天基预警雷达微弱动目标检测与定位方法研究
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摘要
天基预警雷达监视空中和空间目标具有重要的军用价值,但面临低信噪比下机动目标检测、跟踪和定位等技术的挑战,引起国内外的广泛重视。增加积累时间可以达到提高信噪比的要求,但由于目标的高速运动和大机动飞行等特点,进行长时间积累时易发生距离徙动与回波非平稳,使得传统基于FFT的相参积累方法性能恶化。检测前跟踪技术(TBD)通过对多帧图像数据作相关处理,几乎没有信息积累损失,特别适合低信杂噪比下的目标检测与跟踪。此外,识别空间目标要求发射宽带信号,而星上资源相对有限,利用宽带信号对目标进行识别的同时对目标进行定位可充分挖掘信号资源。本文紧密围绕天基雷达微弱运动目标检测与定位中的实际问题,对机动目标长时间相参积累、微弱目标的TBD算法、宽带信号的波达方向(DOA)估计等关键技术进行了深入的研究。本文的具体工作安排如下：
1.讨论天基雷达微弱信号检测技术所涉及的运动目标信号模型和微弱目标长时间能量积累等方面的问题,为后续的研究提供参考。
2.长时间相参积累是提高弱目标检测能力的有效手段,针对积累期间的距离徙动和多普勒展宽问题,提出两种解决途径。方法一首先根据回波高阶相位对包络补偿效果的影响对数据进行分段处理；然后对每个数据段采用速度滤波器组进行划分和keystone变换补偿回波包络走动,并且对包络补偿后的数据采用分数阶傅立叶变换估计回波相位历程；最后采用最小二乘方法拟合出目标的运动参数并对目标回波进行长时间相参积累。方法二针对目标作匀加速运动,且高速目标存在多普勒模糊的情况,将脉冲压缩后的目标回波信号转换到距离-多普勒二维频率域,通过对加速度的一维搜索构造补偿函数进行匹配处理。该方法仅需对加速度参数进行估计,由目标径向速度引起的距离走动和径向加速度引起的距离弯曲均能得到很好的消除。另外,所提算法可以有效地利用快速傅立叶变换实现而无需进行插值操作,运算量小。两种方法均采用计算机仿真进行了验证。
3.基于粒子滤波的TBD算法由于可以有效处理非线性、非高斯和多模式的状态估计问题,特别适用于机动微弱目标的检测与跟踪。针对传统基于粒子滤波的TBD算法中出现的退化现象以及粒子多样性缺失等问题,论文提出了两种改进算法。方法一通过几点改进策略以提高算法的性能,即采用对权重最低的部分“存活”粒子用“新生”粒子将其替换的粒子更新策略,而在每次重采样后,实施马尔科夫链蒙特卡洛(MCMC)移动步骤,引导粒子朝着多样性方向发展,在提升粒子多样性的同时缓解了粒子的退化,保证了目标的持续跟踪。同时将门限的选取转换为与之等价的分辨单元集大小的选取,这种选取方法只与SNR有关,适用于不同的背景噪声下。分析了粒子数量和门限的选取对跟踪性能的影响,分析结果表明只要采取恰当的策略,在粒子数较少时也能取得较好的性能。方法二是在粒子滤波的基础上融合无迹卡尔曼滤波(UKF)算法,融合后的新算法在利用重要性密度函数产生粒子时充分考虑当前时刻的量测,从而引导粒子向高似然区域移动,使得粒子的分布更接近状态的后验概率分布。同时,在每次重采样后实施MCMC移动步骤。计算机仿真验证了算法的有效性。
4.将基于改进粒子滤波的TBD算法应用于雷达微弱信号的积累检测中。通过分析得到了接收信号的表达形式,从而采用与雷达信号处理相匹配的量测数据模型,能克服传统点扩散函数的模型误差。粒子滤波过程中,采用上述的改进算法,即采用“新生”粒子从强度最高的分辨单元集内均匀产生,且按概率对权重最低的部分“存活”粒子用“新生”粒子将其替换的粒子更新策略。仿真实验验证了算法的有效性。
5.识别空间目标一般是利用宽带信号所具有的高距离分辨率进行的,然而星上资源相对有限,为充分挖掘信号资源,利用宽带信号进行目标识别的同时对DOA进行估计很有意义。针对基于相干信号的聚焦(CSM)算法在短数据情况下,观测协方差矩阵估计偏差易导致算法性能下降的问题,提出一种新的解决思路,即利用粒子滤波对不同频率点处的目标阵列流形进行跟踪从而实现宽带DOA估计。该算法基于当前时刻的观测信息,无需估计观测协方差矩阵,使得在短数据情况下能够取得优良的估计性能。同时算法从一组随机的初始值出发,无需预估计波达方向,并且递推过程是基于极大似然估计的思想,具有解相关的能力。计算机仿真验证了算法在短数据、低信噪比和相干源情况下具有比CSM算法更为优良的估计性能。
The modern space-based early warning radars play an importantly military role in surveiling the air and space moving targets. However, it encounters with the challenge of detection, tracking, and positioning of maneuvering targets under low signal noise rate (SNR), which has attracted the attention of researchers all over the world. The SNR can be improved by increasing integration time. However, the signal energy could not be effectively accumulated by FFT-based traditional methods, because the high-speed and maneuvering motion of a target induces range migration in the long-time coherent integration period. Track-before-detect (TBD) technology directly makes correlation between multiple image data, which reserves most of information. Therefore, TBD is especially applicable for the target detection and tracking in the case of low SNR. In addition, in order to detect the space target, it is reqired to transmit wideband signal. In the meantime, the signal information can be sufficiently exploited using the wideband signal to position the moving target. This thesis investigates the challenging problem of detection and positioning for the weak targets in the terms of long-time coherent integration, TBD based on particle filter (PF) and wideband direction-of-arrival (DOA) estimation. The main content of this thesis can be summarized as follows:
1. In this section, the model of a moving target and the long-time energy accumulation for the weak targets, which involve in the detection and tracking for the weak targets by the space-based early warning radar, is introduced. This provides reference for the subsequent research.
2. Increasing integration time can improve the detection performance of weak targets. However, the signal energy could not be effectively accumulated, because the high-speed motion of a target induces range migration in long-time coherent integration period. In Chapter two, two different approaches are addressed for long-time coherent integration. Firstly, the signal is segmented based on the influence of echo's high order phase history on motion compensation. Secondly, the range migration is corrected with keystone transform and then the phase history is estimated with Fractal Fourier transform (FRFT) for each sub-segment. Finally, the motion parameters are refined by using least square algorithm among sub-segments and thus the long-term coherent accumulation can be achieved. For the moving target with constant acceleration and Doppler ambiguity, a new method is proposed by constructing the two-dimensional compression function in range-Doppler frequency domain, which can eliminate the coupling effect between range and azimuth directions. This method makes matching processing by one-dimensional search for the acceleration and does not require target velocity parameters. This method can correct the range migration caused by radial velocity and radial acceleration. The proposed algorithm can be efficiently implemented by using fast Fourier transform without interpolation and thus has low computational complexity. Simulation results show that the proposed algorithm improves the performance for detecting high-speed maneuvering targets.
3. TBD based on particle filter algorithm can effectively handle the problem of the nonlinear, non-Gaussian and multi-modal state estimation, especially applicable for the detection for maneuvering and weak targets. Unfortunately, the traditional PF methods are apt to induce collapse and diversity loss of particles. Two improved TBD algorithms based on the particle filter are proposed to detect and track the weak target in low SNR. Firstly, an updating strategy is proposed by replacing the existing particles with low weights with new particles and then performing Markov chain Monte Carlo (MCMC) moving step after resampling particles. This strategy can improve the diversity among the particles, and simutanouesly guarantee that the particles are effective. Simulation analysis is given for the effect of the number of particles and the detection threshold on target detection. The other algorithm is presented by combining the particle filter with unscented Kalman filter (UKF). Because in the proposed method, the important probability density distribution is calculated based on the current measurement, the sampling particles are most likely to be in the region of high likelihood, which makes the particles distribution more approach to the posterior distribution of the state. Simulation results show that the proposed algorithm provides an improved performance of detecting and tracking weak targets compared with the conventional particle filter.
4. An improved TBD algorithm based on the particle filter is applied for high-speed weak target detection. This method uses a unique measurement model for radar range-Doppler compression, which can effectively reduce the model error of the traditional sensor point spread function. An updating strategy is developed by the following step:newborn particles are uniformly distributed within the set with high-intensity bins and then that the existing particles with low weights are replaced by new particles with probability. This strategy can improve the diversity among the particles, and mitigate the effects of degeneracy. Simulation results show that the proposed algorithm has an improved performance of detecting and tracking dim target compared with the standard particle filter.
5. It is generally to use the high resolution in range direction to indentify space target. However, the resource in the satellitic plateform is limited and thus it is necessary to exploit the signal resource. Therefore, it is significantly meaningfull to estimate DOA along with indentifying the target with wideband signals. It is known that the performance of the coherent signal-subspace method (CSM) algorithm degrades due to the estimation difference of the covariance matrix in the case of small data. Aiming to address this problem, chapter 5 proposes a new method for estimating the DOA of wideband signal, which employs particle filters to track array manifold at different frequency bands. Compared with the CSM, the proposed method utilizes the current observed data and does not require the estimated covariance matrix, thus it performs better in the case of a small sample set. In the meantime, since initial values of the parameters in this method can be selected arbitrarily, thus this method does not require the preliminary DOA estimates. Moreover, the proposed method can localize completely correlated sources because it is based on the idea of maximum likelihood. Simulation results show that the performance of the propose method is better than CSM when the sample set is small, the SNR is low, and the signal sources are correlated.
1.讨论天基雷达微弱信号检测技术所涉及的运动目标信号模型和微弱目标长时间能量积累等方面的问题,为后续的研究提供参考。
2.长时间相参积累是提高弱目标检测能力的有效手段,针对积累期间的距离徙动和多普勒展宽问题,提出两种解决途径。方法一首先根据回波高阶相位对包络补偿效果的影响对数据进行分段处理；然后对每个数据段采用速度滤波器组进行划分和keystone变换补偿回波包络走动,并且对包络补偿后的数据采用分数阶傅立叶变换估计回波相位历程；最后采用最小二乘方法拟合出目标的运动参数并对目标回波进行长时间相参积累。方法二针对目标作匀加速运动,且高速目标存在多普勒模糊的情况,将脉冲压缩后的目标回波信号转换到距离-多普勒二维频率域,通过对加速度的一维搜索构造补偿函数进行匹配处理。该方法仅需对加速度参数进行估计,由目标径向速度引起的距离走动和径向加速度引起的距离弯曲均能得到很好的消除。另外,所提算法可以有效地利用快速傅立叶变换实现而无需进行插值操作,运算量小。两种方法均采用计算机仿真进行了验证。
3.基于粒子滤波的TBD算法由于可以有效处理非线性、非高斯和多模式的状态估计问题,特别适用于机动微弱目标的检测与跟踪。针对传统基于粒子滤波的TBD算法中出现的退化现象以及粒子多样性缺失等问题,论文提出了两种改进算法。方法一通过几点改进策略以提高算法的性能,即采用对权重最低的部分“存活”粒子用“新生”粒子将其替换的粒子更新策略,而在每次重采样后,实施马尔科夫链蒙特卡洛(MCMC)移动步骤,引导粒子朝着多样性方向发展,在提升粒子多样性的同时缓解了粒子的退化,保证了目标的持续跟踪。同时将门限的选取转换为与之等价的分辨单元集大小的选取,这种选取方法只与SNR有关,适用于不同的背景噪声下。分析了粒子数量和门限的选取对跟踪性能的影响,分析结果表明只要采取恰当的策略,在粒子数较少时也能取得较好的性能。方法二是在粒子滤波的基础上融合无迹卡尔曼滤波(UKF)算法,融合后的新算法在利用重要性密度函数产生粒子时充分考虑当前时刻的量测,从而引导粒子向高似然区域移动,使得粒子的分布更接近状态的后验概率分布。同时,在每次重采样后实施MCMC移动步骤。计算机仿真验证了算法的有效性。
4.将基于改进粒子滤波的TBD算法应用于雷达微弱信号的积累检测中。通过分析得到了接收信号的表达形式,从而采用与雷达信号处理相匹配的量测数据模型,能克服传统点扩散函数的模型误差。粒子滤波过程中,采用上述的改进算法,即采用“新生”粒子从强度最高的分辨单元集内均匀产生,且按概率对权重最低的部分“存活”粒子用“新生”粒子将其替换的粒子更新策略。仿真实验验证了算法的有效性。
5.识别空间目标一般是利用宽带信号所具有的高距离分辨率进行的,然而星上资源相对有限,为充分挖掘信号资源,利用宽带信号进行目标识别的同时对DOA进行估计很有意义。针对基于相干信号的聚焦(CSM)算法在短数据情况下,观测协方差矩阵估计偏差易导致算法性能下降的问题,提出一种新的解决思路,即利用粒子滤波对不同频率点处的目标阵列流形进行跟踪从而实现宽带DOA估计。该算法基于当前时刻的观测信息,无需估计观测协方差矩阵,使得在短数据情况下能够取得优良的估计性能。同时算法从一组随机的初始值出发,无需预估计波达方向,并且递推过程是基于极大似然估计的思想,具有解相关的能力。计算机仿真验证了算法在短数据、低信噪比和相干源情况下具有比CSM算法更为优良的估计性能。
The modern space-based early warning radars play an importantly military role in surveiling the air and space moving targets. However, it encounters with the challenge of detection, tracking, and positioning of maneuvering targets under low signal noise rate (SNR), which has attracted the attention of researchers all over the world. The SNR can be improved by increasing integration time. However, the signal energy could not be effectively accumulated by FFT-based traditional methods, because the high-speed and maneuvering motion of a target induces range migration in the long-time coherent integration period. Track-before-detect (TBD) technology directly makes correlation between multiple image data, which reserves most of information. Therefore, TBD is especially applicable for the target detection and tracking in the case of low SNR. In addition, in order to detect the space target, it is reqired to transmit wideband signal. In the meantime, the signal information can be sufficiently exploited using the wideband signal to position the moving target. This thesis investigates the challenging problem of detection and positioning for the weak targets in the terms of long-time coherent integration, TBD based on particle filter (PF) and wideband direction-of-arrival (DOA) estimation. The main content of this thesis can be summarized as follows:
1. In this section, the model of a moving target and the long-time energy accumulation for the weak targets, which involve in the detection and tracking for the weak targets by the space-based early warning radar, is introduced. This provides reference for the subsequent research.
2. Increasing integration time can improve the detection performance of weak targets. However, the signal energy could not be effectively accumulated, because the high-speed motion of a target induces range migration in long-time coherent integration period. In Chapter two, two different approaches are addressed for long-time coherent integration. Firstly, the signal is segmented based on the influence of echo's high order phase history on motion compensation. Secondly, the range migration is corrected with keystone transform and then the phase history is estimated with Fractal Fourier transform (FRFT) for each sub-segment. Finally, the motion parameters are refined by using least square algorithm among sub-segments and thus the long-term coherent accumulation can be achieved. For the moving target with constant acceleration and Doppler ambiguity, a new method is proposed by constructing the two-dimensional compression function in range-Doppler frequency domain, which can eliminate the coupling effect between range and azimuth directions. This method makes matching processing by one-dimensional search for the acceleration and does not require target velocity parameters. This method can correct the range migration caused by radial velocity and radial acceleration. The proposed algorithm can be efficiently implemented by using fast Fourier transform without interpolation and thus has low computational complexity. Simulation results show that the proposed algorithm improves the performance for detecting high-speed maneuvering targets.
3. TBD based on particle filter algorithm can effectively handle the problem of the nonlinear, non-Gaussian and multi-modal state estimation, especially applicable for the detection for maneuvering and weak targets. Unfortunately, the traditional PF methods are apt to induce collapse and diversity loss of particles. Two improved TBD algorithms based on the particle filter are proposed to detect and track the weak target in low SNR. Firstly, an updating strategy is proposed by replacing the existing particles with low weights with new particles and then performing Markov chain Monte Carlo (MCMC) moving step after resampling particles. This strategy can improve the diversity among the particles, and simutanouesly guarantee that the particles are effective. Simulation analysis is given for the effect of the number of particles and the detection threshold on target detection. The other algorithm is presented by combining the particle filter with unscented Kalman filter (UKF). Because in the proposed method, the important probability density distribution is calculated based on the current measurement, the sampling particles are most likely to be in the region of high likelihood, which makes the particles distribution more approach to the posterior distribution of the state. Simulation results show that the proposed algorithm provides an improved performance of detecting and tracking weak targets compared with the conventional particle filter.
4. An improved TBD algorithm based on the particle filter is applied for high-speed weak target detection. This method uses a unique measurement model for radar range-Doppler compression, which can effectively reduce the model error of the traditional sensor point spread function. An updating strategy is developed by the following step:newborn particles are uniformly distributed within the set with high-intensity bins and then that the existing particles with low weights are replaced by new particles with probability. This strategy can improve the diversity among the particles, and mitigate the effects of degeneracy. Simulation results show that the proposed algorithm has an improved performance of detecting and tracking dim target compared with the standard particle filter.
5. It is generally to use the high resolution in range direction to indentify space target. However, the resource in the satellitic plateform is limited and thus it is necessary to exploit the signal resource. Therefore, it is significantly meaningfull to estimate DOA along with indentifying the target with wideband signals. It is known that the performance of the coherent signal-subspace method (CSM) algorithm degrades due to the estimation difference of the covariance matrix in the case of small data. Aiming to address this problem, chapter 5 proposes a new method for estimating the DOA of wideband signal, which employs particle filters to track array manifold at different frequency bands. Compared with the CSM, the proposed method utilizes the current observed data and does not require the estimated covariance matrix, thus it performs better in the case of a small sample set. In the meantime, since initial values of the parameters in this method can be selected arbitrarily, thus this method does not require the preliminary DOA estimates. Moreover, the proposed method can localize completely correlated sources because it is based on the idea of maximum likelihood. Simulation results show that the performance of the propose method is better than CSM when the sample set is small, the SNR is low, and the signal sources are correlated.
引文
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