Sensor Selection for Angle of Arrival Estimation Based on the Two-Target Cramér-Rao Bound

Sensor selection is a useful method to help reduce data throughput, as well as computational, power, and hardware requirements, while still maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown source models. In this work, we...

Robust Pareto-Optimal Radar Receive Filter Design for Noise and Sidelobe Suppression

Integrated sidelobe level is a useful measure to quantify robustness of a waveform-filter pair to unknown range clutter and multiple closely located targets. Sidelobe suppression on receive will incur a loss in the signal to noise ratio after pulse compression. We derive a pulse compression filter that has the greatest integrated sidelobe...

Array Design Based on the Worst-Case Cramér-Rao Bound to Account for Multiple Targets

Sensor selection is a useful method to help reduce computational, hardware, and power requirements while maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown target directions. We propose to tackle the sensor selection problem...

Single-Pulse Estimation of Target Velocity on Planar Arrays

Doppler velocity estimation in pulse-Doppler radar is done by evaluating the target returns of bursts of pulses. While this provides convenience and accuracy, it requires multiple pulses. In adaptive and cognitive radar systems, the ability to adapt on consecutive pulses, instead of bursts, brings potential performance benefits. Hence, with...

Privacy-Preserving Distributed Processing: Metrics, Bounds and Algorithms

Privacy-preserving distributed processing has recently attracted considerable attention. It aims to design solutions for conducting signal processing tasks over networks in a decentralized fashion without violating privacy. Many existing algorithms can be adopted to solve this problem such as differential privacy, secure multiparty...

On the duality of globally constrained separable problems and its application to distributed signal processing

In this paper, we focus on the challenge of processing data generated within decentralised wireless sensor networks in a distributed manner. When the desired operations can be expressed as globally constrained separable convex optimisation problems, we show how we can convert these to extended monotropic programs and exploit Lagrangian duality...

Overlapped Transform Coding of Images: Theory, Application and Realization