In the synthetic aperture radar(SAR)imaging system of high-speed maneuvering platform,the existing design methods of pulse repetition frequency(PRF)have some shortcomings including that the lower bound for the PRF calculated based on the azimuth signal bandwidth is too high,and the factor of radar's duty ratio is neglected.In view of the above problems,firstly,a set of constraint equations that related to PRF and duty ratio are derived to avoid the fac-tors such as transmitting pulse interference,echo interference directly below the platform and range ambiguity.Then,a new design method for the lower bound of PRF in the case of range sectioned SAR imaging is proposed.The simulation results show that the proposed method can achieve a smaller PRF lower limit when highly squinted spotlight SAR imag-ing seeker moves at a high speed,which effectively expands the range of PRF selection and can better meet the need of practical engineering applications.

In the actual localization scenarios, the receiving station is usually installed on the moving platform, which leads to the random error in its moving state information. However, the target source location accuracy is very sensitive to the location information of the receiving station, and the small error in the location of the receiving station will lead to a large error in the estimation of the target source position.Therefore, considering the random error of the location information of the receiving station, a solution to locate the mobile source using the measured values of the time difference of arrival and the frequency difference of arrival is proposes in this paper. Logistic chaotic mapping is introduced into sparrow search algorithm to locate and track the target. Logistic chaotic mapping can reduce the risk of the algorithm convergence to local optimal, so as to solve the problem of poor localization accuracy in the case of low sensor position error. The analysis of simulation results shows that the accuracy of the proposed algorithm is closer to the Cramer-Rao lower bound than that of semi-definite programming and reformulation linearization technique（SDP-RLT）, genetic algorithm, sparrow search algorithm and ant colony algorithm under the condition of low sensor position error.

In recent years, the missile-borne SAR technology has been successfully applied. It can be used for terrain matching and automatically detect and identify the target under complex background conditions. This paper introduces design and simulation analysis of missile-borne SAR system, including system composition and working principle, loss analysis, calculation of the noise equivalent scattering coefficient, and simulation of the pulse repetition frequency. The results show that the design of the missile-borne SAR system is a complex process that requires repeated synthesis and optimization.

针对传统相位干涉仪测向法精度不高和MUSIC算法空间谱峰搜索耗时长的问题,提出了一种基于加权最小二乘法和MUSIC算法相结合的均匀圆阵测向技术.首先根据阵列短基线组求解相位模糊,并通过引入中间变量将相位差测量方程转化为线性方程组,然后运用加权最小二乘法对其进行求解,并利用信号到达角与中间变量的关系得到信号到达角初始估计,最后MUSIC算法依据信号到达角初始估计进行空间谱峰搜索,得到高精度信号到达角估计.仿真实验对比了所提方法与现有相位干涉仪及MUSIC算法的角度估计精度和耗时,证实了所提方法的正确性和有效性.

This paper is concerned with the problem of moving source localization using multiple-time time difference of arrival (TDOA) measurements collected by spatially distributed stationary receivers. We transform the nonlinear multiple-time TDOA equations into a set of pseudo-linear ones and then employ several weighted least-squares minimizations to obtain the source position and velocity estimates. The performance of the proposed solution is shown to be able to reach the Cramer-Rao lower bound (CRLB) accuracy by simulation studies when the TDOA measurement noises are sufficiently small.

This paper develops an efficient bi-iterative source location and velocity estimation method with time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements. Because of the high nonlinearity in the TDOA and FDOA measurement equations, obtaining high source location and velocity estimation accuracy is far from straightforward. The developed bi-iterative method calculates the source location and velocity alternately, which significantly decreases the computational cost. The proof of convergence of the bi-iterative method is provided and the approximate efficiency is theoretically analyzed. Most importantly, the source location and velocity estimation accuracy of the bi-iterative method is closer to the Cramer–Rao lower bound (CRLB) than that of other existing localization methods. Simulation studies are given to confirm the approximate efficiency and to show the superior performance of the proposed method in comparison with relative methods.

传统时差定位方法一般是在假设传感器位置信息准确已知的前提下进行的.然而在实际情形中,传感器位置信息往往含有随机误差,这些误差会严重影响对目标的定位精度.针对这一问题,提出了一种传感器位置误差情况下的多维标度时差定位算法.首先利用传感器位置和时差构造对称标量积矩阵,然后利用子空间理论建立关于目标位置的伪线性方程,最后通过设计加权矩阵来减少传感器位置误差对目标定位精度的影响.采用一阶小噪声扰动理论求出了目标位置估计的偏差及协方差矩阵,并通过仿真实验验证了该算法的有效性.

Aiming at the excessive clutte’s DOFs (degrees of freedom) and huge demand for training samples of 3D-STAP,a three dimensional space-time open-loop clutter block canceller(3D-STCBC)is proposed.First,the vector-matrix model of the three dimensional clutter is constructed with the a priori knowledge. Then the equivalence between the spatial domain and the temporal domain is exploited to design a coefficient matrix to make a cancellation between the clutter blocks.The experiment shows that this method can effectively suppress the clutter even with small samples and dramatically lower the clutter’s DOFs.

In order to solving the problem of ineffective clutter suppression and target detection of space‐time adaptive processing ( STAP) in bistatic airborne radar , a clutter pre‐filtering method applied in the bistatic airborne radar that takes advantage of radar operating parameters , platform velocity and so on is proposed . The velocity error of the airborne platform is also considered . Most of the clutter can be filtered so that the residual clutter can be completely suppressed by the well‐developed STAP algorithm . Computer simulation results show that this method is effectively workable to several classical geometric configurations of bistatic airborne radar . The moving target detectability of the following STAP algorithm is also enhanced after this pre‐filter .

The traditional post-Doppler adaptive processing approaches such as Factored Approach (FA) and Extended Factored Approach (EFA) can significantly reduce the computation-cost and training sample requirement in adaptive processing. However, their clutter suppression ability is considerably degraded with the increasing number of antenna elements. To solve this problem, a two-stage reduced-dimension adaptive processing method based on the decomposition of spatial data is proposed. This method decomposes the spatial data after Doppler filtering into a Kronecker product of two short vectors. Then a bi-quadratic cost function is obtained. The circular iteration is applied to solve the optimal weight. Experimental results show that the proposed method has the advantages of fast convergence and small training samples requirement. It has greater clutter suppression ability especially in small training samples support compared with FA and EFA.

A new time of arrival (TOA) location algorithm based on the linear correction technique is proposed to address the non-linear problem of TOA positioning.The proposed algorithm firstly rearranges the TOA measurement equations into pseudo-linear ones and we obtain the initial target position estimation using the weighted least-squares estimator.Then a linear correction technique is used to correct the initial estimation.The effectiveness of the proposed method is analyzed.Simulation study validates the good performance of the proposed algorithm.

This paper presents a new two-stage weighted least squares (WLS) estimator for source localization using time differences of arrival (TDOA) measured by multiple moving receivers.The first stage transforms the highly nonlinear TDOA equations into a set of pseudo-linear ones by introducing intermediate variables and then uses the WLS estimator to obtain the initial estimation.The second stage refines the initial emitter location estimate obtained in the first stage by exploiting the relationship between the emitter position and the intermediate variables.The efficiency of the proposed algorithm is theoretically analyzed.Simulation shows the good performance of the proposed method.

An efficient bi-iterative source location and velocity estimation method with time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements is developed. The bi-iterative method calculates the source location and velocity alternately, which significantly reduces the computational cost. Most importantly, the estimation accuracy of the bi-iterative method is closer to the Cramer-Rao lower bound than that of other localisation methods. Simulation studies show the superior performance of the proposed method over existing localisation methods.

Conventional location algorithms are based on the postulation that the sensor locations are exactly known.However,in practical situations,the sensor positions generally include random errors,which can con-siderably reduce the source localization accuracy.To tackle this problem,a new time of arrival (TOA)positioning algorithm based on the linear-correction technique is proposed.The proposed algorithm firstly reorganizes the nonlinear TOA equations into pseudo-linear ones and the initial target position estimation is obtained by using weighted least-squares estimatets.Then a linear-correction technique is used to correct the initial position esti-mate.The effectiveness of the proposed method is theoretically analyzed under sufficiently small noise postula-tion.Simulation study validates the good performance of the proposed algorithm.

针对到达时间差(TDOA)定位中出现的非线性估计问题,该文提出一种线性校正TDOA定位算法.首先将高度非线性的TDOA定位方程组转化为一组关于辐射源位置的伪线性方程,利用加权最小二乘(WLS)估计进行初始求解;然后在此基础上通过一阶泰勒级数展开把伪线性方程组转化为关于估计偏差的线性加权最小二乘问题并进行求解,分析了所提算法在测量误差较小时的有效性.最后提出了一种基于加权最小二乘估计的恒加速度运动辐射源的定位方法,相应的估计性能在测量误差较小时也接近克拉美罗界(CRLB).计算机仿真结果验证了该算法的有效性.

传统的后多普勒自适应处理方法,如因子法(FA)和扩展因子法(EFA)虽然能大大降低自适应处理时的运算量和独立同分布样本的需求量,但由于实际中均匀训练样本数目的限制,当天线阵元数进一步增大时,FA和EFA抑制杂波和检测动目标的能力会显著恶化.针对这一问题,提出了一种空域数据重排的后多普勒自适应处理方法.该方法将多普勒滤波后的空域数据重排为一行列数相近的矩阵,空域滤波器权系数也表示成可分离的形式,从而得到一双二次代价函数,利用循环迭代的思想求解权系数.实验表明该方法具有快速收敛,所需训练样本少的优点,尤其在大阵列、小样本条件下该方法抑制杂波的性能明显优于FA和EFA.

For the divergence problem of traditional iterative process based location algorithms,a new modified Newton algorithm for the passive location from time differences of arrival (TDOA) is proposed. The proposed algorithm firstly reorganizes the nonlinear TDOA equations into pseudo-linear ones by using an auxiliary parameter, and a constrained weighted least-squares minimization is developed for the positioning problem instead of the Maximum Likelihood estimator.A modified Newton method based on eigenvalue modification is then applied to obtain the emitter position.In order to reduce the number of iterations,an appropriate iteration step size is computed via one-dimensional optimization by the quadratic interpolation method.Simulation results demonstrate the effectiveness of the proposed algorithm.

This paper develops an efficient bi-iterative source location and propagation speed estimation method utilizing time difference of arrival (TDOA) measurements. The source location and propagation speed estimation is a nonlinear problem due to the nonlinearity in the TDOA measurement equations. The newly developed bi-iterative method computes the source location and propagation speed alternately. The asymptotic convergence of the new bi-iterative method is theoretically analyzed. First-order perturbation analysis is applied to the newly developed solution to derive its bias and variance. The first-order analytical results show that the proposed method provides approximately unbiased source position and propagation speed estimates for low noise levels and the accuracy of these estimates approaches the Cramer-Rao lower bound (CRLB). The extension of the new bi-iterative method to the more general situation where the sensor locations are subject to random errors is also presented. Simulation studies are given to show the good performance of the proposed method.

A new algorithm based on multi-reference receivers for the passive location from time difference of arrival (TDOA) is proposed.The proposed algorithm first transforms the nonlinear TDOA equations into a set of pseudo-linear equations of the emitter position,and then the weighted least squares algorithm is applied to obtain the initial solution.By taking the relationship among the entries of the initial solution into account,we get a set of more accurate emitter position estimates via the weighted least squares algorithm with the obtained initial solution,the average of these estimates is then taken as the final estimate of the emitter position.Simulation results show that the proposed algorithm achieves high positioning accuracy for both near and far field emitters.

Space-time adaptive processing (STAP) has a huge computational complexity and a large training samples requirement, which limit its practical applications. The traditional post-Doppler adaptive processing methods such as factored approach (FA) and extended factored approach (EFA) can significantly reduce the computational complexity and the training sample requirement in adaptive processing, and maintain nearly the same performance as the optimal STAP. However, because training samples are restricted in real-world environments, their performances can be considerably degraded in the large-scale antenna array. To solve this problem, the post-Doppler adaptive processing method based on the spatial domain reconstruction is proposed. In this method, the spatial clutter data after Doppler filtering is reconstructed as a matrix that has close columns and rows. The spatial weights vector in FA or EFA is also re-expressed as the product of two shorter weight vectors. Then the cyclic minimizer is applied to find the desired solution. Experimental results show that the proposed method has the advantages of fast convergence and small training samples requirement. It has greater moving target detection ability especially under the condition for large-scale antenna array and small training samples support than FA and EFA.