Compressed Remote Sensing of Sparse Objects

Abstract

Abstract. The linear inverse source and scattering problems are studied from the perspective of compressed sensing, in particular the idea that sufficient incoherence and sparsity guarantee uniqueness of the solution. By introducing the sensor as well as target ensembles, the maximum number of recoverable targets (MNRT) is proved to be at least proportional to the number of measurement data modulo a log-square factor with overwhelming probability.Important contributions include the discoveries of the threshold aperture, consistent with the classical Rayleigh criterion, and the decoherence effect induced by random antenna locations.The prediction of theorems are confirmed by numerical simulations.

Compressive Sensing Theory for Optical Systems Described by a Continuous Model

“…This resembles the synthetic aperture imaging which has been previously analyzed under the paraxial approximation in Fannjiang et al 2010. In contrast, the forward scattering direction with θ l = θ l almost surely violates the constraint (84).…”

Compressed sensing applied to weather radar

“…For example, an aircraft surveillance radar display typically tracks a finite number of aircrafts that is very small compared to the totalk number of resolution cells. (Fannjiang et al 2010). This is because the corresponding spectrum shows only a finite number of Doppler frequencies, as shown in simulation of Figure 6(a).…”

Impact of External Phenomena In Compressed Sensing Methods For Wireless Sensor Networks

“…Now, let's suppose that each sensor node senses the same signal. General idea of the investigated method stands on the fact that each sensor node carries out multiplication of one line of the same measurement matrix with the measured samples [11]. The result is that a single node produces only a single coefficient that represents one element of the reduced vector b (see Fig.…”