Direction of arrival (DOA) estimation for coprime multiple-input multiple-output radar is studied, and a combined unitary estimation of signal parameters via rotational invariance technique (ESPRIT)-based algorithm is proposed. The transmitter and the receiver adopt coprime arrays, which are sparse but still uniform. Therefore, unitary ESPRIT is first used to obtain arbitrary ambiguous DOA estimations based on the rotational invariances of transmit and receive arrays, respectively. After recovering all the other estimations, unique DOA estimation is achieved by finding the coincide results from transmit and receive arrays based on the coprimeness. The proposed algorithm obtains more accurate DOA estimation, achieves higher angle resolution, and identifies more targets than conventional methods. Multiple simulations are conducted to verify the improvement of the proposed algorithm.

Direction of arrival (DOA) estimation using coprime array is studied, and an extended-aperture unitary root multiple signal classification (MUSIC)-based method is proposed. The geometry of prototype coprime array is modified through some translations, which enable one subarray of the coprime array to achieve aperture extension after unitary transformation. Then, the aperture of the other subarray can also be extended based on the rotational invariance of the extended subarray, and 2-D parameter estimations from the two extended subarrays can be achieved in succession via 1-D root MUSIC-based technique. Finally, unique DOA is determined from the intersection of the two automatically paired and coprime estimations. In contrast to the partial search MUSIC method, the estimation of signal parameters via rotational invariance technique based method, the root MUSIC-based method, and the real-valued cross covariance matrix based method, the proposed method gives better DOA estimation results and manages more sources. Furthermore, it has low complexity for the real-valued decomposition. Simulation results verify the improvement of the proposed approach.

The global positioning system (GPS), which provides ubiquitous location-awareness with a constellation of satellites, has become an instrumental function of multiple mass-market applications. Satellite signals, however, may not be capable of penetrating obstacles in harsh environments (e.g., urban canyons, tree canopies, and flyovers). Hence, GPS may not provide adequate localization accuracy for applications like autonomous vehicles. Resorting to data fusion of heterogeneous signals emanating from multiple anchors, we advocate a self-localization method that provides accurate estimates of the vehicle position. To be more specific, several heterogenous emitters whose positions are known are used as anchors to determine the vehicle's position based on the weighted direct position determination (DPD) method that eliminates nonhomogeneity among different emitters. However, the weighted DPD method requires an exhaustive search of the parameter search space and is thus time-consuming. To reduce the computational burden, we propose a weighted cascade compensation estimator (WCCE) that is tailored for real-time tracking and self-localization. The proposed WCCE outperforms traditional DPD methods in terms of computational complexity while achieving nearly comparable localization accuracy. The effectiveness of the proposed method is corroborated by extensive simulated examples.

The increasing of illegal radiations, which are either artificial or unintentional, has seriously influenced the reliable communication and operation of industrial facilities. In this article, we discuss the simultaneous localization of multiple emitters based on big data monitored by a moving unmanned aerial vehicle. Conventional direct position determination (DPD) method suffers from the non-homogeneity of the observation error and is sensitive to the environment, so we develop the weight DPD methods. First, we consider to strengthen the spectrums obtained at slots with higher signal-to-noise ratio, which is blindly calculated. Thereafter, an improved weight is designed to further enhance the localization accuracy, and it can obtain the asymptotically optimal performance under the general Gaussian noise model, which is proved theoretically. Numerical simulations demonstrate that the proposed weight DPD methods outperform the conventional two-step methods and subspace data fusion DPD method in terms of localization accuracy and resolution.

Direction of arrival (DOA) estimation of quasi-stationary signals (QSS) is discussed in this paper, and an algorithm using unfolded coprime array is proposed. Coprime array consists of two uniform linear subarrays with coprime relationship, and the two subarrays of the unfolded coprime array in this paper are arranged along the positive axis and negative axis, respectively. Through the vectorization of the low dimensional cross-covariance matrix and the further exploiture of the non-circularity within the second-order statistics of QSS, 2MN-1 degrees of freedom can be achieved with M+N physical elements. The uniqueness of the DOA estimation based on the non-continuous virtual elements generated from the unfolded coprime array is proved, and unitary polynomial root finding technique is employed to estimate the DOA. The proposed algorithm has low complexity, and it can obtain better DOA estimation performance and handle more sources than Khatri-rao subspace approach and original coprime array-based method. Simulation results verify the effectiveness of the proposed approach.

A two dimensional off-grid direction of arrival (DOA) estimation method using unfolded parallel coprime array (UPCA) is proposed. The proposed UPCA ensures large inter- element spacing, which is robust to the mutual coupling problem, and two dimensional DOA estimation problem is transformed into one dimensional sparse representation (SR), which reduces the computation complexity. Besides, the off-grid DOA problem is taken into account and compensated through joint sparse recovery, which only requires a rough grid. Without the prior knowledge of source number, the proposed algorithm achieves improved DOA estimation results while requiring reduced com- putation complexity when compared to other SR based methods. Simulation results verify the effectiveness of the algorithm.