A Distributed CFAR Detector Based on Local Test Statistics

In order to improve detection performance of a distributed system, a new CFAR （Constant False Alarm Rate） detector is proposed based on local test statistics. In the scheme, each sensor transmits its test sample and a designated order statistic （OS） of its reference samples to the fusion center. At the fusion center, the global test statistic in the test cells is formed by the cell averaging algorithm （CA）, and the global test statistic in the reference cells is also formed by the CA. Comparing the ratio of the global test statistic of test samples and that of reference samples with a scaling factor, which is determined by probability of false alarm, to make the final decision. For a Rayleigh fluctuating target in Gaussian clutter of unknown level, its closed-form expressions for the false alarm probability and the detection probability are obtained in both homogeneous and nonhomogeneous background. The numerical analysis results show that the CFAR detector proposed in this paper has good detection performance.     

Two adaptive detectors for range-spread targets in non-Gaussian clutter

This paper addresses adaptive detection of range-spread target in spherically invariant random vector clutter. Based on the nonadaptive detectors of NSDD-GLRT and SDD-GLRT, two adaptive detectors named ANSDD-GLRT and ASDD-GLRT are devised by replacing the unknown normalized clutter covariance matrix with the sample covariance matrix based on the secondary data. The formulas of detection probability and false alarm probability are deduced. Moreover, the constant false alarm rate properties of both ...     

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.     

Joint range ambiguity resolving and multiple maneuvering targets tracking in clutter via MMPHDF-DA

It is difficult to track multiple maneuvering targets of which the number is unknown and time- varying, especially when there is range ambiguity. The random finite sets （RFS） based probability hypothesis density filter （PHDF） is an effective solution to the problem of multiple targets tracking. However, when tracking multiple targets via the range ambiguous radar, the problem of range ambiguity has to be solved. In this paper, a multiple model PHDF and data association （MMPHDF-DA） based method is proposed to address multiple maneuvering targets tracking with range ambiguous radar in clutter. Firstly, by introducing the turn rate of target and the discrete pulse interval number （PIN） as components of target state vector, and modeling the incremental variable of the PIN as a three-state Markov chain, the problem of multiple maneuvering targets tracking with range ambiguity is converted into a hybrid state filtering problem. Then, by implementing a novel ＂track-estimate＂ oriented association with the filtering results of the hybrid filter, target tracks are provided at each time step. Simulation results demonstrate that the MMPHDF-DA can estimate target state as well as the PIN simultaneously, and succeeds in multiple maneuvering target tracking with range ambiguity in clutter. Simulation results also demonstrate that the MMPHDF-DA can overcome the limitation of the Chinese Remainder Theorem for range ambiguity resolving.     

A New Distributed CFAR Processor Based On OSCA

A new OSCA CFAR processor using distributed sensors is presented in this paper, In the scheme, each sensor transmits its test sample and the designated order statistic （OS） of its surrounding observations to the fusion center, At the fusion center, the test samples and the order statistic quantities are combined with the cell average algorithm （CA） respectively to make the final decision. For a Rayleigh fluctuating target in Gaussian noise of unknown level, we obtain its closed-form expressions for the false alarm probability and the detection probability. The numerical results indicate that the detection performance of the proposed OSCA scheme is very close to that of the ideal centralized CFAR, and much better than other distributed CFAR processors,     

Vibration target detection and vibration parameters estimation based on the DPCA technique in dual-channel SAR

This paper proposes a new method based on the displaced phase center antenna(DPCA) technique for the detection and parameters estimation of vibration target in dual-channel synthetic aperture radar(SAR).The echo model of vibration target is established in dual-channel SAR according to the equivalent phase center principle.By accumulating the DPCA signal amplitude in the azimuth direction,we realize the vibration target detection in clutter and noise background.Through the analysis of DPCA signal amplitude and phase characteristics of vibration target,we then convert the vibration parameters estimation to estimation of a signal with the form of absolute value of a sine function,and the converting conditions is also given.Accordingly,an algorithm combining the Fourier transform with least squares is proposed for estimation.The simulation results show that the algorithm can estimate each vibration parameter precisely at a low signal-to-noise ratio.     

Cyclic autocorrelation based blind OFDM detection and identification for cognitive radio

Cognitive radio is considered as a promising technique to increase the utilization of limited spectral resource. The key issue in cognitive radio is to design a reliable spectrum sensing method that is able to detect the signal in the target channel as well as to recognize different signals. In this paper, focusing on classifying different OFDM signals, we propose a two-step detection and identification approach. The key parameters to separate different OFDM signals are the subcarrier spacing and guard interval. A simple but reliable peak detection method is adopted in the first step, while a peak searching method is used to determine the length of guard interval. Simulations are carried out in AWGN to verify the validation of the proposed method. It is shown that our method can satisfy the detection and identification requirement with a low false alarm probability.     

Optimal MAC based cooperative spectrum sensing in cognitive radio networks

This paper considers a cognitive radio (CR) system in non-ideal fading wireless channels and pro-poses cooperative spectrum sensing schemes based on coherent multiple access channels (MAC),serving as an alternative way to improve the cooperative spectrum sensing performance and provide space diversity for spec-trum sensing.Sufficient statistics are transmitted using a common channel from the secondary users (SUs) to a fusion center (FC) where the global decision is obtained.The optimal scaling factors of the proposed schemes are obtained by maximizing the detection probability under a target false alarm probability and a transmit power constraint.Because the proposed optimal MAC scheme has high computational complexity,a sub-optimal solu-tion based on maximization of the deflection coefficient (DC) is also proposed.Simulation results show that the proposed algorithms can significantly improve the spectrum sensing performance and approach the detection baseline.     

Novel parametric optimum processing method for airborne radar

In radar target detection, an optimum processor needs to automatically adapt its weights to the environment change. Conventionally, the optimum weights are obtained by substantial independently and identically distributed (i.i.d.) interference samplings, which is not always realistic in an inhomogeneous clutter background of airborne radar. The lack of i.i.d. samplings will inevitably lead to performance deterioration for optimum processing. In this paper, a novel parametric adaptive processing method is proposed for airborne radar target detection based on the modified Doppler distributed clutter (DDC) model with contribution of clutter's internal motion. It is different from the conventional methods in that the adaptive weights are determined by two parameters of DDC model, i.e., angular center and spread. A low-complexity nonlinear operators approach is also proposed to estimate these parameters. Simulation and performance analysis are also provided to show that the proposed method can remarkably redu     

New optimal algorithm of data association for multi-passive-sensor location system

In dense target and false detection scenario of four time difference of arrival (TDOA) for multi-passive-sensor location system, the global optimal data association algo- rithm has to be adopted. In view of the heavy calculation burden of the traditional optimal assignment algorithm, this paper proposes a new global optimal assign- ment algorithm and a 2-stage association algorithm based on a statistic test. Compared with the traditional optimal algorithm, the new optimal algorithm avoids the complicated operations for finding the target position before we calculate as- sociation cost; hence, much of the procedure time is saved. In the 2-stage asso- ciation algorithm, a large number of false location points are eliminated from can- didate associations in advance. Therefore, the operation is further decreased, and the correct data association probability is improved in varying degrees. Both the complexity analyses and simulation results can verify the effectiveness of the new algorithms.     

双基地雷达前向散射地表杂波统计特性分析

本文通过对杂波实测数据的处理,利用高斯分布模型、指数分布模型、幂指数分布模型及高阶AR等分布模型,得到了双基地雷达前向散射地表杂波概率分布和功率谱分布的数学模型;并从统计理论上解析地推导了杂波统计模型对目标检测性能的影响.基于估计的概率分布、功率谱分布数学模型及广义匹配滤波理论,实现了前向散射地表杂波的有效抑制与目标运动参数的精确估计,并利用实测数据进行了验证.     

基于MMPHDF的多机动目标联合检测、跟踪与分类算法

针对场景中存在新目标出现、旧目标消失(即目标数目变化)和密集杂波的复杂情形,利用多模型概率假设密度滤波器(MMPHDF)在多机动目标联合检测与跟踪上的优势,加入类别辅助信息,提出了一种多机动目标联合检测、跟踪与分类算法.该算法的基本思想是在MMPHDF中用属性向量扩展单目标状态向量,用位置和属性的组合测量似然函数代替单目标位置及杂波位置测量似然函数,提高了不同类目标与杂波测量间的鉴别能力,从而改善了目标数目及状态的估计精度;在更新目标状态后,对目标属性信息进行更新,更为精确的目标数目及状态估计又保证了目标分类性能.本文给出了该算法的粒子实现方法.仿真结果验证了上述结论.     

基于SMC-PHD滤波的显式航迹维持算法

多目标跟踪的实时性、目标的状态提取与航迹连续的正确率受杂波、漏检、目标近距离这些因素的干扰。为了解决这些问题,提出一种改进的SMC-PHD滤波器。首先,基于2个“一对一”准则,提出粒子贴标签方法和粒子簇权值重置机制,可屏蔽高先验密度区域杂波以及检测的不确定性对多目标状态估计及其数目的干扰。其次,将多目标状态提取转换为多个可提供身份标识的单目标状态提取,得到显式的航迹维持。此外,提出一种新颖的粒子重采样方法,可减少近距离目标对彼此后验信息的干扰。仿真验证了提出的显式航迹维持算法的有效性。与基本的SMC-PHD滤波器相比,显著地提高了多目标跟踪的性能,包括实时性与精度。     

多传感器多目标跟踪的粒子PHD滤波算法

针对单传感器跟踪系统的缺陷,提出了基于粒子概率假设密度(PHD)滤波的多传感器多目标跟踪算法.这种算法不仅避免了多传感器多目标跟踪的数据关联问题,而且在漏检、目标密集、航迹交叉、小范围内目标数多的杂波环境下能够稳定、精确地估计目标状态和目标数.仿真实验比较了单传感器粒子PHD滤波与多传感器的粒子PHD滤波的跟踪性能,验证了该方法的跟踪性能和精度.     

海面目标的自适应预白化检测前跟踪算法

针对超视距雷达的海面目标检测问题,提出一种基于自适应预白化处理的检测前跟踪（TBD）算法。在目标TBD处理之前,利用海杂波的自回归模型构建白化滤波器进行杂波预白化,在跟踪阶段采用递归贝叶斯算法估计目标运动状态,在检测阶段通过跟踪滤波器的输出构造广义似然比进行似然比检测。不同信噪比下的仿真结果表明,该算法能有效抑制海杂波,检测到低信噪比的目标。．     

基于幅值标准偏差的海杂波背景下小目标检测

针对传统舰载雷达和岸基雷达很难检测出海杂波背景下小目标的现状,提出一种基于局部回波幅值标准偏差的方法来检测海杂波背景下小目标。该算法基于数学统计思想：计算相同极化方式下不同距离单元海杂波幅值的标准偏差,分析发现距离单元含有小目标主要回波的幅值的标准偏差明显大于距离单元只含有海杂波回波的幅值的标准偏差,而只含有海杂波的不同距离单元间标准偏差值变化较小,因此可以基于不同距离单元幅值的标准偏差的差值作为检测小目标的标准。实验证明,该算法可在没有任何先验知识条件下较准确地检测出海杂波背景下的小目标。     

基于概率假设密度的多目标视频跟踪算法

研究目标数变化的多目标视频跟踪问题.首先阐述了概率假设密度(PHD)滤波的基本原理;然后给出序列图像多目标跟踪系统的运动目标检测算法、状态方程、观测方程以及基于高斯混合概率假设密度(GM-PHD)的多目标视频跟踪算法的具体实现.该算法有效解决了新目标出现、目标合并、目标分裂及目标消失等多目标跟踪问题.实验结果表明,该算法在复杂场景下具有较强的鲁棒性,能有效实现目标数变化的多目标视频跟踪.     

Adaptive detection of distributed targets in compound-Gaussian clutter with inverse gamma texture

The problem of coherent detection for distributed target in compound-Gaussian clutter with inverse gamma texture is studied and three detectors. One-step generalized likelihood ratio test (GLRT), maximum a-posteriori GLRT and two-step GLRT, are proposed respectively in a Bayesian architecture. Resultantly, these detectors have similar detection structures with their test statistics modulated by the shape and scale parameters of the texture. Alternatively, they can be reformulated into another form with their test statistics associated with the scale parameter and detection thresholds related with the shape parameter. And this detection structure can be seen as a matched filter form with a shape-parameter-dependent threshold like the detectors for point target. Subsequently, the proposed detectors are compared with two-step GLRT based on compound-Gaussian clutter without considering texture model, their detection performances are evaluated, and their robustness are analyzed via Monte Carlo simulations. Results enlighten us that: (1) the three Bayesian detectors bear pretty much the same detection performances; (2) the detection performances fluctuate more intensely when the shape parameter or the scale parameter is smaller; (3) the shape parameter has more influences on the detection performances than the scale parameter, as it is an indication of the clutter impulsiveness.     

GM‐PHD Filter with State‐Dependent Clutter

In traditional filtering methods, clutter is often assumed to obey a uniform distribution over the entire monitoring area. For many sensors, however, clutter may concentrate in target‐containing regions. Under this condition, the performance of the traditional multi‐target tracking filter can be degraded. In an effort to solve this problem, this paper proposes an improved algorithm based on a Gaussian Mixture probability hypothesis density (GM‐PHD) filter to deal with state‐dependent clutter. First, the relationship between state and clutter is modeled using the uniform distribution centered on the target state. Then, the clutter intensity is calculated according to the distribution of clutter in the whole monitoring area and is used to update the filter. The simulation results show that the improved filter can track targets’ trajectories more effectively in an environment of state‐dependent clutter than the standard GM‐PHD filter.     

基于CFAR海上溢油检测研究

在SAR图像处理的基础上,提出一种新的基于恒虚警率( CFAR-Constant False Alarm Rate) 技术,确定SAR 图像中检测溢油整体阈值的方法。该方法采用高斯分布(正态分布) 作为SAR 图像灰度的概率密度函数,由CFAR 技术直接导出用于检测海上溢油整体阈值的计算公式,进行虚警去除。该算法避免了复杂公式迭代和求解形状参数计算过程,也避免了用二分法寻找阈值的循环解算过程,提高了检测速度。使用ENVISAT图像对该算法进行检验,结果显示所提出的算法在检测精度和检测速度上都有明显的改进。