Height estimation for automotive MIMO radar with group-sparse reconstruction

A method is developed for sequential azimuth and height estimation of small objects at far distances in front of a moving vehicle using coherent or mutually incoherent MIMO arrays. The model considers phases and amplitudes for near-field multipath signals produced by specular non-diffusive ground-reflections where the reflection phase shift and power attenuation due to the interaction with the ground is assumed unknown. Group-sparsity allows combining measurements along the trajectory of the vehicle provided that the road is flat as well as measurements from multiple incoherent sensors at different locations in the vehicle. It is shown in simulations that the proposed approach significantly increases estimation accuracy and decreases false alarms, both crucial for the detection of small objects at far distances. This model is suitable for non-uniform sparse arrays and can be used for height estimation using efficient methods such as block orthogonal matching pursuit.Comment: 1

Sparse Doppler Sensing Based on Nested Arrays

Spectral Doppler ultrasound imaging allows visualizing blood flow by estimating its velocity distribution over time. Duplex ultrasound is a modality in which an ultrasound system is used for displaying simultaneously both B-mode images and spectral Doppler data. In B-mode imaging short wide-band pulses are used to achieve sufficient spatial resolution in the images. In contrast, for Doppler imaging, narrow-band pulses are preferred in order to attain increased spectral resolution. Thus, the acquisition time must be shared between the two sequences. In this work, we propose a non-uniform slow-time transmission scheme for spectral Doppler, based on nested arrays, which reduces the number of pulses needed for accurate spectrum recovery. We derive the minimal number of Doppler emissions needed, using this approach, for perfect reconstruction of the blood spectrum in a noise-free environment. Next, we provide two spectrum recovery techniques which achieve this minimal number. The first method performs efficient recovery based on the fast Fourier transform. The second allows for continuous recovery of the Doppler frequencies, thus avoiding off-grid error leakage, at the expense of increased complexity. The performance of the techniques is evaluated using realistic Field II simulations as well as in vivo measurements, producing accurate spectrograms of the blood velocities using a significant reduced number of transmissions. The time gained, where no Doppler pulses are sent, can be used to enable the display of both blood velocities and high quality B-mode images at a high frame rate

Adaptive Virtual Waveform Design for Millimeter-Wave Joint Communication-Radar

Joint communication and radar (JCR) waveforms with fully digital baseband generation and processing can now be realized at the millimeter-wave (mmWave) band. Prior work has proposed a mmWave wireless local area network (WLAN)-based JCR that exploits the WLAN preamble for radars. The performance of target velocity estimation, however, was limited. In this paper, we propose a virtual waveform design for an adaptive mmWave JCR. The proposed system transmits a few non-uniformly placed preambles to construct several receive virtual preambles for enhancing velocity estimation accuracy, at the cost of only a small reduction in the communication data rate. We evaluate JCR performance trade-offs using the Cramer-Rao Bound (CRB) metric for radar estimation and a novel distortion minimum mean square error (MMSE) metric for data communication. Additionally, we develop three different MMSE-based optimization problems for the adaptive JCR waveform design. Simulations show that an optimal virtual (non-uniform) waveform achieves a significant performance improvement as compared to a uniform waveform. For a radar CRB constrained optimization, the optimal radar range of operation and the optimal communication distortion MMSE (DMMSE) are improved. For a communication DMMSE constrained optimization with a high DMMSE constraint, the optimal radar CRB is enhanced. For a weighted MMSE average optimization, the advantage of the virtual waveform over the uniform waveform is increased with decreased communication weighting. Comparison of MMSE-based optimization with traditional virtual preamble count-based optimization indicated that the conventional solution converges to the MMSE-based one only for a small number of targets and a high signal-to-noise ratio

Sub-Nyquist Radar: Principles and Prototypes

In the past few years, new approaches to radar signal processing have been introduced which allow the radar to perform signal detection and parameter estimation from much fewer measurements than that required by Nyquist sampling. These systems - referred to as sub-Nyquist radars - model the received signal as having finite rate of innovation and employ the Xampling framework to obtain low-rate samples of the signal. Sub-Nyquist radars exploit the fact that the target scene is sparse facilitating the use of compressed sensing (CS) methods in signal recovery. In this chapter, we review several pulse-Doppler radar systems based on these principles. Contrary to other CS-based designs, our formulations directly address the reduced-rate analog sampling in space and time, avoid a prohibitive dictionary size, and are robust to noise and clutter. We begin by introducing temporal sub-Nyquist processing for estimating the target locations using less bandwidth than conventional systems. This paves the way to cognitive radars which share their transmit spectrum with other communication services, thereby providing a robust solution for coexistence in spectrally crowded environments. Next, without impairing Doppler resolution, we reduce the dwell time by transmitting interleaved radar pulses in a scarce manner within a coherent processing interval or "slow time". Then, we consider multiple-input-multiple-output array radars and demonstrate spatial sub-Nyquist processing which allows the use of few antenna elements without degradation in angular resolution. Finally, we demonstrate application of sub-Nyquist and cognitive radars to imaging systems such as synthetic aperture radar. For each setting, we present a state-of-the-art hardware prototype designed to demonstrate the real-time feasibility of sub-Nyquist radars.Comment: 51 pages, 26 figures, 2 tables, Book chapter. arXiv admin note: text overlap with arXiv:1611.0644

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 non-adaptive mechanism.Comment: 29 pages, 13 figure

A Class of Novel STAP Algorithms Using Sparse Recovery Technique

A class of novel STAP algorithms based on sparse recovery technique were presented. Intrinsic sparsity of distribution of clutter and target energy on spatial-frequency plane was exploited from the viewpoint of compressed sensing. The original sample data and distribution of target and clutter energy was connected by a ill-posed linear algebraic equation and popular $L_1$ optimization method could be utilized to search for its solution with sparse characteristic. Several new filtering algorithm acting on this solution were designed to clean clutter component on spatial-frequency plane effectively for detecting invisible targets buried in clutter. The method above is called CS-STAP in general. CS-STAP showed their advantage compared with conventional STAP technique, such as SMI, in two ways: Firstly, the resolution of CS-STAP on estimation for distribution of clutter and target energy is ultra-high such that clutter energy might be annihilated almost completely by carefully tuned filter. Output SCR of CS-STAP algorithms is far superior to the requirement of detection; Secondly, a much smaller size of training sample support compared with SMI method is requested for CS-STAP method. Even with only one snapshot (from target range cell) could CS-STAP method be able to reveal the existence of target clearly. CS-STAP method display its great potential to be used in heterogeneous situation. Experimental result on dataset from mountaintop program has provided the evidence for our assertion on CS-STAP.Comment: 8 pages, 5 figure

High Resolution FDMA MIMO Radar

Traditional multiple input multiple output radars, which transmit orthogonal coded waveforms, suffer from range-azimuth resolution trade-off. In this work, we adopt a frequency division multiple access (FDMA) approach that breaks this conflict. We combine narrow individual bandwidth for high azimuth resolution and large overall total bandwidth for high range resolution. We process all channels jointly to overcome the FDMA range resolution limitation to a single bandwidth, and address range-azimuth coupling using a random array configuration.Comment: arXiv admin note: substantial text overlap with arXiv:1608.0779

Joint Doppler and Channel Estimation with Nested Arrays for Millimeter Wave Communications

Channel estimation is essential for precoding/combining in millimeter wave (mmWave) communications. However, accurate estimation is usually difficult because the receiver can only observe the low-dimensional projection of the received signals due to the hybrid architecture. We take the high speed scenario into consideration where the Doppler effect caused by fast-moving users can seriously deteriorate the channel estimation accuracy. In this paper, we propose to incorporate the nested array into analog array architecture by using RF switch networks with an objective of reducing the complexity and power consumption of the system. Based on the covariance fitting criterion, a joint Doppler and channel estimation method is proposed without need of discretizing the angle space, and thus the model mismatch effect can be totally eliminated. We also present an algorithmic implementation by solving the dual problem of the original one in order to reduce the computational complexity. Numerical simulations are provided to demonstrate the effectiveness and superiority of our proposed method

Sparsity-Based STAP Design Based on Alternating Direction Method with Gain/Phase Errors

We present a novel sparsity-based space-time adaptive processing (STAP) technique based on the alternating direction method to overcome the severe performance degradation caused by array gain/phase (GP) errors. The proposed algorithm reformulates the STAP problem as a joint optimization problem of the spatio-Doppler profile and GP errors in both single and multiple snapshots, and introduces a target detector using the reconstructed spatio-Doppler profiles. Simulations are conducted to illustrate the benefits of the proposed algorithm.Comment: 7 figures, 1 tabl

Pulse-Doppler Signal Processing with Quadrature Compressive Sampling

Quadrature compressive sampling (QuadCS) is a newly introduced sub-Nyquist sampling for acquiring inphase and quadrature (I/Q) components of radio-frequency signals. For applications to pulse-Doppler radars, the QuadCS outputs can be arranged in 2-dimensional data similar to that by Nyquist sampling. This paper develops a compressive sampling pulse-Doppler (CoSaPD) processing scheme from the sub-Nyquist samples. The CoSaPD scheme follows Doppler estimation/detection and range estimation and is conducted on the sub-Nyquist samples without recovering the Nyquist samples. The Doppler estimation is realized through spectrum analyzer as in classic processing. The detection is done on the Doppler bin data. The range estimation is performed through sparse recovery algorithms on the detected targets and thus the computational load is reduced. The detection threshold can be set at a low value for improving detection probability and then the introduced false targets are removed in the range estimation stage through inherent detection characteristic in the recovery algorithms. Simulation results confirm our findings. The CoSaPD scheme with the data at one eighth the Nyquist rate and for SNR above -25dB can achieve performance of the classic processing with Nyquist samples.Comment: 37 pages, 18 figure