Multiradar Tracking System Using Radial Velocity Measurements

Multiradar tracking using both position and radial velocity measurements is discussed. The measurement of two or more different radial velocity components allows the calculation of rectangular velocity components. The measurement noise of the velocity components is filtered using a Kalman filter in the same way as the Cartesian position components. Before the conversion of velocity components from radial to Cartesian coordinates, the radial velocities are aligned on a time scale to account for the time shift of the radar measurements. In order to compare multiradar tracking system performance with and without radial velocity, some simulation tests have been performed for typical paths. The simulation results show a significant improvement when radial velocity is used for tracking.     

HRR Detector for Slow-Moving Targets in Sea Clutter

The radar detection of targets in the presence of sea clutter has historically relied upon the radial velocity of targets with respect to the radar platform either by exploiting the relative target Dopplers (for targets with sufficient radial velocity) or by discerning the paths targets traverse from scan to scan. For targets with little to no radial velocity component, though, it can become quite difficult to differentiate targets from the surrounding sea clutter. This paper addresses the detection of slow-moving targets in sea clutter using a high resolution radar (HRR) such that the target has perceptible extent in range. Under the assumption of completely random sea clutter spikes based on an epsiv-contaminated mixture model with the signal and clutter powers known, optimal detection performance results from using the likelihood ratio test (LRT). However, for realistic sea clutter, the clutter spikes tend to be a localized phenomenon. Based upon observations from real radar data measurements, a heuristic approach exploiting a salient aspect of the idealized LRT is developed which is shown to perform well when applied to real measured sea clutter.     

密集杂波环境下的数据关联快速算法

基于联合概率数据互联（JPDA）的思想,提出了一种新的数据关联快速算法（Fast Al-gorithm for Data Association,简称FAFDA算法）.该方法不需象在最优JPDA算法中那样生成所有可能的联合互联假设,因而具有计算量小,易于工程实现的特点。仿真结果表明,与最优JPDA算法相比,FAFDA算法的跟踪性能令人满意,并且在密集杂波环境下可实时、有效地跟踪100批次以上的目标。     

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.     

基于OTHR与纯角度传感器群数据关联的模式辨识与定位精度提升

 天波超视距雷达(OTHR)目标跟踪面临着"三低"(低检测概率、低数据率和低测量精度)和"多径"(多条传播路径)的挑战,因此传播模式的准确辨识与目标定位精度提升是改善跟踪能力的关键。首先利用纯角度传感器群获得目标地理位置的初步估计,然后采用极大似然估计建立了OTHR的传播模式和杂波模式的辨识规则,进而利用最小方差估计准则实现OTHR和纯角度传感器群的量测融合。仿真结果表明,此算法的模式辨识正确率很高,能明显提升方位角的测量精度,但是不能明显提升径向距的精度。     

基于点集合并的修正Hough变换TBD算法

在低信噪比条件下,基于Hough变换的检测前跟踪算法是进行强杂波背景下目标航迹检测的一种手段。本文针对Hough变换后一个目标产生多条可能航迹以及航迹内可能存在杂波点的问题,提出了一种基于能量最大点和点集合并的修正Hough变换检测前跟踪算法。该算法利用量测点时序、能量信息及目标速度先验信息对Hough变换后点迹进行关联和剔除,能够有效的对目标原始航迹进行回溯。针对高斯噪声背景下的飞行目标,仿真结果表明该算法能够对微弱目标进行有效检测,在目标数目、杂波密度、信噪比发生变化的条件下仍能保持较高的检测概率。     

Automatic detection and tracking of a small surface watercraft in shallow water using a high-frequency active sonar

A high-frequency (HF) active sonar can be used to detect and track a small fast surface watercraft in shallow water based on the evolution of the watercraft wake observed in the sonar image sequence. An automatic detection and tracking (ADT) algorithm is described for this novel application. For each ping, the measurement of the target's polar position consists of 2 steps. First, the target bearing is estimated by finding the direction of arrival of the cavitation noise emitted by the watercraft. Then range measurements are extracted from the range profile (constant-angle cut of the sonar image) at the estimated target bearing. Range normalization and clutter map processing are used to reduce the number of false measurements. Estimates of the target's Cartesian position and velocity are updated at the sonar pulse repetition rate using the Kalman filter with debiased consistent converted measurements and nearest neighbour data association. The proposed algorithm is demonstrated using real data.     

Highest density gates for target tracking

The problem of forming validation regions or gates for new sensor measurements obtained when tracking targets in clutter is considered. Target dynamics and measurement characteristics are modeled with, possible non-Gaussianities or nonlinearities, so that some degree of approximation is usually required in the computation of the filtering densities for the target position and predictive densities for future measurements. Highest density gates (HDGs) are proposed as summaries of the predictive densities. These gates are constructed numerically, via simulation from the filtering density approximation. The algorithm results in gates that are “exact” (up to numerical accuracy) regardless of the approximation used for the filtering density. That is, given an approximation to the filtering density, the gating procedure accounts for all further nonlinearities and non-Gaussianities. Numerical example show that when the predictive density is markedly non-Gaussian, HDGs offer advantages over the more common rectangular and ellipsoidal gates     

利用幅值信息的超视距雷达多路径概率数据互联算法

天波超视距雷达是通过电离层反射实现超视距广域监视的,其地理坐标系下的量测方程存在强非线性,同时由于电离层的不同分层,造成了多路径传播的严重问题,即同时存在多个量测模型。多路径概率数据互联(MPDA)滤波器将坐标配准与概率数据互联相结合,解决了超视距目标跟踪中的多路径传播问题,但在杂波环境下滤波跟踪精度不高。文中提出了一种基于信号幅值特征信息的MPDA算法(A-MPDA),当跟踪单一的、存在4种可能非线性量测的非机动目标时,仿真结果表明所提出的算法比标准MPDA有更好的跟踪精度。     

Mixture Reduction Algorithms for Point and Extended Object Tracking in Clutter

The Bayesian solution to the problem of tracking a target with measurement association uncertainty gives rise to mixture distributions, which are composed of an ever increasing number of components. To produce a practical tracking filter, the growth of components must be controlled by approximating the mixture distribution. Two mixture reduction schemes (a joining algorithm and a clustering algorithm) have been derived for this purpose. If significant well spaced mixture components are present, these techniques can provide a useful improvement over the probabilistic data association filter (PDAF) approach, which reduces the mixture to a single Gaussian component at each time step. For the standard problem of tracking a point target in uniform random clutter, a Monte Carlo simulation study has been employed to identify the region of the problem parameter space where significant performance improvement is obtained over the PDAF. In the second part of this paper, the formal Bayesian filter is derived for an extended target consisting of an array of measurement sources with association uncertainty. A practical multiple hypothesis filter is implemented using mixture reduction and simulation results are presented.     

A Probabilistic Strongest Neighbor Filter Algorithm for m Validated Measurements

A new form of the probabilistically strongest neighbor filter (PSNF) algorithm taking into account the number of validated measurements is proposed. The probabilistic nature of the strongest neighbor (SN) measurement in a cluttered environment is shown to be varied with respect to the number of validated measurements. Incorporating the number of validated measurements into design of the PSNF produces a consistent and cost effective data association method. Simulation studies show that the new filter is less sensitive to the unknown spatial clutter density and is more reliable for practical target tracking in nonhomogeneous clutter than the existing PSNF. It has similar performances to the probabilistic data association filter amplitude information (PDAF-AI) with much less computational complexities.     

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     

Use of the radial velocity measurement in target tracking

A new sequential filtering algorithm that incorporates the radial velocity measurement into a Kalman filter, in the presence of correlated range and radial velocity measurement errors, is presented. An analysis is given concerning its asymptotic behavior on the basis of analysis of its stochastic controllability and observability. The simulation results verify the analysis and show that the new algorithm is superior to the conventional extended Kalman filter (EKF) and close to an ideal filter.     

复合高斯杂波中距离扩展目标的迭代近似GLRT检测器

 研究了结构化的复合高斯杂波(CGC)背景中距离扩展目标自适应检测问题。针对异质杂波背景中的近似广义似然比检验(AGLRT-HTG)检测器应用于CGC背景中时存在一定的信杂比损失问题,结构化的复合高斯杂波采用自回归过程建模,结合近似广义似然比检验(AGLRT)方法和迭代估计思想,提出了CGC背景中距离扩展目标的迭代近似广义似然比检测器(RAGLRT-CGC)。从理论上分析了极限情况下RAGLRT-CGC虚警概率与检测门限关系的解析表达式。仿真结果表明,在CGC背景中,RAGLRT-CGC对不同多主散射点目标具有较好的鲁棒性,并且检测性能明显优于AGLRT-HTG。     

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.     

基于先验门限优化准则的探测阈值自适应选择

针对 2维测量和 4 -sigma确认门 ,把先验检测门限优化准则和修正 Riccati方程的解析近似表示相结合 ,得到了在瑞利起伏环境下使跟踪性能优化的信号探测阈值解析表示式 ,从而使在线求解自适应信号探测阈值能比较容易地实现。通过研究和仿真发现 :在滤波稳定阶段 ,本文给出的自适应信号检测门限方法的跟踪性能优于固定虚警率方法的跟踪性能 ;基于先验检测门限优化准则实现检测 -跟踪的联合优化要求信噪比要大于一定的门限 ,在瑞利起伏环境下 ,对 2维测量和 4 -sigma确认门 ,该门限为 1 .57     

基于相位相关的部分可辨编队精细起始算法

针对部分可辨条件下编队目标的精细起始难题,提出了一种基于相位相关的部分可辨编队精细起始算法。首先,采用基于坐标映射距离差分的快速群分割与基于编队中心点的预互联对雷达量测进行预处理;然后,利用图像匹配中相位相关特性,将相邻时刻编队结构进行补偿对准,解决了低目标发现概率情况下的编队结构对准问题;最后,采用增加虚拟量测并后验判决的方式,结合最近邻法做编队航迹精细互联,在填补航迹缺失、增加正确航迹的同时抑制虚假航迹的产生。经仿真验证,与修正的逻辑法、基于相对位置矢量的灰色编队精细起始算法相比,本文所提算法在提高航迹正确起始率、抑制虚假航迹方面性能优势显著,且对环境杂波与雷达精度具有较好的鲁棒性,对目标发现概率具有较好的适应性。     

Multi-Target Tracking in Clutter without Measurement Assignment

When tracking targets using radars and sonars, the number of targets and the origin of data is uncertain. Data may be false measurements or clutter, or they may be detections from an unknown number of targets whose possible trajectories and detection processes can only be described in a statistical manner. Optimal all-neighbor multi-target tracking (MTT) in clutter enumerates all possible joint measurement-to-track assignments and calculates the a posteriori probabilities of each of these joint assignments. The numerical complexity of this process is combinatorial in the number of tracks and the number of measurements. One of the key differences between most MTT algorithms is the manner in which they reduce the computational complexity of the joint measurement-to-track assignment process. We propose an alternative approach, using a form of soft assignment, that enables us to bypass this step entirely. Specifically, our approach treats possible detections of targets followed by other tracks as additional clutter measurements. It starts by approximating the a~priori probabilities of measurement origin. These probabilities are then used to modify the clutter spatial density at the location of the measurements. A suitable single target tracking (STT) filter then uses the modified clutter intensity for updating the track state. In effect, the STT filter is transformed into an MTT filter with a numerical complexity that is linear in the number of tracks and the number of measurements. Simulations show the effectiveness of this approach in a number of different multi-target scenarios.     

Detection of small objects in clutter using a GA-RBF neural network

Detection of small objects in a radar or satellite image is an important problem with many applications. Due to a recent discovery that sea clutter, the electromagnetic wave backscatter from a sea surface, is chaotic rather than purely random, computational intelligence techniques such as neural networks have been applied to reconstruct the chaotic dynamic of sea clutter. The reconstructed sea clutter dynamical system which usually takes the form of a nonlinear predictor does not only provide a model of the sea scattering phenomenon, but it can also be used to detect the existence of small targets such as fishing boats and small fragments of icebergs by observing abrupt changes in the prediction error. We applied a genetic algorithm (GA) to obtain an optimal reconstruction of sea clutter dynamic based on a radial basis function (RBF) neural network. This GA-RBF uses a hybrid approach that employes a GA to search for the optimum values of the following RBF parameters: centers, variance, and number of hidden nodes, and uses the least square method to determine the weights. It is shown here that if the functional form of an unknown nonlinear dynamical system can be represented exactly using an RBF net (i.e., no approximation error), this GA-RBF approach can reconstruct the exact dynamic from its time series measurements. In addition to the improved accuracy in modeling sea clutter dynamic, the GA-RBF is also shown to enhance the detectability of small objects embedded in the sea. Using real-life radar data that are collected in the east coast of Canada by two different radar systems: a ground-based radar and a satellite equipped with synthetic aperture radar (SAR), we show that the GA-RBF network is a reliable detector for small surface targets in various sea conditions and is practical for real-life search and rescue, navigation, and surveillance applications     

Stap using knowledge-aided covariance estimation and the fracta algorithm

In the airborne space-time adaptive processing (STAP) setting, a priori information via knowledge-aided covariance estimation (KACE) is employed in order to reduce the required sample support for application to heterogeneous clutter scenarios. The enhanced FRACTA (FRACTA.E) algorithm with KACE as well as Doppler-sensitive adaptive coherence estimation (DS-ACE) is applied to the KASSPER I & II data sets where it is shown via simulation that near-clairvoyant detection performance is maintained with as little as 1/3 of the normally required number of training data samples. The KASSPER I & II data sets are simulated high-fidelity heterogeneous clutter scenarios which possess several groups of dense targets. KACE provides a priori information about the clutter covariance matrix by exploiting approximately known operating parameters about the radar platform such as pulse repetition frequency (PRF), crab angle, and platform velocity. In addition, the DS-ACE detector is presented which provides greater robustness for low sample support by mitigating false alarms from undernulled clutter near the clutter ridge while maintaining sufficient sensitivity away from the clutter ridge to enable effective target detection performance