Many applications, such as radio channel estimation, require solving for unknowns in overcomplete bases. Nonlinear solvers like Basis Pursuit Denoising (BPDN) can leverage sparse statistics to improve accuracy relative to linear solvers. The Locally Competitive Algorithm (LCA) is a nonlinear dynamical system that converges on the solution to BPDN, and can be implemented via a Spiking Neural Network that is orders of magnitude faster and more power efficient than CPU-based BPDN solutions. However, the Spiking LCA scales quadratically with the dimensionality of the state estimate, which can quickly make physical implementation impractical. In this work, we introduce a multi-layered complex-valued LCA architecture, which – by taking advantage of hierarchical sparsity in the channel estimation problem – allows sub-quadratic scaling of computational resource requirements, reducing resource requirements for a 357 complex channel estimation by 7.6x relative to a single layer solution. We implemented a 1470 complex channel, 2-layer Spiking LCA on Intel’s Loihi chip, and demonstrated a 10x reduction in temporal smear relative to a linear solver.

This work introduces the PNT Chain, a fully distributed filtering solution to collaborative positioning with ranging radios in GNSS-degraded and denied environments. The PNT (Positioning, Navigation, and Timing) Chain uses an Undirected Acyclic Bayes Graph (UABG) to factor a Kalman filter on the poses of an entire swarm into tractable cliques. The cliques are distributed across the physical nodes, which in combination with the use of equivalent propagation and equivalent measurement messages, keeps network traffic to an absolute minimum. The distributed PNT Chain algorithm is compared with an idealized (and impractical) centralized filter, and a legacy relative navigation algorithm in two scenarios where only two UAVS in a swarm are receiving GNSS signals. In both cases, the distributed PNT Chain achieves a positional accuracy comparable with the completely centralized filter, and an order of magnitude better than the legacy approach, while only requiring 22% the computation cost of the centralized filter.

With the advent of widespread digital technology, modern radar and communication systems have grown more complex and agile, rendering them difficult to adequately document and identify. The traditional solution of comparing incoming signals to a library of known waveforms is therefore becoming unworkable. The authors present two solutions to the problem of modern radio frequency (RF) waveform identification: a deep neural network and a recurrent neural network using GRUs. Both networks are designed to fuse together an arbitrary number of agile RF pulses and identify the emitter that produced them. Compared to a naïve DNN approach that simply averaged together pulses before classification, our solution adds a pre-projection step, which preserves information about sequential agility, even after averaging across pulses. After being trained against a set of 15 highly ambiguous emitters, the naïve DNN identified 52.2 % of test waveforms, our DNN with projection identified 72.3 %, and our RNN solution identified 84.8%.