In free space optical communication, a beacon light loses too much energy after a long-distance transmission and faces strong interference from background light. The beacon light illuminated on a quadrant detector (QD) is so weak that the output signal-to-noise ratio (SNR) of a QD is very low, which leads to a significant decrease in the accuracy of the direct position detection method. To solve this problem, an improved light spot position detecting method is proposed. Since the background light and the dark current noise are white noise, we could consider concentrating the energy of QD output signal at a certain frequency point to enhance the output SNR. Therefore, a cosine signal is used to modulate the intensity of a beacon light at the transmitting end. Then the QD output photocurrents are also cosine signals with the same frequency as the modulating signal. Putting the photocurrent signals into a cross-correlation operation with a reference signal, which is the same as the modulating signal, can enhance the QD output SNR at a certain frequency point. Unfortunately, the result of the classical cross-correlation is attenuated with increasing delay. It is hard to detect the amplitude of the classical cross-correlation result. So, we used cyclic cross-correlation to obtain a stable correlation result to detect its amplitude accurately. The experiment results show that even when the QD output SNR is less than −17 dB, the detection root-mean-square error of the proposed method is 0.0092 mm, which is a quarter of the direct position detection method. Moreover, this method only needs a small amount of data to accomplish the calculation and is especially suitable for real-time spot position detection.

In free space optical communications, long-distance transmission leads to the attenuation of beacon light, where we adopt a quadrant detector (QD) to receive the weak signal. However, the background light interferes so strongly that the output signal-to-noise ratio (SNR) of QD is at a low level, which causes a decrease in accuracy of the direct detection method. This requires finding a new light spot detection method, so an improved detection method is proposed. Because the dark current noise and the background light noise are both white noise, we adopt a Kalman filter to estimate the real output of four electric signals of QD. Unfortunately, running these through an amplifier introduces some direct current (DC) offsets into the signals. In order to balance the effect of the DC offsets, we consider using the modulation method, where we employ a sine signal to modulate the intensity of the beacon light at the transmitting end, after which we can give an inverse gain to move the center of signals to near zero to eliminate the DC offsets when we calculate the data. In Kalman filtering, we use the peak values of the signals in every period after the analog to digital converter (ADC) as the elements of the measurement matrix. Experimental results show that even when QD output SNR is about −10 dB, the detection root-mean-square errors decrease by 51.5% using the improved detection method compared with the direct detection method. Moreover, Kalman filtering does not require a large amount of data, which means it works efficiently, can reduce the cost of hardware resources, and is available for the real-time calculation of spot position.

Recently, non-line-of-sight (NLOS) detection based on time of flight (TOF) has been investigated. In order to simulate the NLOS location of a hidden object, we derive the signal scattered by the object and build a model of photon flight based on photon scattering and propagation. To improve the authenticity of the model, the bidirectional reflectance distribution function (BRDF) is used to characterize the scattering process. The Gauss filter is proposed to extract the TOF of the scattering sources of interest without a priori information or manual judgment of the useful scattered signal by filtering the disturbance out of the histogram. The hidden object can then be located by TOF processing. Compared with previous work using a fitting algorithm, the Gauss filtering approach preserves more waveform information and presents improved positioning accuracy and robustness under the influence of noise, which is demonstrated in both simulation and experiment. It is possible to locate a NLOS object automatically through filtering identification of the object signal. The simplicity, high efficiency, and automation of this algorithm make it applicable for tracking a hidden moving object.