Fengzhou Dai

Adaptive Detection of Wideband Radar Range Spread Targets with Range Walking in Clutter

In this paper, we address detection of range spread targets with range walking in partially homogeneous clutter, which is often encountered in wideband radar. By considering the target range walking effect, a target returns model is proposed that represents the relationship between Doppler frequency and range frequency. Based on this, a generalized matched subspace detector (GMSD) and a general adaptive subspace detector (GASD) are proposed in the frequency domain. Moreover, to eliminate excess integration loss introduced by the mismatched detection window size, two improved detectors are proposed in range domain. The theoretical analysis shows that the GMSDs are of a constant false alarm rate (CFAR) with respect to both the power and the covariance matrix of the clutter but that the GASDs are of a CFAR with respect to the clutter power and an approximate CFAR with respect to the clutter covariance matrix. Simulated experiments are carried out to verify the effectiveness of the proposed detectors.

Radar HRRP target recognition in frequency domain based on autoregressive model

In this paper, we adopt the autoregressive (AR) model to characterize the frequency spectrum amplitude of high-resolution range profile (HRRP) and extract the AR and partial correlation (PARCOR) coefficients, which are invariant to the initial-phase, translation and scale changes of HRRP, as discriminating features. Moreover, a mixture model based frame partition method is proposed and a Bayesian Ying-Yang (BYY) harmony learning algorithm is adopted to determine the frame number automatically during parameter learning. Experimental results based on measured data demonstrate the proposed features are superior to others in their minor frame number, robustness to sample size and good rejection ability.

A fast efficient power allocation algorithm for target localization in cognitive distributed multiple radar systems

It is well-known that the power allocation can enhance the power utilization of the distributed radar systems. We first analyze two interesting non-increasing properties of Cramer-Rao low bound (CRLB) for target location via distributed multiple radar systems. On the basis of the classical power allocation methods 15, this paper proposes a fast efficient power allocation algorithm applied to cognitive distributed multiple radar systems, which depends greatly on an alternating global search algorithm(AGSA). In this paper, our aim is directly to minimize the non-convex CRLB 15 of target location estimation. The convergence of the proposed algorithm is theoretically analyzed by LaSalle invariance principle. We analyze the computational complexity of the two closely-related algorithms. The famous Pareto optimal set associated with power allocation is obtained by the proposed algorithm, and it can indirectly derive the solution to problem for minimizing total power budget. Experimental results demonstrate that our algorithm has quick convergence and good performance.

A Fused-Lasso-Based Doppler Imaging Algorithm for Spinning Targets With Occlusion Effect

The occlusion effect of targets with the spinning motion is usually ubiquitous when illuminated by the radar. Considering this effect in a single range Doppler imaging for spinning targets is of practical significance. In this paper, we develop a novel approach to image spinning targets with occlusion effect, which is referred to as a fused-Lasso-based Doppler-only snapshot imaging. Due to the spinning motion, the radar illuminates different parts of the target at each observation instant, which causes the backscattering intensities of the scatterers, are slowly time-varying. The 2D fused-Lasso model is introduced to capture this time-varying characteristic. Furthermore, the underlying factors for the imaging performance are thoughtfully analyzed. The proposed method is able to adaptively estimate the shadowed region, do Doppler imaging, and work well in low radar pulse repetition frequency situations. Finally, a series of simulations is carried out to validate the performance of the proposed method.

Generalized adaptive subspace detector for range-Doppler spread target with high resolution radar

A generalized adaptive subspace detector for range-Doppler spread target (RDST-GASD) in the non-Gaussian clutter is derived in this paper. The subspace model of multi-pulse wideband radar target returns is established in the frequency-slow time domain. The clutters are modeled as nonhomogeneous spherically invariant random vectors (SIRVs); that is, the power of the clutter is different from one range cell to another. The clutter covariance matrix is estimated with the secondary data. The constant false alarm rate (CFAR) property of RDST-GASD with respect to both the power and the covariance matrix of the clutter is demonstrated theoretically. Considering that there is target range walking across range cells during a coherent processing interval (CPI) for wideband radar, the RDST-GASD does range alignment to the multiple returns of the target in a CPI. As a result, the coherent integration is implemented and the detection performance is improved.

Knowledge-based wideband radar target detection in the heterogeneous environment

Abstract In this paper, we address wideband radar target detection in the heterogeneous environment. Firstly, a linear model of the wideband radar target return with the steering vector dispersion is established. Secondly, the heterogeneous clutter is modeled as a two-dimensional wide-sense stationary (WSS) process with inverse complex Wishart distributed random covariance matrices in the time-space and frequency domain. Then, several generalized likelihood ratio test (GLRT) based detectors are designed, some of which integrate the prior knowledge of the clutter covariance matrix with the Bayesian approach, while the others are with the heuristic approach. Finally, the performance of the detectors is evaluated by simulations, and the results show that the detectors based on the Bayesian approach outperform the other detectors.

Motion-parameter estimation for precession-with-nutation space targets based on wideband radar measurements

Nutation and precession parameter estimation is of great importance for attitude estimation of spin-stabilized space targets. In this paper, we propose a novel method to estimate the nutation and precession parameters of a rigid-body object from the one-dimensional high-resolution radial-range measurements. First, we elaborately derive the kinematic equations and one-dimensional radial-projection model of the precession-with-nutation object, where fixed-point and slipping scatterers are separately taken into account. Second, by using the radial-range trajectories of multiple scatterers, we employ the factorization-based algorithm to obtain the motion reconstruction. Third, we estimate the nutation and precession parameters based on the reconstructed motion and the spectrum analysis of the associated trajectories. Moreover, the l1-norm-based one-dimensional trajectory-recovering technique is developed to deal with the incomplete radial-range trajectories caused by the shadowing effect, specularity, and so on. Finally, experiments are carried out on the electromagnetic analysis data to verify the proposed model and parameter-estimation method.

Joint-sparse estimation of migration targets with velocity ambiguity

Range migration is the basic and troublesome problem in moving target detection for wideband radar. In this paper, we propose a joint-sparse estimation algorithm that gives a sparse representation of migration targets with velocity ambiguity. The proposed algorithm utilizes a non-ambiguous representation of the wideband signals to alleviate the velocity ambiguity, then the estimation of the targets scenario is achieved by joint-sparse recovery. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm in case of a multi-target scenario.

A heterogeneous Markov chain model of target existence variable for Bayesian track-before-detect

A heterogeneous Markov chain (MC) model, instead of the homogeneous MC model, is proposed in this paper to describe target existence variable for Bayesian track-before-detect. The proposed model is more consistent with the transitions of target existence variable, which leads to the improvement of the performance of detection and tracking.

Detection performance comparison for wideband and narrowband radar in noise

The detection performance of wideband radars in noise is better than that of the narrowband radars under some conditions, due to higher range resolution and less target return fluctuation. The detection probabilities of wideband and narrowband radars for the wideband non-fluctuation, Rayleigh and Ricean target models in white Gaussian noise are deduced. The detection curves show that the wideband radars outperform the narrowband radars in detection performance in the case of high detection probabilities. But the detection predominance of the wideband radars is meaningless when the bandwidth of the radar is increased to a certain extent, because the integration loss of the wideband radar energy integration detector is increased with the increasing range resolution.