A Simplified Sub-Nyquist Receiver Architecture for Joint DOA and Frequency Estimation

Joint estimation of carrier frequency and direction of arrival (DOA) for multiple signals has been found in many practical applications such as Cognitive Radio (CR). However, Nyquist sampling mechanism is costly or implemented due to wide spectrum range. Taking advantage of sub-Nyquist sampling technology, some array receiver architectures are proposed to realize joint estimation of carrier frequency and DOA. To further decrease equivalent sampling rate and hardware complexity, we propose a simplifying receiver architecture based on our previous work. We come up with joint DOA and frequency estimation algorithms for the novel architecture. The simulations demonstrate that the receiver architecture and the proposed approaches are feasible.Comment: arXiv admin note: text overlap with arXiv:1604.0503

Joint DOA and Frequency Estimation with Sub-Nyquist Sampling

In this paper, to jointly estimate the frequency and the direction-of-arrival(DOA) of the narrowband far-field signals, a novel array receiver architecture is presented by the concept of the sub-Nyquist sampling techniques. In particular, our contribution is threefold. i) First, we propose a time-space union signal reception model for receiving array signals, where the sub-Nyquist sampling techniques and arbitrary array geometries are employed to decrease the time-domain sampling rate and improve the DOA estimation accuracy. A better joint estimation is obtained in the higher time-space union space. ii) Second, two joint estimation algorithms are proposed for the receiving model. One is based on a trilinear decomposition from the third-order tensor theory and the other is based on subspace decomposition. iii) Third, we derive the corresponding Cram\'er\text{-}Rao Bound (CRB) for frequency and DOA estimates. In the case of the branch number of our architecture is equal to the reduction factor of the sampling rate, it is observed that the CRB is robust in terms of the number of signals, while the CRB based on the Nyquist sampling scheme will increase with respect to the number of signals. In addition, the new steer vectors of the union time-space model are completely uncorrelated under the limited number of sensors, which improves the estimation performance. Furthermore, the simulation results demonstrate that our estimates via the receiver architecture associated with the proposed algorithms closely match the CRB according to the noise levels, the branch number and the source number as well

Phased Array-Based Sub-Nyquist Sampling for Joint Wideband Spectrum Sensing and Direction-of-Arrival Estimation

In this paper, we study the problem of joint wideband spectrum sensing and direction-of-arrival (DoA) estimation in a sub-Nyquist sampling framework. Specifically, considering a scenario where a few uncorrelated narrowband signals spread over a wide (say, several GHz) frequency band, our objective is to estimate the carrier frequencies and the DoAs associated with the narrowband sources, as well as reconstruct the power spectra of these narrowband signals. To overcome the sampling rate bottleneck for wideband spectrum sensing, we propose a new phased-array based sub-Nyquist sampling architecture with variable time delays, where a uniform linear array (ULA) is employed and the received signal at each antenna is delayed by a variable amount of time and then sampled by a synchronized low-rate analog-digital converter (ADC). Based on the collected sub-Nyquist samples, we calculate a set of cross-correlation matrices with different time lags, and develop a CANDECOMP/PARAFAC (CP) decomposition-based method for joint DoA, carrier frequency and power spectrum recovery. Perfect recovery conditions for the associated parameters and the power spectrum are analyzed. Our analysis reveals that our proposed method does not require to place any sparse constraint on the wideband spectrum, only needs the sampling rate to be greater than the bandwidth of the narrowband source signal with the largest bandwidth among all sources. Simulation results show that our proposed method can achieve an estimation accuracy close to the associated Cram\'{e}r-Rao bounds (CRBs) using only a small number of data samples