These events are then combined into a single output stream, known as Address Event Representation (AER), containing the pixel location, time, and event polarity [1]. These sensors offer high temporal resolution (up to 1 \(\upmu\)s), low latency (<1 ms), high dynamic range, and low power requirements with sparse data output [3]. EVSs have shown promise in applications such as hand gesture recognition [4], star and satellite tracking [5], celestial object detection near bright sources like the moon [6], visual odometry [7], traffic data collection [8], optical flow detection [9], high speed counting [10], vibration monitoring [11], visual light positioning systems [12], and high-speed particle detection and tracking in microfluidic devices [13]. Despite their many merits, these sensors can be overwhelmed by light sources that exhibit significant changes in illumination over extremely short time-frames. Individual pixel sensitivity is also reduced with increased scene brightness due to the EVS's detection of changes in log-compressed photocurrent; logarithmic thresholds correspond to larger linear thresholds at higher photocurrents. Therefore, overwhelming sources can reduce the ability to see targets of interest in a scene. Figure 2 shows approximated effects of adding signals on event generation using a rudimentary EVS model. A combined signal's events will have timing characteristics that differ from the individual signals' events. Significant work has been accomplished in the fields of dim target detection [14; 15; 16]; noise, background, and extraneous events reduction [17; 18; 19; 20; 21; 22]; and dynamic occlusion removal [23; 24]. However, the problem presented in this work is unique in that normal sensor noise and scene background is so obscured by the overwhelming source that other methods to remove noise or dynamic occlusions do not necessarily apply. Since target events are sparse in reference to the overwhelming source, event-domain detection techniques such as event accumulation frames and event cluster detection are blinded to the targets. Additionally, the method designed and described in this work operates in the frequency domain instead of the event domain since the overwhelming source (described in Section 2.1) has inherent stochastic frequency content due to atmospheric propagation  
Figure 1: Simplified event-based vision sensor (EVS) pixel operation showing how events are generated based on the logarithm of the input intensity, tunable thresholds, and refractory period. Adapted from [2]. which severely limits the use of traditional background suppression techniques. This method is also less affected by issues found in normal frequency analysis [25]. Instead of focusing on the actual frequencies in the scene, this method simply determines which pixels have significant frequency content after a background suppression. Many other frequency-based target detection methods that rely on spectral matching or classification which may misrepresent, or not detect at all, targets due to the overwhelming source's influence on targets' spectra. As a result phase differences between sources, aliasing, and spectral leakage are less relevant. While there are other frequency-based detectors that do not require prior knowledge of target or overwhelming source frequency profiles [26; 27], this method combines frequency-based principal component background suppression with an anomaly detection statistical filtering on data from a unique sensor (simulated) and problem set. The results include an event-domain and another frequency-based detector for performance comparisons. The previously proposed method targeting this unique problem of reducing the effects of an overwhelming source used a center surround concept [28]. The center surround method is based on a pixel hardware change where the average photocurrent of surrounding pixels is subtracted from the central pixel's photocurrent before the event generation phase, removing the effects of the overwhelming source before the sensor output.