Taifan Quan

Statically Fused Converted Position and Doppler Measurement Kalman Filters

The work presented in this paper makes two contributions for exploiting Doppler (range rate) measurements in tracking systems. First, a new linear filter, the converted Doppler measurement Kalman filter (CDMKF), is presented to extract nonlinear pseudostates from converted Doppler measurements (i.e., the product of the range measurements and Doppler measurements). The pseudostates are constructed from the converted Doppler and its derivatives. The linearly evolving equations of the pseudostates are derived for common target motion models. The second contribution of this paper is using the CDMKF along with the converted position measurement Kalman filter (CPMKF), in which only the position measurements are used, to establish a new filtering structure, statically fused converted measurement Kalman filters (SF-CMKF). The resulting states of CPMKF and CDMKF are combined by a static minimum mean squared error (MMSE) estimator, where the nonlinearity and correlation between the pseudostates and the Cartesian states are handled simultaneously, to yield the final state estimates. The dynamic nonlinear estimation problem is converted into dynamic linear estimation followed by static nonlinear fusion. The estimation accuracy can be enhanced by incorporating the Doppler measurements via the linear CDMKF, while the filtering stability can be improved by dealing with nonlinearity outside the filtering recursions. Monte Carlo simulations and comparison with the posterior Cramer-Rao bound demonstrate the effectiveness of the CDMKF and SF-CMKF.

Sparse Representation Denoising for Radar High Resolution Range Profiling

Radar high resolution range profile has attracted considerable attention in radar automatic target recognition. In practice, radar return is usually contaminated by noise, which results in profile distortion and recognition performance degradation. To deal with this problem, in this paper, a novel denoising method based on sparse representation is proposed to remove the Gaussian white additive noise. The return is sparsely described in the Fourier redundant dictionary and the denoising problem is described as a sparse representation model. Noise level of the return, which is crucial to the denoising performance but often unknown, is estimated by performing subspace method on the sliding subsequence correlation matrix. Sliding window process enables noise level estimation using only one observation sequence, not only guaranteeing estimation efficiency but also avoiding the influence of profile time-shift sensitivity. Experimental results show that the proposed method can effectively improve the signal-to-noise ratio of the return, leading to a high-quality profile.

Constant turn model for statically fused converted measurement Kalman filters

In this paper, the discrete temporal evolution equation of pseudo-states is derived for the constant turn (CT) motion with known turn rate. The pseudo-state vector consists of the converted Doppler (the product of target true range and range rate) and its first derivative. Based on the resulting linear state equation, the converted Doppler measurement Kalman filter (CDMKF) is formulated to extract information from the converted Doppler measurements. The pseudo-states from the CDMKF are fused statically with the outputs of the well known converted position measurement Kalman filter (CPMKF) by a static minimum mean squared error (MMSE) estimator designed for the proposed CT model, resulting in the statically fused converted measurement Kalman filter (SF-CMKF). The validity of the proposed CT model and its benefits to state estimation with Doppler as well as position measurements are demonstrated by assessing the performance of the CDMKF and SF-CMKF. Comparative results show the superior performance of the proposed technique especially in challenging scenarios with large position measurement errors. HighlightsA pseudo-state equation is derived to establish a novel state space representation of the CT motion.The filtering procedure of the converted Doppler measurement Kalman filter for the CT motion is presented.The formulas of the static estimator are provided to fuse the CDMKF and CPMKF, resulting in accurate and robust estimation.

Pseudo states estimation for maneuvering target tracking in Doppler radar systems

Maneuvering target tracking with Doppler measurements is investigated. This paper presents a linear filtering method, converted Doppler measurements Kalman filter (CDMKF), to estimate a pseudo state vector, which consists of the converted Doppler and its derivatives. Then the output of CDMKF is combined with the output of converted position measurements Kalman filter (CPMKF) by a nonlinear static estimator outside the filtering recursions. This constructs a new state estimator which is named as parallel converted measurements Kalman filter (PRL-CMKF). Simulation results demonstrate the effectiveness and robustness of the proposed CDMKF and PRL-CMKF.

Remote sensing of surface currents with single shipborne high-frequency surface wave radar

High-frequency surface wave radar (HFSWR) is a useful technology for remote sensing of surface currents. It usually requires two (or more) stations spaced apart to create a two-dimensional (2D) current vector field. However, this method can only obtain the measurements within the overlapping coverage, which wastes most of the data from only one radar observation. Furthermore, it increases observation’s costs significantly. To reduce the number of required radars and increase the ocean area that can be measured, this paper proposes an economical methodology for remote sensing of the 2D surface current vector field using single shipborne HFSWR. The methodology contains two parts: (1) a real space-time multiple signal classification (MUSIC) based on sparse representation and unitary transformation techniques is developed for measuring the radial currents from the spreading first-order spectra, and (2) the stream function method is introduced to obtain the 2D surface current vector field. Some important conclusions are drawn, and simulations are included to validate the correctness of them.

High-Resolution Ocean Clutter Spectrum Estimation for Shipborne HFSWR Using Sparse-Representation-Based MUSIC

The spreading of the dominant first-order Bragg lines in shipborne high-frequency surface wave radar (HFSWR) severely obscures the detection of the slow-moving targets and the measurement of ocean clutter. Space-time adaptive processing (STAP) is an effective tool for solving the problem. It normally requires a large number of independent and identically distributed (i.i.d.) training samples to estimate the ocean clutter spectrum and design the filter to eliminate the ocean clutter from the test cell. However, the training samples are insufficient due to the system limitation of shipborne HFSWR, and the stationarity of training data is destroyed in the nonstationary and nonhomogeneous ocean environment, which result in decreased performance. Thus, the estimation of the ocean clutter spectrum with small training samples or even only the test cell is an important work for shipborne HFSWR. In this paper, by exploiting the intrinsic sparsity of the ocean clutter in shipborne HFSWR, the multiple signal classification (MUSIC) algorithm based on the sparse representation technique, called SR-MUSIC, is introduced to estimate the ocean clutter spectrum. The correctness of the ocean clutter sparsity and the validity of the SR-MUSIC algorithm for the high-resolution ocean clutter spectrum estimation are verified by the simulation results.

Sequential nonlinear tracking filter without requirement of measurement decorrelation

Sequential measurement processing is of benefit to both estimation accuracy and computational efficiency. When the noises are correlated across the measurement components,decorrelation based on covariance matrix factorization is required in the previous methods in order to perform sequential updates properly. A new sequential processing method, which carries out the sequential updates directly using the correlated measurement components, is proposed. And a typical sequential processing example is investigated, where the converted position measurements are used to estimate target states by standard Kalman filtering equations and the converted Doppler measurements are then incorporated into a minimum mean squared error(MMSE)estimator with the updated cross-covariance involved to account for the correlated errors. Numerical simulations demonstrate the superiority of the proposed new sequential processing in terms of better accuracy and consistency than the conventional sequential filter based on measurement decorrelation.

Measurement of surface currents from a shipborne HFSWR

The spreading first-order sea echo spectrum in shipborne high-frequency surface wave radar (HFSWR) severely impacts the measurement of surface currents. Space-time processing techniques like space-time multiple signal classification (ST-MUSIC) algorithm, usually studied for two-dimensional high resolution radar imaging, are the effective methods for solving the problem. The rank of the sea echo covariance matrix is an important indicator of the severity of the sea echo scenario. With this knowledge, the sea echo can be accurately extracted from the complicated radar returns by using the ST-MUSIC algorithm. Furthermore, the least-squares method is introduced to estimate the uniform surface current vectors from a single shipborne HFSWR by fitting the adjacent radial current vectors. The simulation results have verified the validity of these algorithms.

A Modified STAP Estimator for Superresolution of Multiple Signals

A modified space-time adaptive processing (STAP) estimator is described in this paper. The estimator combines the incremental multiparameter (IMP) algorithm and the existing beam-space preprocessing techniques yielding a computationally cheap algorithm for the superresolution of multiple signals. It is a potential technique for the remote sensing of the ocean currents from the broadened first-order Bragg sea echo spectrum of shipborne high-frequency surface wave radar (HFSWR). Some simulation results and real-data analysis are shown to validate the proposed algorithm.

Non-parametric multi-target tracking with Doppler measurements

This paper investigates the problem of incorporating Doppler measurements to improve multi-target tracking performance. The clutter density in both spatial domain and Doppler direction is assumed to be unknown and non-homogeneous. Instead of modeling the clutter density as the product of the clutter spatial density and the clutter Doppler probability density function (pdf), this paper introduces a hyper-spatial density concept, where the Doppler direction is considered as an additional pseudo position dimension. The spatial sparsity estimator is extended to the hyper-spatial density estimation and is combined with the Joint Integrated Probabilistic Data Association (JIPDA) tracker to deal with non-parametric multi-target tracking problem with Doppler measurements. Monte Carlo simulation results demonstrate the effectiveness of the proposed approach.