We consider the design of polyphase waveforms for ground moving target detection with airborne multiple-inputmultiple-output (MIMO) radar. Due to the constant-modulus and finite-alphabet constraint on the waveforms, the associated design problem is non-convex and in general NP-hard. To tackle this problem, we develop an efficient algorithm based on relaxation and cyclic optimization. Moreover, we exploit a reparameterization trick to avoid the significant computational burden and memory requirement brought about by relaxation. We prove that the objective values during the iterations are guaranteed to converge. Finally, we provide an effective randomization approach to obtain polyphase waveforms from the relaxed solution at convergence. Numerical examples show the effectiveness of the proposed algorithm for designing polyphase waveforms.Index Terms-Multiple-input-multiple-output (MIMO) radar, space time adaptive processing (STAP), signal-to-interferenceplus-noise-ratio (SINR), waveform design, polyphase waveforms.

The groundwater pressure response to the ubiquitous Earth and atmospheric tides provides a largely untapped opportunity to passively characterize and quantify subsurface hydro-geomechanical properties. However, this requires reliable extraction of closely spaced harmonic components with relatively subtle amplitudes but well-known tidal periods from noisy measurements. The minimum requirements for the suitability of existing groundwater records for analysis are unknown. This work systematically tests and compares the ability of two common signal processing methods, the discrete Fourier transform (DFT) and harmonic least squares (HALS), to extract harmonic component properties. First, realistic conditions are simulated by analyzing a large number of synthetic data sets with variable sampling frequencies, record durations, sensor resolutions, noise levels and data gaps. Second, a model of two real-world data sets with different characteristics is validated. The results reveal that HALS outperforms the DFT in all aspects, including the ability to handle data gaps. While there is a clear trade-off between sampling frequency and record duration, sampling rates should not be less than six samples per day and records should not be shorter than 20 days when simultaneously extracting tidal constituents. The accuracy of detection is degraded by increasing noise levels and decreasing sensor resolution. However, a resolution of the same magnitude as the expected component amplitude is sufficient in the absence of excessive noise. The results provide a practical framework to determine the suitability of existing groundwater level records and can optimize future groundwater monitoring strategies to improve passive characterization using tidal signatures.

Landscape-scale habitat restoration has the potential to return ecosystem functions and services and mitigate the loss of native flora and fauna. However, restoration projects rarely monitor the effectiveness of restoration efforts, such as quantifying the establishment success (survival) of the planted species. We monitored a landscape-scale revegetation program in southeastern Australia that planted 5 million plants representing 35 native species over a 4-year period (2012-2015). We assessed the restoration effectiveness across years to evaluate how different lifeforms survived over time and the factors that influenced the differential survival of lifeforms and individual plant species 3 months (spring) and 9 months (after summer) post-planting. Establishment success varied across years with survival lowest in the 2015 planting season. Survival of different lifeforms after summer were associated with site-level variables (e.g. mean maximum temperature, rainfall, and soil type) with survival generally declining due to high temperatures, low rainfall, and for species planted on sandy or saline soils. Maximum temperature, rainfall, and soil type were the most important predictors of compositional change in the 20 species commonly planted across years, with two saltbush species (Atriplex paludosa) and one eucalypt species (Eucalyptus fasciculosa) having the highest survival, while one sedge species (Juncus kraussii) and two grass species (Poa poiformis and Puccinellia stricta) had among the lowest observed survival. These results highlight the importance of monitoring establishment success through survival to detect changes in the composition of lifeforms and species to guide future re-plantings aimed at returning the desired plant diversity.

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We consider the problem of minimizing a block separable convex function (possibly nondifferentiable, and including constraints) plus Laplacian regularization, a problem that arises in applications including model fitting, regularizing stratified models, and multi-period portfolio optimization. We develop a distributed majorizationminimization method for this general problem, and derive a complete, self-contained, general, and simple proof of convergence. Our method is able to scale to very large problems, and we illustrate our approach on two applications, demonstrating its scalability and accuracy.