, the transition probabilities in the interacting multiple model (IMM) are often fixed based on the prior information. However, this conservative setting may result in inaccurate state estimations. To solve this problem, a Bayesian-based online correction function is proposed in this paper, which can adaptively adjust the transition probabilities. To deal with the response lag and the short-term peak estimation error problem during the respond to model jump, a model jumping threshold is defined, so that the current information of the models can be fully utilized by the IMM algorithm and the correction function of the transition probabilities can be further improved. Subsequently, an adaptive transition probability-based parallel IMM algorithm is proposed in this paper. Finally, three maneuvering target tracking simulations are conducted to verify the performance of the proposed algorithm, the results show that the proposed algorithm can improve the response speed of the system model jump and the state estimation accuracy. The effectiveness and feasibility of the algorithm are proven.

A multiple-model generalized labeled multi-Bernoulli filter based on blocked Gibbs sampling for tracking maneuvering targets

In this paper, an efficient implementation of the multiple-model generalized labeled multi-Bernoulli filter (MM-GLMB) is presented for tracking multiple maneuvering targets. To alleviate the generation of the redundant components, the original two-staged implementations of MM-GLMB filter are integrated into a single step bringing the benefit that only one truncation procedure is required per iteration. In this study, the authors take the convergence behavior of the Gibbs sampling into full consideration to improve the convergence rate. The blocked Gibbs sampling over lattice Gaussian distribution based solution to the implementation of MM-GLMB filter is proposed to greatly relax the computational load. The numerical simulations demonstrate the efficacy of the proposed algorithm with low computational cost.

Maximum Fuzzy Correntropy Kalman Filter and Its Application to Bearings-Only Maneuvering Target Tracking

In this paper, a novel maximum fuzzy correntropy Kalman filter (MFC-KF) algorithm is proposed to solve the problem that the effect of different samples on state estimation is uncertain in common correntropy. In the proposed algorithm, a new optimization criterion—the maximum fuzzy correntropy criterion with fuzzy correntropy based on fuzzy information theory—is used to optimize the Kalman filter, by reducing the effect of the common correntropy applying the same weight for all samples. Moreover, to apply the MFC-KF algorithm to bearings-only maneuvering target tracking, it is combined with the least-squares method for measurement conversion. Moreover, the kernel width is set adaptively. Simulations show that the proposed algorithm can track a target more accurately than the interactive multi-model extended Kalman filter (IMMEKF), the interactive multi-model unscented Kalman filter (IMMUKF), or the maximum correntropy Kalman filter (MCKF).

Structure Identification of Recursive TSK Particle Filtering via Type-2 Intuitionistic Fuzzy Decision

To reduce the dimension and realize the adaptive adjustment of the fuzzy rules in the Takagi–Sugeno–Kang (TSK) model, a recursive TSK particle filtering algorithm based on type-2 intuitionistic fuzzy decision is proposed. In the proposed algorithm, we mainly emphasize on the structure identification of the TSK fuzzy model. Firstly, the similarity of features is calculated by the ridge reduction distribution function. Secondly, the type-2 fuzzy membership degree is applied to redefined a new intuitionistic fuzzy set, which is used to calculate the decision score (type-2 intuitionistic fuzzy membership degree) of each attribute feature. And we select the appropriate features (premise variables) by the threshold decision principle. Thirdly, the importance density function of particle filtering is constructed with the estimation results of the proposed TSK fuzzy model, which can effectively reduce the particle degradation phenomenon. Finally, the results of experiments on datasets show that the proposed algorithm is efficient and is excellent at handling the nonlinear non-Gaussian problem in the maneuvering target tracking system.

Industry Tip: Picking the Minimum Process Noise Variance for Your NCV Track Filter

When tracking maneuvering targets with a nearly constant velocity (NCV) Kalman filter with discrete white noise acceleration, the selection of the process noise variance is complicated by the fact that the process noise errors are modeled as white Gaussian, while target maneuvers are deterministic or highly correlated in time. In this industry tip, guidance on picking the minimum process noise variance for the NCV Kalman filter is provided.

Maneuvering target tracking with improved interactive multiple model algorithm

Maneuvering target tracking with improved interactive multiple model algorithm

An Efficient Power Allocation Strategy for Maneuvering Target Tracking in Cognitive MIMO Radar

In this paper, an efficient power allocation (PA) strategy is developed for maneuvering target tracking (MTT) in the collocated MIMO radar. The mechanism of our strategy is to implement the optimal PA based on the prior target maneuvering information in the tracking cycle. The predicted conditional Cramer-Rao lower bound (PC-CRLB) is derived, normalized and adopted as the optimization criterion, since the PC-CRLB is based on the most recently realized measurement and provides a more accurate lower bound than the standard posterior CRLB (PCRLB). We fully demonstrate that the established optimization model is convex. By exploiting the monotonic decreasing property of the objective function, an efficient sequential relaxation-based solver is proposed for the solution, where the PA for one target is identified that whether should be fixed on the minimum level at each iteration. Simulation results show better tracking performance compared with the uniform allocation, and improved efficiency, compared with convex optimization tools.

AGIMM tracking filter algorithm based on manoeuvring feature correction

Considering the fast speed and strong manoeuvring of small offshore targets, traditional filtering algorithms cannot effectively track the filtering due to the unstable grid centre of weak manoeuvring target and slow tracking convergence of strong manoeuvring target. To overcome this problem, an adaptive grid interaction multiple model (AGIMM) tracking filter algorithm based on manoeuvring feature correction was developed. The modified algorithm detects the target manoeuvring according to the residual information of the filter, while the model structure can be adjusted under different manoeuvring conditions. Monte Carlo simulation shows that the AGIMM algorithm based on manoeuvring feature correction can improve the calculation accuracy by 35% and reduce the calculation time by 18%.

AGIMM tracking filter algorithm based on manoeuvring feature correction

Considering the fast speed and strong manoeuvring of small offshore targets, traditional filtering algorithms cannot effectively track the filtering due to the unstable grid centre of weak manoeuvring target and slow tracking convergence of strong manoeuvring target. To overcome this problem, an adaptive grid interaction multiple model (AGIMM) tracking filter algorithm based on manoeuvring feature correction was developed. The modified algorithm detects the target manoeuvring according to the residual information of the filter, while the model structure can be adjusted under different manoeuvring conditions. Monte Carlo simulation shows that the AGIMM algorithm based on manoeuvring feature correction can improve the calculation accuracy by 35% and reduce the calculation time by 18%.

Improved IMM algorithm based on XGBoost

Based on Markov hypothesis, IMM uses multiple motion models to match the moving states of the target, and assumes the transfer probability of each model according to the prior knowledge, which has strong adaptability to tracking maneuvering target. However, it is not direct enough to obtain prior knowledge from source data statistics and then make decision according to maximum likelihood, and the information of source data is not fully utilized. Therefore, we use XGBoost in machine learning algorithm to replace this process. We propose XGBoost-IMM model algorithm. XGBoost can fully learn the information of the source data and make decision on the target motion model, and then IMM can perform multi-filter filtering based on the decision. Experimental results show that our algorithm has good performance.

An Efficient Algorithm for Maneuvering Target Tracking [Tips & Tricks

Maneuvering target tracking is an important technology in engineering applications [1]-[3]. The traditional methodologies for designing it can be divided into two categories: modelbased and model-free algorithms. Almost all tracking algorithms are model based. The main idea behind modelbased tracking algorithms is to choose a representation that fits the actual state trajectories of the target movement and then to estimate the state based on the noisy observations recorded by sensors. The Kalman filter and its extensions are the most popular methods to estimate the state of a system. However, the stability and convergence rate of these algorithms depend directly on the accurate initial state estimation, unknown parameters, and covariance matrices of the process and measurement noise.

De-correlated unbiased sequential filtering based on best unbiased linear estimation for target tracking in Doppler radar

In target tracking applications, the Doppler measurement contains information of the target range rate, which has the potential capability to improve the tracking performance. However, the nonlinear degree between the measurement and the target state increases with the introduction of the Doppler measurement. Therefore, target tracking in the Doppler radar is a nonlinear filtering problem. In order to handle this problem, the Kalman filter form of best linear unbiased estimation (BLUE) with position measurements is proposed, which is combined with the sequential filtering algorithm to handle the Doppler measurement further, where the statistic characteristic of the converted measurement error is calculated based on the predicted information in the sequential filter. Moreover, the algorithm is extended to the maneuvering target tracking case, where the interacting multiple model (IMM) algorithm is used as the basic framework and the model probabilities are updated according to the BLUE position filter and the sequential filter, and the final estimation is a weighted sum of the outputs from the sequential filters and the model probabilities. Simulation results show that compared with existing approaches, the proposed algorithm can realize target tracking with preferable tracking precision and the extended method can achieve effective maneuvering target tracking.

3D 가변 선회 모델 및 기구학적 구속조건을 사용한 기동표적 추적

본 논문에서는 관측자가 얻을 수 있는 시선각(LOS) 측정값을 사용하여 관심표적의 상태변수를 추정하는 연구를 수행하였다. 관심상태변수는 표적의 위치, 속도 및 가속도로 설정하였다. 시선각 측정값은 필터에 표적운동모델 적용을 어렵게 하는 비선형성이 강한 측정값이다. 이러한 문제해결을 위해 가측정치 공식(Pseudomeasurement equation)을 사용하여 시선각 측정값 수식을 변경한 후 3D 가변선회(3D Variable Turn) 표적운동모델을 적용하였다. 또한 필터의 성능을 위해 기구학적구속조건(Kinematic Constraint)을 적용하였다. 필터는 초기조건에 강건한 특성을 가진 Bias Compensation Pseudomeasurement Filter (BCPMF)를 사용하였다. 병렬 계산의 이점을 위해 Two Stage Kalman Filter 형태를 추가적으로 적용하였다. 이 기법들을 사용하여 TBCPMF 3DVT-KC 필터를 제안하였고 시뮬레이션을 통해 성능을 확인하였다.

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles (UAVs). In this paper, aiming to address the control problem of maneuvering target tracking and obstacle avoidance, an online path planning approach for UAV is developed based on deep reinforcement learning. Through end-to-end learning powered by neural networks, the proposed approach can achieve the perception of the environment and continuous motion output control. This proposed approach includes: (1) A deep deterministic policy gradient (DDPG)-based control framework to provide learning and autonomous decision-making capability for UAVs; (2) An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning; and (3) An algorithm of task-decomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV’s control model built based on MN-DDPG. The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV’s flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.

UAV Maneuvering Target Tracking in Uncertain Environments Based on Deep Reinforcement Learning and Meta-Learning

This paper combines deep reinforcement learning (DRL) with meta-learning and proposes a novel approach, named meta twin delayed deep deterministic policy gradient (Meta-TD3), to realize the control of unmanned aerial vehicle (UAV), allowing a UAV to quickly track a target in an environment where the motion of a target is uncertain. This approach can be applied to a variety of scenarios, such as wildlife protection, emergency aid, and remote sensing. We consider a multi-task experience replay buffer to provide data for the multi-task learning of the DRL algorithm, and we combine meta-learning to develop a multi-task reinforcement learning update method to ensure the generalization capability of reinforcement learning. Compared with the state-of-the-art algorithms, namely the deep deterministic policy gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3), experimental results show that the Meta-TD3 algorithm has achieved a great improvement in terms of both convergence value and convergence rate. In a UAV target tracking problem, Meta-TD3 only requires a few steps to train to enable a UAV to adapt quickly to a new target movement mode more and maintain a better tracking effectiveness.

Polynomial least squares multiple-model estimation: simple, optimal, adaptive, practical

This paper presents a polynomial least squares (LS) method of overcoming numerous weaknesses and deficiencies in the interacting multiple model (IMM), which comprises multiple Kalman filter models as the state-of-the-art algorithm for tracking maneuvering targets. The paper also addresses several polynomial LS misunderstandings and flaws applied in econometrics. These aims are achieved by first uniquely deriving a very simple version of conventional LS, which fits discrete deterministic data by minimizing the sum of squared deviations of data from an assumed polynomial (often called data-fitting). The contrasting contemporary polynomial LS method filters out corrupting statistical noise from already existing known polynomials, as in target tracking. Contemporary polynomial LS is developed from the conventional LS method derived by Gauss. Polynomial LS conventionally applied to data-fitting and contemporarily applied to noise filtering are shown to be different problems that are not to be conflated as done in econometrics, where data-fitting is described as regression using a 1st degree polynomial called ordinary LS. Most significantly, analogous to the IMM, the polynomial LS multiple model (LSMM) is derived. Contrary to the IMM, which is not optimized; the LSMM is optimized with a trade-off between the statistical variance and the deterministic bias-squared to minimize the mean-square-error (variance plus bias-squared). A sequence of optimal LSMMs matched to accelerations covering the spectrum of acceleration between zero and the assumed maximum are derived and applied in designing an adaptive algorithm. Results demonstrate improved target tracking and accuracy by the LSMM of maneuvering targets compared with published IMM performances.

Performance comparison of CKF and UKF in passive state noise of UAV

Interacting multiple model (IMM) is widely used in target tracking, and is recognized as the most effective method for maneuvering target tracking. In order to remove the motion state noise in UAV passive location, this paper uses the idea of target tracking algorithm for reference. By setting the observation station data points in advance, introducing detection factors into CV and CT models, and then constructing measurement equations through geometric kinematics and fusing IMM algorithm, the filtering performance of IMM-CKF-CV-CT and IMM-UKF-CV-CT under typical s-maneuver in two-dimensional plane is studied. The simulation results of MATLAB show that IMM-CKF-CV-CT has higher accuracy and stronger stability than IMM-UKF.