The Resolution Performance of Range Estimators

The theoretical basis and methods of implementation of a moment algorithm for the range separation estimation of two closely spaced point targets are presented. Moment estimation and noise filtering techniques introduced here result in a considerable improvement over Baum's algorithm. The error bounds are established and it is shown that the spectral moment estimator exhibits optimum (zero bias, minimum variance) performance when the target separation normalized to the standard deviation of the Gaussian pulse is 2?1.5. Monte Carlo simulation is performed to verify the approximations made and to demonstrate the feasibility of the working models.     

基于重构算法的一种微弱目标检测后处理方法

 重点研究了形态学重构算法在微弱目标检测和跟踪中的应用，主要体现在对原始图像的背景估计过程和检测后处理过程。对于经过背景估计和二值化后的图像，若直接进行跟踪算法，由于虚警点数目太多，会严重影响跟踪算法的效率，同时也给跟踪结果造成了负面效应。因此，考虑在二值化之后，跟踪之前，引进后处理过程，以消除虚警点。在已知目标大致尺寸的情况下，选用不同的两个标记重构，同时消除小于目标的虚警点和大于目标的虚假目标。     

Improved multi-target tracking in clutter by PDA smoothing

An algorithm is presented for the recursive tracking of multiple targets in cluttered environment by making use of the joint probabilistic data association fixed-lag smoothing (JPDAS) techniques. It is shown that a significant improvement in the accuracy of track estimation of both nonmaneuvering and maneuvering targets may be achieved by introducing a time lag of one or two sampling periods between the instants of estimation and latest measurement. Results of simulation experiments for a radar tracking problem that demonstrate the effects of fixed-lag smoothing are also presented     

A maximum likelihood approach to data association

An approach is presented to data association (DA) problems for which measurements are independent from scan to scan. It is demonstrated that maximum likelihood (ML) estimation of target parameters may be efficiently implemented by an EM iterative scheme. The algorithm is applied to multitarget trajectory estimation of constant-velocity targets from passive (bearing-only) sensors     

Sequential Piecewise Recursive Filter for GPS Low-Dynamics Navigation

The design, implementation, and performance of a real-time estimation algorithm, referred to in this paper as the sequential piecewise recursive (SPWR) algorithm, for the global-positioning system (GPS) low-dynamics navigation system is described. The SPWR algorithm for this application was implemented in single precision arithmetic (32 bit, floating point). Numerical results are presented covariance and filter gains at a slower rate than the state measurement update, and it uses U-D factor formulation to perform covariance computations. The SPWR algorithm saves real-time processing requirements without appreciable degradation of filter performance. Another important feature of the SPWR algorithm is that it incorporates pseudorange and delta-range data from each GPS satellite sequentially for navigation solution. The SPWR algorithm, for this application, was implemented in single precision arithmetic (32 bit, floating point). Numerical results are presented.     

地空协同防空目标抗差跟踪算法

针对现有系统误差配准算法以已知系统误差变化模型为前提条件、相应的目标状态估计易受系统误差配准结果影响等不足之处,在机载雷达与地基雷达协同防空预警体系下,对系统误差存在情况下的目标跟踪问题进行了研究,并提出了有效的地空协同防空目标抗差跟踪算法。仿真结果表明所提算法可得到无偏、稳定、有效的目标状态估计,并且相对于系统误差目标状态联合估计算法,所提算法计算量小,对系统误差变化有很强的鲁棒性,可适应实际工程应用中可能出现的异常情况,为后续决策提供稳定有效的目标信息。     

组合式快速JPDA算法

基于有序状态空间搜索方法,定义联合事件为问题节点,将多目标数据关联问题求解转化为状态空间问题求解。定义联合数据关联概率函数对数表达为节点的估价函数,减少扩展节点数,迅速产生和搜索Ｎ个最大联合概率事件。给出快速计算Ｎ个最大联合概率事件的ＪＰＤＡ公式,并提出一种确定新生节点是否是已生成节点的简便方法。算法的搜索次数不随回波数增加而增加,有效地解决了传统ＪＰＤＡ算法的实时性问题,兼有贝叶斯方法和非贝叶斯方法的各自优点。     

A measurement-driven adaptive probability hypothesis density filter for multitarget tracking

This paper studies the dynamic estimation problem for multitarget tracking. A novel gating strategy that is based on the measurement likelihood of the target state space is proposed to improve the overall effectiveness of the probability hypothesis density(PHD) filter. Firstly, a measurement-driven mechanism based on this gating technique is designed to classify the measurements. In this mechanism, only the measurements for the existing targets are considered in the update step of the existing targets while the measurements of newborn targets are used for exploring newborn targets. Secondly, the gating strategy enables the development of a heuristic state estimation algorithm when sequential Monte Carlo(SMC) implementation of the PHD filter is investigated, where the measurements are used to drive the particle clustering within the space gate.The resulting PHD filter can achieve a more robust and accurate estimation of the existing targets by reducing the interference from clutter. Moreover, the target birth intensity can be adaptive to detect newborn targets, which is in accordance with the birth measurements. Simulation results demonstrate the computational efficiency and tracking performance of the proposed algorithm.     

Fast-Adapting Matrix Filtering

Implementation and convergence of adaptive processors are of continuing interest. To this end, a fast-adapting, linear, general recursive matrix-filtering algorithm is described. Two complex xoefficients are optimized at each recursion. A signal and interference estimation procedure is included. Results for some low-resolution, frequency domain beamformer trials with two targets, and using a single sample set, are presented. Considerable adaptive improvement occurs, along with target resolution, compared with a correlation beamformer. The algorithm shows rapid and direct convergence.     

Multiple target angle tracking using sensor array outputs

The use of the output of an array of sensors to track multiple independently moving targets is reported. The output of each sensor in the array is the sum of signals received from each of the targets. The results of direction-of-arrival estimation by eigenvalue analysis are extended to derive a recursive procedure based on a matrix quadratic equation. The solution of this matrix quadratic equation is used to provide updated target positions. A linear approximation method for estimating the solution of the matrix equation is presented. The algorithm is demonstrated by the simulated tracking of two targets. The main advantage of the algorithm is that a closed-form solution for updating the target angle estimates has been obtained. Also, its application is straightforward, and the data association problem due to uncertainty in the origin of the measurements is avoided. However, it requires the inversion of an N×N as well as other linear operations, so that the computational burden becomes substantial as N becomes very large     

一种单传感器实时系统误差配准算法

针对运动单传感器系统误差配准问题进行了研究,提出了一种基于位置未知固定目标的单传感器实时系统误差配准算法。算法利用传感器对固定目标的两时刻量测值,构建包含传感器系统误差的等效系统状态及其状态方程与量测方程,并基于扩展卡尔曼滤波技术实现了利用位置未知的固定目标对传感器系统误差的实时精确滤波估计。蒙特卡洛仿真结果验证了算法的有效性,具有对系统误差的稳定估计性能、快速的滤波收敛能力、较高的系统误差配准精度以及较强的工程实用性。     

On Computing the Cramer-Rao Bound for a Random Signal

The Cramer-Rao bound for estimating parameters of a random signal is obtained using a sampled data approach. A numerical example is presented to illustrate the result. In two limiting cases, it is shown that this bound converges to the well-known bounds for the point and Rayleigh targets.     

Precision large scale air traffic surveillance using IMM/assignmentestimators

We present the development and implementation of a multisensor-multitarget tracking algorithm for large scale air traffic surveillance based on interacting multiple model (IMM) state estimation combined with a 2-dimensional assignment for data association. The algorithm can be used to track a large number of targets from measurements obtained with a large number of radars. The use of the algorithm is illustrated on measurements obtained from 5 FAA radars, which are asynchronous, heterogeneous, and geographically distributed over a large area. Both secondary radar data (beacon returns from cooperative targets) as well as primary radar data (skin returns from noncooperative targets) are used. The target IDs from the beacon returns are not used in the data association. The surveillance region includes about 800 targets that exhibit different types of motion. The performance of an IMM estimator with linear motion models is compared with that of the Kalman filter (KF). A number of performance measures that can be used on real data without knowledge of the ground truth are presented for this purpose. It is shown that the IMM estimator performs better than the KF. The advantage of fusing multisensor data is quantified. It is also shown that the computational requirements in the multisensor case are lower than in single sensor case, Finally, an IMM estimator with a nonlinear motion model (coordinated turn) is shown to further improve the performance during the maneuvering periods over the IMM with linear models     

基于红外视觉/激光雷达融合的目标识别与定位方法

近年来,基于可见光图像的目标识别在无人车感知领域得到了广泛应用.然而,可见光图像目标识别无法应用于弱光和黑暗环境.针对于此,提出了一种基于红外视觉/激光雷达融合的目标识别与定位算法.首先,通过基于颜色迁移的数据增强训练方法,提高了红外目标识别算法的泛化性能.继而,提出了一种基于激光雷达修正的单目深度估计方法,通过视觉图...     

Multiple moving target feature extraction for airborne HRR radar

This study considers the clutter suppression and feature extraction of multiple moving targets for airborne high range resolution (HRR) phased array radar. To avoid the range migration problems that occur in the HRR radar data, we divide each HRR profile into nonoverlapping low range resolution segments. No information is lost due to the division and hence no loss of resolution occurs. We show how to use a vector auto-regressive filtering technique to suppress the clutter. Then a relaxation-based parameter estimation algorithm is presented for multiple moving target feature extraction. Numerical results are given to demonstrate the effectiveness of the algorithm     

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.     

Tracking of crossing targets with imaging sensors

The authors present an algorithm for the tracking of crossing targets using the centroid measurement and the centroid offset measurement of the distributed image formed by the targets. The measurements are obtained by a forward-looking infrared (FLIR) imaging sensor. The joint probabilistic data association merged-measurement coupled filter (JPDAMCF) is used for state estimation which performs filtering in a coupled manner for the targets with common measurements. Two filters are examined: one assuming the displacement noise white and the other one modeling it correctly as autocorrelated. The latter is shown to yield substantially better performance. The proposed algorithm demonstrates the usefulness of the JPDAMCF for tracking crossing targets in combination with the models for the centroid and offset measurements. Even though the centroid offset measurement requires more computations and a more complex model for estimation, it yields significantly better results if the filter accounts for its colored measurement noise     

基于傅里叶变换的航迹对准关联算法

研究了在组网雷达存在系统误差情况下的目标航迹关联问题,理论分析了雷达系统误差对目标航迹的影响,并将该影响表示为目标航迹的旋转和平移量。在此基础上,提出了一种基于傅里叶变换的系统误差配准前航迹对准关联算法,该算法将组网雷达的航迹数据看做为一种整体信息,采用傅里叶变换理论来估计和补偿组网雷达目标航迹数据到融合中心航迹数据的相对旋转量和平移量,将雷达网中雷达上报的目标航迹数据对准到融合中心,从而不依赖于估计雷达网系统误差,实现了误差配准前的航迹准确关联,能够为后端的系统误差配准提供可靠的关联目标航迹数据。     

基于动力学模型的再入弹道估计方法研究

对再入飞行器进行跟踪,获取目标弹道的精确估计具有十分重要的现实意义。飞行器在再入段的弹道可用较为精确的动力学模型描述,并利用UKF对目标的弹道进行估计,与3RR.几何法解算结果进行比对。仿真结果表明,对于弹道式再入飞行器,基于BRV-Exp模型的弹道估计精度比3RR.法具有明显改善;对于机动式再入飞行器,基于MRV-Wiener模型的估计精度只是略微优于3RR.法的弹道估计精度。