Jianjun Huang

Angle-Doppler imaging based on compressive sensing in random PRF airborne radar

In this paper, a novel method based-on Doppler compressive sampling for the sparsity-based space-time adaptive processing (STAP) is proposed. In our method, the radar transmits a small amount of pulses randomly extracted from the aperiodic pulse sequence. Then, we build the sparse model with Doppler compressive sampling, analyze the capability of solving the Doppler ambiguity, and generate the angle-Doppler image of both targets and clutter for a range bin of interest. Simulation results illustrate that the phenomenon of Doppler ambiguity disappear and the recovery performance of proposed method can approach to that of under uncompressed sampling.

Fuzzy logic-based multi-factor aided multiple-model filter for general aviation target tracking

A fuzzy logic-based multi-factor aided multiple-model filter (FLMAMMF) for General aviation (GA) maneuvering target tracking (MTT) is presented. The target category and meteorological information are introduced into the interacting multiple model (IMM) filter to perform GA target tracking. Fuzzy logic inference is employed in the proposed algorithm to reflect the complicated relationship between these two factors and the transition probability matrix (TPM). Both the number of models in model set and the transition probabilities between models are adjusted through fuzzy inference. Simulation results show that the proposed method is efficient and effective.

A VB-IMM filter for ADS-B data

A variational Bayesian approximation-based interacting multiple model (VB-IMM) filter for automatic dependent surveillance-broadcast (ADS-B) Data is proposed. ADS-B data is a type of measurements with unknown noise variances. The variational Bayesian adaptive Kalman filter (VB-AKF) is a recursively forming separable approximation to the joint distribution of both states and noise parameters by the variational Bayesian method. The proposed algorithm adopts the interacting multiple models (IMM) to update the iteration, and adjusts adaptively its parameters according to the accuracy category of measurements to solve the problem of maneuvering target tracking. Simulation results show that the proposed method can achieve better performance in tracking accuracy in the situations with unknown noise variance.

Robust sparsity-based space-time adaptive processing considering array gain/phase errors

This paper proposes a novel robust sparsity-based space-time adaptive processing (STAP) algorithm considering array gain/phase errors for airborne radar. The proposed algorithm builds the sparse signal model considering the array errors, formulates the sparse problem to jointly estimate clutter spatio-temporal profile and the array gain/phase errors, and then designs the space-time adaptive filter based on the reconstructed spatio-temporal profiles. Simulation results show that the proposed algorithm can greatly eliminate the impacts of the array gain/phase errors and improve the output signal-to-interference-noise ratio (SINR) performance.

Reconstruction for infrared image based on block-sparse compressive sensing

Infrared (IR) image sequences are sparse in nature. This feature has been widely applied to IR image compression. In order to improve the recovery accuracy of IR small target image, a novel scheme is presented in this paper. Firstly, IR small target image is represented as a signal with characteristic of block-sparsity. Then, Bayesian framework is employed to model the temporally correlated source vector of IR image. Finally, the IR image is recovered by using sparse Bayesian learning algorithm. We also present the experimental results with noisy measurements. The numerical results show that the proposed algorithm is more accurate when compared with algorithm for image with single frame, and more importantly, it could complete reconstruct of IR image sequence faster than the algorithm for single image.

Sparsity-based space-time adaptive processing in random pulse repetition frequency and random arrays radar

In this paper, we focus on sparsity-based space-time adaptive processing (STAP) in airborne radar with compressive sampling both in Doppler and spatial domains. Compared with the uniform pluses repetition Frequency (UPRF) and uniform arrays (UA) radar, the designed radar transmits random pulse repetition interval pulses and receives the returns with random arrays, which reduces the number of pulses in one coherent processing interval (CPI) and brings down the number of sensors. Firstly, we build the sparse model with spatial-temporal compressive sampling, and analyze the restricted isometry property (RIP) for the steering dictionary. Then, we recover the clutter angle-Doppler profile via existing sparse recovery algorithms, and design the space-time filter to mitigate the clutter. Simulations are conducted to illustrate the effectiveness of the sparsity-based STAP in random PRF and random arrays radar.

Sparsity-based space-time adaptive processing considering array gain/phase error

This paper introduces a novel sparsity-based spacetime adaptive processing (STAP) considering array gain/phase er-ror (AGPE-STAP) for airborne radar. The proposed AGPE-STAP algorithm combines a conventional sparsity-based STAP method and a conventional array gain/phase error calibration method. The proposed method first models the received returns considering array gain/phase error, estimates the array gain/phase error, calibrates the space-time steering dictionary, and at last designs the filter using the conventional sparsity-based STAP algorithm. Simulation results show that the proposed algorithm outperforms the existing sparsity-based STAP algorithm without calibration in presence of array gain/phase error.

Compressed sensing MIMO radar waveform optimization without signal recovery

It has been proved that compressed sensing (CS) is an efficient technique in MIMO radar system due to their ability to significantly reduce the computational burden. In this paper, we present an iterative waveform optimization algorithm for compressed sensing MIMO radar without signal reconstruction. Using prior information of target and clutter, the transmitted waveforms and the receiving filters are optimized alternatively. By maximizing SINR, the high detection performance of compressed sensing MIMO radar is guaranteed. Without reconstructing the signal, the complexity in receiver can also be decreased. We calculate the SINR of MIMO radar waveforms and compare with its own upper bounds and the SINR corresponding to the LFM waveforms. The numerical results show that using the algorithm in waveform design process, the SINR of compressed sensing MIMO radar waveforms is improved significantly compare to the LFM waveforms.

Space-time adaptive processing airborne radar with coprime pulse repetition interval

In this paper, motivated by the success of coprime array in the direction-of-arrival (DOA) estimation, we introduce the idea of coprime pulse repetition interval (PRI) into the space-time adaptive processing (STAP) airborne radar. Through transmitting and receiving the pulses with coprime PRI, we can reduce the transmitting energy and improve the capabilities of electronic counter-countermeasures (ECCM). We use the lags between the receiving pulses to construct virtual pulses. By using the virtual pulses, we can obtain a new snapshot with a larger dimension than the real one. The constructed snapshots are exploited to estimate the clutter-plus-noise covariance matrix and then to form the STAP filter. Simulation results show that the proposed coprime PRI strategy STAP radar can achieve a good performance with reduced pulses.

Compressive Detection Using Sub-Nyquist Radars for Sparse Signals

This paper investigates the compression detection problem using sub-Nyquist radars, which is well suited to the scenario of high bandwidths in real-time processing because it would significantly reduce the computational burden and save power consumption and computation time. A compressive generalized likelihood ratio test (GLRT) detector for sparse signals is proposed for sub-Nyquist radars without ever reconstructing the signal involved. The performance of the compressive GLRT detector is analyzed and the theoretical bounds are presented. The compressive GLRT detection performance of sub-Nyquist radars is also compared to the traditional GLRT detection performance of conventional radars, which employ traditional analog-to-digital conversion (ADC) at Nyquist sampling rates. Simulation results demonstrate that the former can perform almost as well as the latter with a very small fraction of the number of measurements required by traditional detection in relatively high signal-to-noise ratio (SNR) cases.