杂波下强机动目标跟踪自适应数据关联算法

杂波下的机动目标跟踪的综合概率数据关联(IPDA)算法是在概率数据关联(PDA)算法的思想基础上引入目标存在及可观测概率所形成的.本文进一步通过引入自适应调整因子,提出了针对强机动目标跟踪的自适应IPDA算法(CIPDA),并通过仿真论证,与传统的IPDA相比,CIPDA提高了对强机动目标跟踪的稳定性和精确度.     

On the sensor order in sequential integrated probability data association filter

The processing order of sensors with different detection probabilities and in different clutter densities in a multi-sensor system is investigated in this paper. A sequential implementation of the integrated probability data association (IPDA) algorithm under random set framework is derived. Under the assumptions of different detection probabilities and different clutter densities of individual sensor in a multi-sensor system, we reach the conclusion that the sequential IPDA filter depends on the order analyzing the target existence probability of varying sensor orders. Moreover, we obtain the optimal order of sensors for the sequential IPDA filter in terms of maximizing the target existence probability. The conclusions are demonstrated by simulation results.     

A general smoothing equation for signal estimation using randomly delayed observations in the correlated signal-noise case

This paper treats the least-squares linear smoothing problem for signal estimation using measurements contaminated by additive white noise correlated with the signal, with stochastic delays. We derive a general smoothing equation which is applied to obtain specific smoothing algorithms, which are referred in the signal estimation literature as fixed-point, fixed-interval, and fixed-lag smoothing. Using an innovation approach, the general smoothing equation is derived without requiring the whole knowledge of the state-space model generating the signal, but only covariance information of the signal and the observation noise, as well as the delay probabilities.     

Signal estimation with multiple delayed sensors using covariance information

Recursive filtering and smoothing algorithms to estimate a signal from noisy measurements coming from multiple randomly delayed sensors, with different delay characteristics, are proposed. To design these algorithms an innovation approach is used, assuming that the state-space model of the signal is unknown and using only covariance information. To measure the precision of the proposed estimators formulas to calculate the filtering and smoothing error covariance matrices are also derived. The effectiveness of the estimators is illustrated by a numerical simulation example where a signal is estimated using observations from two randomly delayed sensors having different delay properties.     

Stability evaluation and track life of the PDAF for tracking inclutter

The authors present an effective approach of a hybrid nature to the nonsimulation performance evaluation of the probabilistic data association filtering (PDAF) method for tracking in clutter. In this approach, a continuous-valued covariance, which is a function of a discrete-valued random variable (the number of validated measurements), is used to characterize the tracking errors in an average sense. This covariance can be calculated offline recursively from a modified Riccati equation, which can be obtained by replacing the measurement-dependent terms in the original stochastic equation with their conditional expectations. This approach has the merit that it yields a quantification of the transients of tracking divergence as well as substantially better accuracy than previous work. Such an approach is particularly suitable for stability evaluation of tracking filters. In addition, a quantitative study of the track-life problem is made in which the number of validated measurements plays a central role     

Covariance recovery from a square root information matrix for data association

Data association is one of the core problems of simultaneous localization and mapping (SLAM), and it requires knowledge about the uncertainties of the estimation problem in the form of marginal covariances. However, it is often difficult to access these quantities without calculating the full and dense covariance matrix, which is prohibitively expensive. We present a dynamic programming algorithm for efficient recovery of the marginal covariances needed for data association. As input we use a square root information matrix as maintained by our incremental smoothing and mapping (iSAM) algorithm. The contributions beyond our previous work are an improved algorithm for recovering the marginal covariances and a more thorough treatment of data association, now including the joint compatibility branch and bound (JCBB) algorithm. We further show how to make information theoretic decisions about measurements before actually taking the measurement, therefore allowing a reduction in estimation complexity by omitting uninformative measurements. We evaluate our work on simulated and real-world data.     

Tracking in clutter with strongest neighbor measurements. I.Theoretical analysis

When tracking a target in clutter, a measurement may have originated from either the target, clutter, or some other source. The measurement with the strongest intensity (amplitude) in the neighborhood of the predicted target measurement is known as the “strongest neighbor” (SN) measurement. A simple and commonly used method for tracking in clutter is the so-called strongest neighbor filter (SNF), which uses the SN measurement at each time as if it were the true one. The paper deals with tracking in clutter with the SN measurements. It presents analytic results, along with useful comments, for the SN measurement and the SNF, including the a priori and a posteriori probabilities of data association events, the conditional probability density functions and the covariance matrices of the SN measurement, and various mean-square-error matrices of state prediction and state update. These results provide valuable insight into the problem of tracking in clutter and theoretical foundation for the development of improved tracking algorithms, for performance analysis, prediction, and comparison of tracking with the SN measurements, and for solving some important detection-tracking problems, such as the optimal determination of the detection threshold and gate size     

Robust estimation of covariance matrices

Data manipulations which increase the robustness and accuracy of estimators of covariance parameters by using the innovations correlation approach are considered. The procedures are especially useful for improving estimates of process-noise covariance parameters for slowly varying systems when measurement noise is large. The innovations correlate covariance estimation technique developed by P.R. Belanger (1974) is extended to the case where process noise is weak in magnitude compared to measurement noise. Belanger's method exploits the linear relationship between the desired noise covariance parameters and the correlations of the innovation sequence of a suboptimal Kalman filter to formulate a least-squares algorithm. The estimates of the process-noise covariance parameters are improved by low-pass prefiltering and downsampling the data before applying the least-squares innovations correlation algorithm. Results for a single-output, linear time-invariant system are stated, and the subsequent analysis treats only this case     

杂波协方差矩阵结构的融合估计方法

针对特定杂波背景下的最优或次优杂波协方差矩阵估计方法难以适应过渡杂波环境的问题,提出协方差矩阵结构的融合估计方法,通过调整参数涵盖现有的3种杂波协方差矩阵估计方法,并分析所提出方法对应的自适应归一化匹配滤波器的自适应特性.其次,确定了控制参数的经验公式,经验公式符合数值结果.最后,从估计精度、恒虚警率特性和检测性能3个方面对所提出方法和已有方法进行对比分析.仿真结果表明,在过渡杂波环境中,所提出方法的精度更高、检测效果更好,对实际杂波非高斯程度时空渐变性具有较强的适应能力.     

Detection thresholds for tracking in clutter--A connection between estimation and signal processing

In the Kalman-Bucy filter and other trackers, the dependence of tracking performance upon the quality of the measurement data is well understood in terms of the measurement noise covariance matrix, which specifies the uncertainty in the values of the measurement inputs. The measurement noise and process noise covariances determine, via the Riccati equation, the state estimation error covariance. When the origin of the measurements is also uncertain, one has the widely studied problem of data association (or data correlation), and tracking performance depends critically on signal processing parameters, primarily the probabilities of detection and false alarm. In this paper we derive a modified Riccati equation that quantifies (approximately) the dependence of the state error covariance on these parameters. We also show how to use a receiver operating characteristic (ROC) curve in conjunction with the above relationship to determine the detection threshold in the signal processing system that provides measurements to the tracker so as to minimize tracking errors. The approach presented in this paper provides a feedback mechanism from the information processing (tracking) subsystem to the signal processing subsystem so as to optimize the overall performance in clutter.     

利用直接数据域的双基机载雷达地面动目标检测方法

双基机载雷达的杂波存在严重的距离依赖性,从距离上获得的样本不能够准确估计杂波加噪声协方差矩阵,从而导致空时自适应处理(STAP)的杂波抑制性能严重下降。针对该问题,提出了利用直接数据域(DDD)的双基机载雷达地面动目标检测方法。因为DDD算法是在单个距离门对目标滤除后进行空时平滑来获得足够多的样本,所以不需要对双基雷达杂波距离依赖性进行补偿。仿真实验验证了该方法的有效性。     

On smoothing in linear discrete systems with time delays†

An algorithm is presented for optimal linear fixed-point and fixed-lag smoothing in non-stationary linear discrete systems with multiple time delays. Relations derived previously in the problem of filtering are used directly to obtain the fixed-point and fixed-lag smoothing filters. The smoothing error covariance matrix is obtained via a recursive matrix equation. For the particular case of systems without delay terms, the algorithm defines new smoothing procedures.     

基于高斯过程的多机动扩展目标跟踪

针对杂波环境下多机动扩展目标跟踪问题, 提出一种基于高斯过程的变结构多模型联合概率数据关联方法.首先, 采用期望模型扩展方法构建自适应模型集, 并对各个扩展目标状态进行初始化.其次, 基于高斯过程建立联合跟踪门以选择有效量测, 形成联合关联矩阵.然后, 拆分联合关联矩阵得到可行关联矩阵并求解关联事件概率.最后, 利用联合概率数据关联滤波器更新各个扩展目标的状态和协方差, 并将更新的状态进行融合, 得到最终的状态估计.仿真验证了所提方法的有效性.     

A Bayesian solution and its approximations to out-of-sequence measurement problems

Target tracking using delayed, out-of-sequence measurements is a problem of growing importance due to an increased reliance on networked sensors interconnected via complex communication network architectures. In such systems, it is often the case that measurements are received out-of-time-order at the fusion center. This paper presents a Bayesian solution to this problem and provides approximate, implementable algorithms for both cluttered and non-cluttered scenarios involving single and multiple time-delayed measurements. Such an approach leads to a solution involving the joint probability density of current and past target states.In contrast, existing solutions in the literature modify the sensor measurement equation to account for the time delay and explicitly deal with the resulting correlations that arise in the process noise and current target state. In the Bayesian solution proposed in this paper, such cross correlations are treated implicitly. Under linear Gaussian assumptions, the Bayesian solution reduces to an augmented state Kalman filter (AS-KF) for scenarios devoid of clutter and an augmented state probabilistic data association filter (AS-PDA) for scenarios involving clutter. Computationally efficient versions of AS-KF and AS-PDA are considered in this paper. Simulations are presented to evaluate the performance of these solutions.     

基于杂波强度在线估计的多目标跟踪算法

针对多目标跟踪中的未知杂波强度,提出了基于熵分布的杂波强度在线估计算法.利用有限混合模型对未知杂波强度建模,将仅依赖于混合权重的熵分布作为混合参数的先验;利用拉格朗日乘子法推导了混合权重在极大后验意义下的在线估计公式;以随机近似过程为在线估计策略,推导了基于缺失数据的分量均值和方差的在线估计公式.仿真结果表明,基于熵分布的杂波强度在线估计算法改进了概率假设密度滤波器在多目标跟踪中的性能.     

小样本机载分布式雷达空时自适应处理方法

针对机载分布式雷达地杂波和干扰抑制的问题,提出一种基于小样本协方差重构的机载分布式雷达空时自适应处理（space-time adaptive processing,STAP）方法。该方法根据分布式雷达接收的杂波与干扰具有相关性不同的特点,首先利用小样本估计出各子雷达的杂波空时协方差矩阵和干扰全空域协方差矩阵,然后通过块对角延拓和时域拓展方法得到完整的空时协方差矩阵。所提方法利用全部空域自由度,在有效抑制杂波的同时抑制较多数量的干扰,克服了理想协方差矩阵估计时所面临的独立同分布（independent and identically distributed,i.i.d.）样本严重不足的问题。理论分析和仿真实验验证了所提方法的有效性。     

Design and feasibility study of an integrated pointing device apparatus for individuals with spinal cord injury

Despite the commercial availability of numerous computer-pointing devices, many severely disabled individuals still rely on customized equipment to operate computers. This study presents a novel Integrated Pointing Device Apparatus (IPDA) that integrates numerous commercial pointing devices. The novel IPDA, which complies with a standard USB 1.1 interface, is compatible with most tested computer-pointing devices and flexibly integrates commercial computer devices, tailoring them to suit individual needs. By using simple integrated circuit design and low-cost electronic components, this low-cost apparatus is easily maintained. The feasibility of the IPDA was evaluated by four subjects with high-level cervical (C4-5) spinal cord injury (SCI). Participants performed normal move-and-click and drag-and-drop tasks typically performed by computer pointing devices. Each participant not able to use a traditional computer mouse or trackball were able to operate a computer adequately with the IPDA and three including one operating a trackball with his chin, operated computers easily and smoothly. This feasibility study showed that the IPDA effectively integrates commercial pointing devices, thereby providing the possibility for some people with SCI to obtain computer operability. This study demonstrated the advantages of flexibility, low cost, and acceptable efficiency of the novel IPDA.     

针对地球自转影响的星载正侧面阵雷达杂波补偿方法

对于星载正侧面阵雷达,地球自转会导致地面杂波的多普勒分布发生展宽,使得杂波协方差矩阵估计不准确,致使空时自适应处理(STAP)的杂波抑制性能下降.针对该问题,本文提出了一种杂波补偿方法.该方法利用星载雷达的轨道参数对地球自转分量进行估计,然后对杂波数据进行多普勒补偿.该方法能够减轻由于地球自转引起的杂波多普勒分布展宽,...     

Spatial smoothing based methods for direction-of-arrival estimation of coherent signals in nonuniform noise

Spatial smoothing techniques have been widely used to estimate the directions-of-arrival (DOAs) of coherent signals. However, in general these techniques are derived under the condition of uniform white noise and, therefore, their performance may be significantly deteriorated when nonuniform noise occurs. This motivates us to develop new methods for DOA estimation of coherent signals in nonuniform noise in this paper. In our methods, the noise covariance matrix is first directly or iteratively calculated from the array covariance matrix. Then, the noise component in the array covariance matrix is eliminated to achieve a noise-free array covariance matrix. By mitigating the effect of noise nonuniformity, conventional spatial smoothing techniques developed for uniform white noise can thus be employed to reconstruct a full-rank signal covariance matrix, which enables us to apply the subspace-based DOA estimation methods effectively. Simulation results demonstrate the effectiveness of the proposed methods.     

Knowledge-based target detection in compound Gaussian clutter with inverse Gaussian texture

This paper addresses the problem of adaptive detection of radar targets embedded in heterogeneous compound-Gaussian clutter environments. Based on the Bayesian theory, a priori knowledge of clutter is utilized to improve detection performance. The clutter texture is modeled by the inverse Gaussian distribution to describe the heavy-tailed clutter. Furthermore, clutter's heterogeneity results in insufficient secondary data, and the inverse complex Wishart distribution is exploited to model the speckle covariance matrix. Based on a priori distributions of clutter, a novel detector without using secondary data is derived via the generalized likelihood ratio test (GLRT). Monte Carlo experiments are performed to evaluate the detection performance of the proposed detector. Experimental results illustrate that the proposed detector outperforms its competitors in scenarios with limited secondary data.