In this paper, we propose a direction-of-arrival (DOA) estimation method by combining multiple signal classification (MUSIC) of two decomposed linear arrays for the corresponding coprime array signal processing. The title “DECOM” means that, first, the nonlinear coprime array needs to be DECOMposed into two linear arrays, and second, Doa Estimation is obtained by COmbining the MUSIC results of the linear arrays, where the existence and uniqueness of the solution are proved. To reduce the computational complexity of DECOM, we design a two-phase adaptive spectrum search scheme, which includes a coarse spectrum search phase and then a fine spectrum search phase. Extensive simulations have been conducted and the results show that the DECOM can achieve accurate DOA estimation under different SNR conditions.

DECOM: DOA estimation with combined MUSIC for coprime array

Algorithm design for parallel implementation of the SMC-PHD filter

The present work introduces a new optimization technique suitable for adaptive beamforming of linear antenna arrays. The proposed technique is a new PSO variant called Adaptive Mutated Boolean PSO (AMBPSO) where the update formulae are implemented exclusively in Boolean form by using an e-ciently adaptive mutation process. The AMBPSO aims at estimating the excitation weights applied on the array elements considering that a desired signal and several interference signals are received by the array at respective directions of arrival. In order to exhibit the robustness of the technique, the optimization process does not take into account the interference correlation matrix. A certain power level of additive Gaussian noise is also considered by the technique. The AMBPSO has been applied in several cases of uniform linear antenna arrays with difierent spacing between adjacent elements and difierent noise power level and therefore seems to be quite promising in the smart antenna technology.

/pdf/a-novel-adaptive-beamforming-technique-applied-on-linear-1tgi9cuwz6.pdf

A Novel Adaptive Beamforming Technique Applied on Linear Antenna Arrays Using Adaptive Mutated Boolean PSO

A novel blind Direction of arrival (DOA) and polarization estimation method for polarization-sensitive uniform circular array is investigated in this paper. An analysis of the received signal of the polarization-sensitive uniform circular array shows that the received signal has trilinear model characteristics, and hence trilinear decomposition-based blind DOA and polarization estimation for polarization sensitive uniform circular array is proposed in this paper. Our proposed algorithm has better DOA and polarization estimation performance. Our proposed algorithm can be thought of as a generalization of ESPRIT, and has wider application than ESPRIT method. The useful behavior of the proposed algorithm is verified by simulations.

/pdf/novel-blind-joint-direction-of-arrival-and-polarization-6ekdsc5sxf.pdf

Novel blind joint direction of arrival and polarization estimation for polarization-sensitive uniform circular array

The performance of traditional beamformers tends to degrade due to inaccurate estimation of covariance matrix and imprecise knowledge of array steering vector. The inaccurate estimation of covariance matrix can be attributed to limited data samples and the presence of desired signal in the training data. The mismatch between the actual and presumed steering vectors can be due to the error in the position (geometry) and/or in the look direction estimate. In this paper, we propose a difierential evolution (DE) based robust adaptive beamforming that is able to achieve near optimal performance even in the presence of geometry error. Initially, we estimate an optimal steering vector by maximizing and minimizing the signal power in and out of the desired signal's angular range, respectively. Then, we estimate the look direction and reconstruct the covariance matrix. Based on the obtained steering vector, estimate for look direction and reconstructed covariance matrix, near optimal output SINR, can be obtained with the increase in the input SNR without observing any saturation even in the presence of geometry error. Numerical simulations are presented to demonstrate the e-cacy of the proposed algorithm.

/pdf/a-differential-evolution-approach-for-robust-adaptive-3dy6gbaqja.pdf

A Differential Evolution Approach for Robust Adaptive Beamforming Based on Joint Estimation of Look Direction and Array Geometry

The adaptive beamformers often suffer severe performance degradation when there exist uncertainties in the steering vector of interest. In this paper, we develop a new approach to robust adaptive beamforming in the presence of an unknown signal steering vector. Based on the observed data, we try to estimate an equivalent directionof-arrival (DOA) for each sensor, in which all factors causing the steering vector uncertainties are ascribed to the DOA uncertainty only. The equivalent DOA of each sensor can be estimated one by one with the assumption that the elements of the steering vector are uncorrelated with each other. Using a Bayesian approach, the equivalent DOA estimator of each sensor is a weighted sum of a set of candidate DOA’s, which are combined according to the value of the a posteriori probability for each pointing direction. In this way, the signal steering vector and the diagonal loading sample matrix inversion (DL-SMI) version adaptive beamformer can be obtained. Numerical simulations illustrate the robustness of the proposed beamforming algorithm.

/pdf/robust-adaptive-beamforming-for-steering-vector-47t5987h9x.pdf

Robust Adaptive Beamforming for Steering Vector Uncertainties Based on Equivalent DOAs Method

In this paper, we propose a novel fuzzy-control-based particle fllter (FCPF) for maneuvering target tracking, which combines the advantages of standard particle fllter (SPF) and multiple model particle fllter (MMPF). That is, the SPF is adopted during non-maneuvering movement while the MMPF is adopted during maneuvering movement. The key point of the FCPF is to use a fuzzy controller, which could imitate the thoughts of human beings in some degree, to detect the target's maneuver and use a backward correction sub-algorithm to alleviate the performance degradation of MMPF caused by detection delay. Simulation results indicate that the proposed algorithm has a much better tracking accuracy than the SPF while keeps approximately equal computational complexity. Compared with MMPF, both algorithms have no tracking lost, but the tracking accuracy of the proposed FCPF is a little better than the MMPF, and the FCPF consumes about 66% computation time of the MMPF. Thus, the proposed algorithm ofiers a more efiective way for maneuvering target tracking.

/pdf/fuzzy-control-based-particle-filter-for-maneuvering-target-13wlf33ba6.pdf

Fuzzy-control-based particle filter for maneuvering target tracking

Adaptive beamforming, which uses a weight vector to maximize the signal-to-interference-plus-noise ratio (SINR), is often sensitive to estimation error and uncertainty in the parameters, such as direction of arrival (DOA), steering vector and covariance matrix. Robust beamforming attempts to mitigate this sensitivity and diagonal loading in sample covariance matrix can improve the robustness. In this paper, beamformer based on particle fllter (PF) is proposed to improve the robustness by optimizing the diagonal loading factor in sample covariance matrix. In the proposed approach, the level of diagonal loading is regarded as a group of particles and optimized using PF. In order to compute the post probability of particles beyond the knowledge of noise, a simplifled cost function is derived flrst. Then, a statistical approach is developed to decide the level of diagonal loading. Finally, simulations with several frequently encountered types of estimation error are conducted. Results show a better performance of the proposed beamformer than other typical beamformers using diagonal loading. In particular, the prominent advantage of the proposed approach is that it can perform well even noise and error in the steering vector are unknown.

/pdf/robust-adaptive-beamforming-based-on-particle-filter-with-56x14melua.pdf

Robust Adaptive Beamforming Based on Particle Filter with Noise Unknown

In this paper, we propose a simplifled particle probability hypothesis density (PHD) fllter and its hardware implementation for multiple-target tracking (MTT). In the proposed algorithm, the update step of particle PHD fllter is simplifled and the time-varying number of measurements is arranged in combination series/parallel mode. This may result in flxed hardware architecture and therefore present a convenient hardware implementation of particle PHD fllter. Simulation results indicate that for the MTT problems, this proposed simplifled algorithm shows similar performance with the standard particle PHD fllter but has faster processing rate. Experiment study shows that the proposed simplifled algorithm can be e-ciently implemented in hardware and can efiectively solve the MTT problems.

/pdf/simplified-particle-phd-filter-for-multiple-target-tracking-2jvpxq894b.pdf

Simplified Particle PHD Filter for Multiple-Target Tracking: Algorithm and Architecture

This paper aims at finding an algorithm featuring good estimation performance and easy hardware implementation for tracking airborne target hidden in blind Doppler. Incorporating the current statistical model which is effective in dealing with the maneuvering motions that most blind Doppler issues are caused, a current statistical model particle filter (CSM-PF) is presented in this paper for tracking airborne targets hidden in blind Doppler. Simulation results demonstrate that the proposed CSM-PF shows similar performance with the interacting multiple model particle filter (IMM-PF) in terms of tracking accuracy and track continuity, but it avoids the difficulty of model selection for maneuvering targets. In addition, when hardware implementation is considered, the proposed CSM-PF has lower processing latency, fewer resource utilization and lower hardware complexity than the IMM-PF.

/pdf/tracking-airborne-targets-hidden-in-blind-doppler-using-1v3g7srp0j.pdf

Kang Sheng Chen

Papers

Robust Adaptive Beamforming for Steering Vector Uncertainties Based on Equivalent DOAs Method

Fuzzy-control-based particle filter for maneuvering target tracking

Robust Adaptive Beamforming Based on Particle Filter with Noise Unknown

Simplified Particle PHD Filter for Multiple-Target Tracking: Algorithm and Architecture

Tracking Airborne Targets Hidden in Blind Doppler Using Current Statistical Model Particle Filter