Huadong Meng

A novel STAP algorithm using sparse recovery technique

A novel STAP algorithm based on sparse recovery technique, called CS-STAP, were presented. Instead of using conventional maximum likelihood estimation of covariance matrix, our method utilizes the echo statistics on spatial-temporal plane, which is extracted from sample data of only ONE training range cell with Compressed Sensing techniques, to construct a new estimator of covariance matrix, and build the optimal detector based on it. Full description of CS-STAP is given. Numerical result on real data has provided the evidence for great potential of CS-STAP as a effective approach when clutter is non-stationary because it need much less training data compared with common STAP methods.

A Low-cost Vehicle Detection and Classification System based on Unmodulated Continuous-wave Radar

Vehicle detection and classification system is an important part of the intelligent transportation systems (ITS). Its function is to measure traffic parameters such as flow-rate, speed, and vehicle types, which are valuable information for applications of road surveillance, traffic signal control, road planning, and so on. This paper presents a novel low-cost vehicle detection and classification system which is based on a K-band unmodulated CW radar. This system utilizes time-frequency analysis, multi-threshold detection, and Hough transform as the major signal processing methods to extract speed and shape information of vehicles from Doppler signature they generate. It can perform vehicle detection, speed measurement, and vehicle classification simultaneously. Experimental results show that the proposed system and algorithms can provide promising performance and accuracy.

Velocity Estimation and Range Shift Compensation for High Range Resolution Profiling in Stepped-Frequency Radar

In this letter, a novel radial velocity estimation and range shift compensation algorithm is proposed for high-range resolution profiling of moving targets in stepped-frequency (SF) radar. Compared to traditional methods, this algorithm is based on a more precise signal model, and can therefore achieve much higher estimation accuracy. Furthermore, the range shift problem caused by target motion can be resolved without alterations to the radar waveform. The performance of this algorithm is demonstrated using simulated and experimental results.

Cognitive random stepped frequency radar with sparse recovery

Random stepped frequency (RSF) radar, which transmits random-frequency pulses, can suppress the range ambiguity, improve convert detection, and possess excellent electronic counter-countermeasures (ECCM) ability [1]. In this paper, we apply a sparse recovery method to estimate the range and Doppler of targets. We also propose a cognitive mechanism for RSF radar to further enhance the performance of the sparse recovery method. The carrier frequencies of transmitted pulses are adaptively designed in response to the observed circumstance. We investigate the criterion to design carrier frequencies, and efficient methods are then devised. Simulation results demonstrate that the adaptive frequency-design mechanism significantly improves the performance of target reconstruction in comparison with the nonadaptive mechanism.

Adaptive matching pursuit with constrained total least squares

Compressive sensing (CS) can effectively recover a signal when it is sparse in some discrete atoms. However, in some applications, signals are sparse in a continuous parameter space, e.g., frequency space, rather than discrete atoms. Usually, we divide the continuous parameter into finite discrete grid points and build a dictionary from these grid points. However, the actual targets may not exactly lie on the grid points no matter how densely the parameter is grided, which introduces mismatch between the predefined dictionary and the actual one. In this article, a novel method, namely adaptive matching pursuit with constrained total least squares (AMP-CTLS), is proposed to find actual atoms even if they are not included in the initial dictionary. In AMP-CTLS, the grid and the dictionary are adaptively updated to better agree with measurements. The convergence of the algorithm is discussed, and numerical experiments demonstrate the advantages of AMP-CTLS.Compressive sensing (CS) can effectively recover a signal when it is sparse in some discrete atoms. However, in some applications, signals are sparse in a continuous parameter space, e.g., frequency space, rather than discrete atoms. Usually, we divide the continuous parameter into finite discrete grid points and build a dictionary from these grid points. However, the actual targets may not exactly lie on the grid points no matter how densely the parameter is grided, which introduces mismatch between the predefined dictionary and the actual one. In this article, a novel method, namely adaptive matching pursuit with constrained total least squares (AMP-CTLS), is proposed to find actual atoms even if they are not included in the initial dictionary. In AMP-CTLS, the grid and the dictionary are adaptively updated to better agree with measurements. The convergence of the algorithm is discussed, and numerical experiments demonstrate the advantages of AMP-CTLS.

Motion Compensation of Moving Targets for High Range Resolution Stepped-Frequency Radar

High range resolution (HRR) profiling using stepped-frequency pulse trains suffers from range shift and the attenuation/dispersion of range profiles while the target of interest is moving. To overcome these two drawbacks, a new algorithm based on the maximum likelihood (ML) estimation is proposed in this paper. Without altering the conventional stepped-frequency waveform, this algorithm can estimate the target velocity and thereby compensate the phase errors caused by the targets motion. It is shown that the velocity can be accurately estimated and the range profile can be correctly reconstructed.

Performance Limits and Geometric Properties of Array Localization

Location-aware networks are of great importance and interest in both civil and military applications. This paper determines the localization accuracy of an agent, which is equipped with an antenna array and localizes itself using wireless measurements with anchor nodes, in a far-field environment. In view of the Cramér-Rao bound, we first derive the localization information for static scenarios and demonstrate that such information is a weighed sum of Fisher information matrices from each anchor-antenna measurement pair. Each matrix can be further decomposed into two parts: 1) a distance part with intensity proportional to the squared baseband effective bandwidth of the transmitted signal and 2) a direction part with intensity associated with the normalized anchor-antenna visual angle. Moreover, in dynamic scenarios, we show that the Doppler shift contributes additional direction information, with intensity determined by the agent velocity and the root mean squared time duration of the transmitted signal. In addition, two measures are proposed to evaluate the localization performance of wireless networks with different anchor-agent and array-antenna geometries, and both formulae and simulations are provided for typical anchor deployments and antenna arrays.

Adaptive Sparse Representation for Source Localization with Gain/Phase Errors

Sparse representation (SR) algorithms can be implemented for high-resolution direction of arrival (DOA) estimation. Additionally, SR can effectively separate the coherent signal sources because the spectrum estimation is based on the optimization technique, such as the L1 norm minimization, but not on subspace orthogonality. However, in the actual source localization scenario, an unknown gain/phase error between the array sensors is inevitable. Due to this nonideal factor, the predefined overcomplete basis mismatches the actual array manifold so that the estimation performance is degraded in SR. In this paper, an adaptive SR algorithm is proposed to improve the robustness with respect to the gain/phase error, where the overcomplete basis is dynamically adjusted using multiple snapshots and the sparse solution is adaptively acquired to match with the actual scenario. The simulation results demonstrate the estimation robustness to the gain/phase error using the proposed method.

Collaborative penalized Gaussian mixture PHD tracker for close target tracking

Abstract Gaussian mixture probability hypothesis density (GM-PHD) recursion is a promising, computationally tractable implementation for the probability hypothesis density (PHD) filter. The competitive GM-PHD (CGM-PHD) and penalized GM-PHD (PGM-PHD) filters employ renormalization schemes to refine the weights assigned to each target and improve the estimation performance of the GM-PHD filter for closely spaced targets. However, these methods do not provide target trajectories over time, and the problem of wrongly identifying close targets is not still solved for the GM-PHD tracker. In this paper, we propose a collaborative penalized scheme to overcome the drawbacks of the GM-PHD tracker using the track label of each Gaussian component in the GM-PHD recursion. The simulation results show that the collaborative penalized GM-PHD (CPGM-PHD) tracker not only improves the estimation accuracy of the number of target and states but also provides the correct identities of targets in close proximity.

Extended Target Recognition in Cognitive Radar Networks

We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS). Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches.