Exploring LoRa for Sensing

Wireless sensing received a great amount of attention in recent years and various wireless technologies have been exploited for sensing, including WiFi [1], RFID [2], ultrasound [3], 60 GHz mmWave [4] and visible light [5]. The key advantage of wireless sensing over traditional sensing is that the target does not need to be equipped with any sensor(s) and the wireless signal itself is being used for sensing. Exciting new applications have been enabled, such as passive localization [6] and contactless human activity sensing [7]. While promising in many aspects, one key limitation of current wireless sensing techniques is the very small sensing range. This is because while both direct path and reflection path signals are used for communication, only the weak target-reflection signals can be used for sensing. Take Wi-Fi as an example: the communication range can reach 20 to 50 meters indoors but its sensing range is merely 4 to 8 meters. This small range further limits the through-wall sensing capability of Wi-Fi. On the other hand, many applications do require long-range and through-wall sensing capability. In a fire rescue scenario, the sensing device cannot be placed close to the building, and the long-range through-wall sensing capabilities are critical for detecting people deep inside the building. Table I summarizes the sensing range of existing wireless technologies. We can see that long-range through-wall sensing is still missing with wireless sensing.

A Novel Joint TDOA/FDOA Passive Localization Scheme Using Interval Intersection Algorithm

Due to the large measurement error in the practical non-cooperative scene, the passive localization algorithms based on traditional numerical calculation using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) often have no solution, i.e., the estimated result cannot meet the localization background knowledge. In this context, this paper intends to introduce interval analysis theory into joint FDOA/TDOA-based localization algorithm. The proposed algorithm uses the dichotomy algorithm to fuse the interval measurement of TDOA and FDOA for estimating the velocity and position of a moving target. The estimation results are given in the form of an interval. The estimated interval must contain the true values of the position and velocity of the radiation target, and the size of the interval reflects the confidence of the estimation. The point estimation of the position and the velocity of the target is given by the midpoint of the estimation interval. Simulation analysis shows the efficacy of the algorithm.

Passive Localization of Moving Target with Channel State Information

With the popularity of wireless networks and smart devices, wireless signal-based passive target sensing and localization have become a hot research topic and attracted numerous researchers’ interests. The existing passive localization solutions require multiple receivers, which is not practical for real-world applications. In response to this compelling problem, in this paper, we propose a practical single access point-based passive moving target localization system. Concretely, it first utilizes multiple antennas of the access point to form an antenna array and extended antenna, to capture channel state information (CSI) at different spatial locations. Then, leveraging the obtained CSI, the signal parameters, including the angle of arrival (AoA) and time of flight (ToF), are estimated. Based on the estimated signal parameters and the locations of the antenna array and extended antenna, finally, the passive localization of the moving target is realized. Comprehensive experiments are conducted under the real-world scenario with two different test platforms, and the experimental results show the proposed algorithm’s median localization can reach 1.087 m when the number of antennas is 4 and the signal bandwidth is 80 MHz, demonstrating the effectiveness of the proposed algorithm.

The Direction of Arrival Location Deception Model Counter Duel Baseline Phase Interferometer Based on Frequency Diverse Array

With the emergence and development of the passive localization, the radiation source is more visible for the location system which endangers their survival. Therefore, there is an urgent demand for the radio frequency (RF) stealth technology. An effective method to realize RF stealth is location deception, therefore, for the passive localization system, this paper proposes a direction of arrival (DOA) location deception method using the frequency diverse array (FDA) against the dual baseline phase interferometer. Since the direction-finding of the dual baseline phase interferometer is based on the received signal with fixed frequency, the FDA signal has a deception effect on the interferometer owing to the introduction of the small frequency increment. Considering the influence of the frequency increment sequence on the deception effect, we derive the optimizations of the DOA location deception via the average location deviation for the sampling time in the case of no noise and noise, respectively. Besides, considering the time dependency of the beam, we investigate the average SNR (ASNR) and the corresponding CRLB to verify the proposed method. Numerical examples and simulations show that the proposed method can counter the interferometer by realizing location deception.

A Frequency Domain Direct Localization Method Based on Distributed Antenna Sensing

Traditional two-step passive localization methods need to extract the parameters like the direction of arrival (DOA), time of arrival (TOA), and time difference of arrival (TDOA) from the original data to determine the source position, which causes the poor positioning accuracy due to error accumulation. In this paper, a direct position determination (DPD) method is proposed to improve the positioning accuracy and robustness, which is based on a correlation algorithm. Firstly, the cost function directly related to the location of the source can be established by synthesizing the data received by multiantenna in the frequency domain. Then, the position of the source is estimated by the correlation DPD method to search the monitoring area. Compared to the improved TDOA algorithm and Least Squares DPD algorithm, the proposed method shows better localization accuracy of different SNRs. Finally, based on real measured data, it can be seen that the results of the proposed algorithm are better than the improved TDOA algorithm.