Introduction  

With the rapid development of technology, multi-target tracking technology plays a crucial role in numerous fields. These include real-time tracking of surrounding pedestrians, vehicles, and obstacles in autonomous driving, continuous monitoring of multiple moving objects in intelligent surveillance systems, and control of the positions and trajectories of drones in formation flight. As a core component of multi-target tracking technology, radar modules provide solid support for achieving precise and efficient multi-target tracking by virtue of their unique advantages.  

Analysis of Radar Module Working Principles  

Radar essentially operates by transmitting electromagnetic waves and receiving echo signals reflected by targets. In multi-target tracking applications, the common radar modules mainly include millimeter-wave radar and lidar (light detection and ranging).  

Millimeter-Wave Radar  

Millimeter-wave radar operates in the millimeter-wave frequency band, generally referring to electromagnetic waves with frequencies between 30GHz and 300GHz. Its working principle is based on Frequency Modulated Continuous Wave (FMCW) technology, which emits continuous wave signals whose frequency changes linearly with time. When the signal encounters a target, the reflected echo generates a frequency difference with the transmitted signal, known as the beat frequency. By accurately measuring and analyzing the beat frequency, distance information of the target can be obtained. Meanwhile, using multi-antenna array technology, such as a 2-transmit and 4-receive microstrip antenna array, the angle information of the target can be calculated by the phase difference of signals received by different antennas. Additionally, according to the Doppler effect, the change in the frequency of the echo signal relative to the transmitted signal can yield the radial velocity information of the target. For example, in the scenario of autonomous driving, millimeter-wave radar can real-time detect the distance, angle, and velocity of multiple surrounding vehicles, providing key data for the vehicle's decision-making system to facilitate safe and efficient driving.  

Lidar  

Lidar detects targets by using laser beams. It determines the distance to the target by emitting laser pulses and measuring the time interval from transmission to reception. Meanwhile, through scanning methods such as rotation or phased array, lidar can obtain two-dimensional or three-dimensional coordinate information of targets in space. When tracking multiple targets, lidar can quickly generate a point cloud map of the target's surrounding environment, where each point represents a reflection point on the target's surface, containing rich information such as distance and angle. For instance, in the field of intelligent security monitoring, lidar can accurately track the positions and movement trajectories of multiple intruders, issue alarms in a timely manner, and ensure regional security.  

Key Technologies in Multi-Target Tracking  

Target Detection Technology  

High-Resolution Imaging Technology: Radar modules use advanced signal processing algorithms to achieve high-resolution imaging, enabling clear presentation of target details. Take millimeter-wave radar as an example, increasing the bandwidth can effectively improve range resolution, and adopting a denser antenna array can enhance angle resolution. In this way, multiple close-range targets can be accurately distinguished to prevent target confusion.  

Multi-Channel Signal Processing Technology: Using multi-channel technology, radar modules can simultaneously process data from multiple signal channels, which greatly enhances the system's ability to detect multiple targets. This allows radar to detect multiple targets at different positions and with different motion states simultaneously in complex environments. For example, in urban traffic environments, multi-channel millimeter-wave radar can detect various targets such as cars, pedestrians, and bicycles on the前方 (front) road simultaneously.  

Machine Learning-Based Target Detection Algorithms: With the rapid development of machine learning technology, it has been widely applied in radar target detection. By training a large amount of radar data containing different targets, target detection models are constructed. These models can automatically learn the features of targets to achieve fast and accurate target detection. For instance, deep learning-based Convolutional Neural Network (CNN) models can effectively identify different types of targets when processing radar echo data, significantly improving the accuracy and efficiency of detection.  

Data Association Technology  

Nearest Neighbor Algorithm: This is a basic and intuitive data association algorithm, which associates the currently detected target with the nearest target in the set of tracked targets. The distance metric can be Euclidean distance, Mahalanobis distance, etc. In simple scenarios with few targets and large distances between them, the nearest neighbor algorithm can achieve data association quickly and effectively. However, in complex scenarios with multiple targets close to each other, this algorithm may lead to association errors.  

Joint Probabilistic Data Association (JPDA) Algorithm: The JPDA algorithm fully considers all possible association combinations between multiple detected targets and multiple tracked targets. It determines the optimal association scheme by calculating the probability of each association combination. In complex environments with dense multiple targets, the JPDA algorithm can significantly improve the accuracy of data association. However, due to the need to calculate a large number of association combinations, its computational complexity is high, imposing strict requirements on computing resources.  

Multiple Hypothesis Tracking (MHT) Algorithm: The MHT algorithm generates multiple possible association hypotheses for each detected target and continuously tracks and evaluates these hypotheses. As new data arrives, unreasonable hypotheses are gradually deleted, and highly probable hypotheses are retained. This algorithm performs excellently in handling complex situations such as target crossing and occlusion, effectively improving the stability and accuracy of multi-target tracking. Nevertheless, it also faces the problem of large computational load.  

Target Tracking Algorithms  

Kalman Filter Algorithm: Kalman filter is a classic linear minimum mean square error estimation method. In radar multi-target tracking, it recursively estimates the target state based on the target's motion model and the radar's observation model. Through the alternation of the prediction phase and the update phase, the estimated value of the target state is continuously corrected to achieve real-time tracking of the target. The Kalman filter algorithm has high computational efficiency and can achieve good tracking results when the target motion is relatively stable. For example, on highways, where the motion state of vehicles is relatively stable, the Kalman filter algorithm can accurately track the position and velocity of vehicles.  

Extended Kalman Filter (EKF) Algorithm: When the target motion model or observation model is nonlinear, the EKF algorithm approximates the nonlinear function by first-order Taylor expansion to linearize it, and then applies the Kalman filter algorithm for processing. In actual multi-target tracking scenarios, the motion of many targets is not strictly linear, such as the flight trajectory of drones when performing complex tasks. The EKF algorithm can effectively handle such nonlinear situations and improve tracking accuracy.  

Unscented Kalman Filter (UKF) Algorithm: The UKF algorithm adopts a deterministic sampling strategy and approximates the state distribution by selecting a set of Sigma points. Compared with the EKF algorithm, the UKF algorithm has higher accuracy in processing nonlinear problems and can more accurately estimate the target state. In some scenarios with extremely high requirements for tracking accuracy, such as military target tracking, the UKF algorithm can play an important role in providing more reliable data support for decision-making.